diff --git "a/data/dataset_Metamaterial.csv" "b/data/dataset_Metamaterial.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_Metamaterial.csv"
@@ -0,0 +1,33523 @@
+"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Euler/EulerFR.m",".m","1526","85","function [u,v] = EulerFR( freqs )
+
+tic
+
+flclear fem
+
+b = 1.e-3;
+L = 4.e-2;
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 1.e-2;
+
+F = -1000 * th * blk;
+M0 = blk*blk*th*rho;
+I = b^3*th/12;
+A = b*th;
+
+% Geometry
+gBeam=curve2([0,0],[0,L]);
+
+% Analyzed geometry
+clear c
+c.objs={gBeam};
+c.name={'Beam'};
+c.tags={'gBeam'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear pnt
+pnt.Fx = {0,F};
+pnt.Fy = {0,F};
+pnt.constrcond = {'free','norot'};
+pnt.m = {0,M0};
+pnt.ind = [2,1];
+appl.pnt = pnt;
+
+clear bnd
+bnd.heightz = th;
+bnd.Iyy = I;
+bnd.A = A;
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1];
+appl.bnd = bnd;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, 'solcomp',{'u','th','v'}, 'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', 'plist',freqs, ...
+                  'oldcomp',{}, 'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+solnums = 1:length(freqs);
+u = postint(fem,'u', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+v = postint(fem,'v', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Euler/EulerMass.m",".m","1600","89","function [u,v] = EulerFR( freqs )
+
+tic
+
+flclear fem
+
+b = 1.e-3;
+L = 4.e-2;
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 1.e-2;
+l = 4.e-3;
+d = 4.e-3;
+
+F = -1000 * th * blk;
+M0 = blk*blk*th*rho;
+M1 = l*d*th*100*rho;
+I = b^3*th/12;
+A = b*th;
+
+% Geometry
+gBeam=curve2([0,0],[0,L]);
+
+% Analyzed geometry
+clear c
+c.objs={gBeam};
+c.name={'Beam'};
+c.tags={'gBeam'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear pnt
+pnt.Fx = {0,F};
+pnt.Fy = {0,F};
+pnt.constrcond = {'free','norot'};
+pnt.m = {M1,M0};
+pnt.Jz = {M1*(d^2+l^2)/12,0};
+pnt.ind = [2,1];
+appl.pnt = pnt;
+
+clear bnd
+bnd.heightz = th;
+bnd.Iyy = I;
+bnd.A = A;
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1];
+appl.bnd = bnd;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, 'solcomp',{'u','th','v'}, 'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', 'plist',freqs, ...
+                  'oldcomp',{}, 'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+solnums = 1:length(freqs);
+u = postint(fem,'u', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+v = postint(fem,'v', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/GetU.m",".m","757","38","function [u0,u1,M] = GetU( f )
+
+Fin = -1000;
+b = 5.e-4;
+L = 4.e-2;
+h = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+blk = 5.e-3;
+omega = 2 * pi * f;
+
+
+M0 = rho * blk^2 * h;
+F = Fin * blk * h;
+
+lambda = ( 48*pi^2 * rho / E / b^2 .* f.^2 ) .^ (1/4);
+a = lambda*L;
+
+s = sin(a);
+sh = sinh(a);
+c = cos(a);
+ch = cosh(a);
+
+M = M0 + rho*b*h ./ lambda .* (s.*ch+c.*sh)./(c.*ch+1);
+u0 = F ./ M ./ omega.^2;
+u1 = u0 / 2. / (c.*ch+1) .* ( (s.*ch+c.*sh).*s + (c.*ch+1-s.*sh).*c - (s.*ch+c.*sh).*sh + (c.*ch+1+s.*sh).*ch );
+
+F1 = c.*ch+1-s.*sh;
+F2 = c.*ch+1+s.*sh;
+F3 = s.*ch+c.*sh;
+
+uyL   = lambda .* ( -F1.*s + F2.*sh + F3.*(c-ch) );
+uyyL  = lambda.^2 .* ( -F1.*c + F2.*ch + F3.*(-s-sh) );
+uyyyL = lambda.^3 .* ( F1.*s + F2.*sh + F3.*(-c-ch) );
+
+yc = s.*sh ./ lambda ./ F3;
+u1 = yc;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/GetZeros.m",".m","175","14","function [x0,idx] = GetZeros(x,y)
+
+x0 = 0;
+idx = 0;
+n = length(y);
+nz = 1;
+for i=1:(n-1)
+  if( y(i+1)*y(i) <= 0 )
+    x0(nz) = x(i);
+    idx(nz) = i;
+    nz = nz+1;
+  end
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/GetResFreq.m",".m","103","4","function [f] = GetResFreq( alpha, L, E, b, rho )
+
+f = sqrt( (alpha/L)^4 * E * b^2 / 48 / pi^2 / rho );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/BeamMassV.m",".m","1171","55","function [y,v] = BeamMassV( f )
+%  m = Mass on beam
+%  J = Moment of inertia of m
+%  d = distance from tip to cog of m
+
+omega = 2 * pi * f;
+Fin = 1000;
+
+%  Material Properties
+rho = 7850;
+rho2 = 100*7850;
+E = 2.0e11;
+
+%  Dimensions
+b = 1.e-3;
+L = 4.e-2;
+h = 1.e-3;
+A = b*h;
+I = b^3*h/12;
+blk = 1.e-2;
+y = 0:(L/100):L;
+
+d = 5.e-4;
+l = 2.e-3;
+d0 = 0*d/2;
+m = rho*d*l*h;
+J = m*(d^2+l^2)/12;
+
+M0 = rho * blk^2 * h;
+F = Fin * blk * h;
+lambda = ( rho * A / E / I * 4 * pi^2 .* f.^2 ) .^ (1/4);
+
+%  Constants
+a = lambda*L;
+b = m/rho/A/L;
+k = J./m;
+if( m == 0 )
+    k = 0;
+end
+R1 = b .* a.^3 .* ( (k+d0.^2) / L^2 );
+R2 = b .* a.^2 .* ( d0 / L );
+
+s = sin(a);
+sh = sinh(a);
+c = cos(a);
+ch = cosh(a);
+
+F1 = 1 + c.*ch - s.*sh - 2*b.*a.*s.*ch - 2*R1.*c.*sh + (R1.*b.*a-R2.^2).*(1-c.*ch+s.*sh);
+F2 = 1 + c.*ch + s.*sh + 2*b.*a.*c.*sh - 2*R1.*s.*ch + (R1.*b.*a-R2.^2).*(1-c.*ch-s.*sh);
+F3 = (s.*ch+c.*sh).*(1-R1.*b.*a+R2.^2) + 2*( c.*ch.*b.*a - R1.*s.*sh - R2.*(s.*ch-c.*sh) );
+D = 1 + c.*ch - b.*a.*(s.*ch-c.*sh) - R1.*( s.*ch + c.*sh - b.*a.*(1-c.*ch) ) - 2*R2.*s.*sh - R2.^2.*(1-c.*ch);
+
+v = 1./(2*D) * ( F1*cos(lambda*y) + F2*cosh(lambda*y) + F3*(sin(lambda*y)-sinh(lambda*y)) );
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/GetUArray.m",".m","434","24","function [u0,M] = GetUArray( f, L, b )
+
+Fin = -1000;
+h = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+blk = 1.e-2;
+omega = 2 * pi * f;
+F = Fin * blk * h * length(b);
+
+M = rho * blk^2 * h * length(b);
+for i = 1:length(L)
+  lambda = ( 48*pi^2 * rho / E / b(i)^2 .* f.^2 ) .^ (1/4);
+  a = lambda*L(i);
+  s = sin(a);
+  sh = sinh(a);
+  c = cos(a);
+  ch = cosh(a);
+M = M + rho*b(i)*h ./ lambda .* (s.*ch+c.*sh)./(c.*ch+1);
+end
+
+u0 = F ./ M ./ omega.^2;
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Theory/BeamMass.m",".m","1507","65","function [u0,idx,M] = BeamMass( f )
+%  m = Mass on beam
+%  J = Moment of inertia of m
+%  d = distance from tip to cog of m
+
+omega = 2 * pi * f;
+Fin = 1000;
+
+%  Material Properties
+rho = 7850;
+rho2 = 7850;
+E = 2.0e11;
+
+%  Dimensions
+b = 5.e-4;
+L = 4.e-2;
+h = 1.e-3;
+A = b*h;
+I = b^3*h/12;
+blk = 6.e-3;
+
+d = 1.e-3;
+l = 5.e-3;
+d0 = d/2;
+m = rho*d*l*h;
+J = m*(d^2+l^2)/12;
+
+M0 = rho * blk^2 * h;
+F = Fin * blk * h;
+lambda = ( rho * A / E / I * 4 * pi^2 .* f.^2 ) .^ (1/4);
+
+%  Constants
+a = lambda*L;
+b = m/rho/A/L;
+k = J./m;
+if( m == 0 )
+    k = 0;
+end
+
+R1 = b .* a.^3 .* ( (k+d0.^2) / L^2 );
+R2 = b .* a.^2 .* ( d0 / L );
+s = sin(a);
+sh = sinh(a);
+c = cos(a);
+ch = cosh(a);
+
+F1 = 1 + c.*ch - s.*sh - 2*b.*a.*s.*ch - 2*R1.*c.*sh + (R1.*b.*a-R2.^2).*(1-c.*ch+s.*sh);
+F2 = 1 + c.*ch + s.*sh + 2*b.*a.*c.*sh - 2*R1.*s.*ch + (R1.*b.*a-R2.^2).*(1-c.*ch-s.*sh);
+F3 = (s.*ch+c.*sh).*(1-R1.*b.*a+R2.^2) + 2*( c.*ch.*b.*a - R1.*s.*sh - R2.*(s.*ch-c.*sh) );
+D = 1 + c.*ch - b.*a.*(s.*ch-c.*sh) - R1.*( s.*ch + c.*sh - b.*a.*(1-c.*ch) ) - R2.^2.*(1-c.*ch);
+
+uL = 1./(2*D) .* ( F1.*c + F2.*ch + F3.*(s-sh) );
+uyL = 1./(2*D) .* lambda .* ( -F1.*s + F2.*sh + F3.*(c-ch) );
+uyyL = 1./(2*D) .* lambda.^2 .* ( -F1.*c + F2.*ch - F3.*(s+sh) );
+uyyyL = 1./(2*D) .* lambda.^3 .* ( F1.*s + F2.*sh - F3.*(c+ch) );
+mb = rho*A./(2*D.*lambda) .* ( F1.*s + F2.*sh - F3.*(c+ch-2) );
+
+M = M0 + mb + m*uL + m*d0*uyL;
+
+u0 = F ./ M ./ omega.^2;
+[x0,idx] = GetZeros(a,D);
+bc2 = uyyL*E*I - (J+m*d0^2)*omega.^2.*uyL - m*d0*omega.^2.*uL;
+max(a)
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/Beam2SMPS.m",".m","1937","86","function [u] = Beam2SMPS( freq, hAcross )
+%  Add Some Damping beta = 0.001
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 466;
+
+b = 5.e-4;
+L = 4.e-2;
+b2 = 5.e-4;
+L2 = 3.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 2.e-2;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base','center', 'pos',[0,L+d/2], 'rot','0');
+gBeam2 = rect2( b2, L2, 'base','center',  'pos',[0,L+d+L2/2], 'rot','0');
+SubBeam = [1,1,1,1];
+
+clear s
+s.objs={gBase,gBeam,gMass,gBeam2};
+s.name={'Base','Beam','Mass','Beam2'};
+s.tags={'gBase','gBeam','gMass','Beam2'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hs1 = b/hAcross;
+hs2 = 10*hs1;
+fem.mesh=meshinit( fem, 'hauto',5, ...
+                  'hmaxsub',[ 1,5*hs2, 2,hs2, 3,hs1, 4,hs1 ] );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.betadK = 5.e-5;
+equ.alphadM = 0;
+equ.thickness = th;
+equ.ind = [1,1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/Beam1SMPS.m",".m","1779","83","function [u] = Beam1SMPS( freq, hAcross, sType )
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 1900;
+%sType = 2;
+
+b = 5.e-4;
+L = 4.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 3.4e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 2.e-2;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base', 'center', 'pos',[0,L+d/2], 'rot','0');
+SubBeam = [1,1,2];
+
+clear s
+s.objs={gBase,gBeam,gMass};
+s.name={'Base','Beam','Mass'};
+s.tags={'gBase','gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hs1 = b/hAcross;
+hs2 = 10*hs1;
+fem.mesh=meshinit( fem, 'hauto',5, ...
+                  'hmaxsub',[ 1,5*hs2, 2,hs2, 3,hs1 ] );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.loadtype = {'length','area','length'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/RunBlockSMPS.m",".m","421","20","function [u] = RunBlockSMPS( freqs, hAcross )
+
+tic
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BlockSMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/BlockSMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/GetR.m",".m","130","8","function R = GetR( F, f, u )
+
+rho = 1.2;
+cs = 344;
+
+z = F ./ ( 2*pi*j*f.*u ); 
+R = 20 * log10( abs( 1 + 1 / 2 * z / rho / cs ) );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/RunBeamSMPS.m",".m","418","20","function [u] = RunBeamSMPS( freqs, hAcross )
+
+tic
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BeamSMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/BeamSMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/RunBeam2SMPS.m",".m","421","20","function [u] = RunBeam2SMPS( freqs, hAcross )
+
+tic
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = Beam2SMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/Beam2SMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/RunBeam1SMPS.m",".m","421","20","function [u] = RunBeam1SMPS( freqs, hAcross )
+
+tic
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = Beam1SMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/Beam1SMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/BlockSMPS.m",".m","1364","71","function [u] = BlockSMPS( freq, hAcross )
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 1900;
+
+
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 2.1e-2;
+omega = 2*pi*freq;
+F = -1000;
+
+gMass = rect2( blk,blk, 'base','center', 'pos',[0,0], 'rot','0');
+
+clear s
+s.objs={gMass};
+s.name={'Mass'};
+s.tags={'gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+
+hs1 = 2*blk/hAcross;
+fem.mesh=meshinit( fem, 'hauto',5, 'hmax',hs1 );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;0];
+  u = postinterp(fem, 'u', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/RunAll.m",".m","199","9","function [f,u2,u1,u0] = RunAll( hAcross )
+
+f = [1:700];
+d = load('Data/BeamSMPS.dat');
+u2 = d(:,3);
+%u2 = RunBeamSMPS( f, hAcross );
+u1 = RunBeam1SMPS( f, hAcross );
+u0 = RunBlockSMPS( f, hAcross );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/BigMass/BeamSMPS.m",".m","2724","123","%function [u] = BeamSMPS( freq, hAcross )
+
+flclear fem;
+bSolve = 0;
+hAcross = 8;
+freq = 466;
+
+b = 5.e-4;
+L = 4.e-2;
+b2 = 5.e-4;
+L2 = 3.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 2.e-2;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base','center', 'pos',[0,L+d/2], 'rot','0');
+gBeam2 = rect2( b2, L2, 'base','center',  'pos',[0,L+d+L2/2], 'rot','0');
+SubBeam = [1,1,1,1];
+
+clear s
+s.objs={gBase,gBeam,gMass,gBeam2};
+s.name={'Base','Beam','Mass','Beam2'};
+s.tags={'gBase','gBeam','gMass','Beam2'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hs1 = b/hAcross;
+hs2 = 10*hs1;
+fem.mesh=meshinit( fem, 'hauto',5, ...
+                  'hmaxsub',[ 1,5*hs2, 2,hs2, 3,hs1, 4,hs1 ] );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1,1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+
+% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,-1000.0,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.betadK = 0.002;
+equ.alphadM = 0;
+equ.thickness = 0.0010;
+equ.ind = [1,1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/Beam1SMPS.m",".m","1976","87","function [u] = Beam1SMPS( freq, hAcross, sType )
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 1900;
+%sType = 2;
+
+b = 5.e-4;
+L = 4.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 3.4e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 6.e-3;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base', 'center', 'pos',[0,L+d/2], 'rot','0');
+SubBeam = [1,1,2];
+
+clear s
+s.objs={gBase,gBeam,gMass};
+s.name={'Base','Beam','Mass'};
+s.tags={'gBase','gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hscl = 1/hAcross;
+hblk = 0:(b/blk*hscl):1;
+hmx = 0:(b/l*hscl):1;
+hmy = 0:(b/d*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(b/L*hscl):1;
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[14],[11, 8, 3],[1]},{[5, 9, 12],[13],[6],[4]},{[8],[10],[9],[7]}}, ...
+                 'edgelem',{ 1,hblk, 2,hblk, 4,hmy, 6,hmx, 7,hby, 8,hbx}, 'hauto',5);
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.loadtype = {'length','area','length'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/RunBlockSMPS.m",".m","443","21","function [freqs,u] = RunBlockSMPS( hAcross )
+
+tic
+freqs = [1:0.25:1000];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BlockSMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/BlockSMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/GetR.m",".m","130","8","function R = GetR( F, f, u )
+
+rho = 1.2;
+cs = 344;
+
+z = F ./ ( 2*pi*j*f.*u ); 
+R = 20 * log10( abs( 1 + 1 / 2 * z / rho / cs ) );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/RunBeamSMPS.m",".m","440","21","function [freqs,u] = RunBeamSMPS( hAcross )
+
+tic
+freqs = [1:0.25:1000];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BeamSMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/BeamSMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/RunBeam1SMPS.m",".m","443","21","function [freqs,u] = RunBeam1SMPS( hAcross )
+
+tic
+freqs = [1:0.25:1000];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = Beam1SMPS( freqs(i), hAcross );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/Beam1SMPS.dat';
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/BlockSMPS.m",".m","1362","71","function [u] = BlockSMPS( freq, hAcross )
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 1900;
+
+
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 8.7e-3;
+omega = 2*pi*freq;
+F = -1000;
+
+gMass = rect2( blk,blk, 'base','center', 'pos',[0,0], 'rot','0');
+
+clear s
+s.objs={gMass};
+s.name={'Mass'};
+s.tags={'gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+
+hs1 = blk/hAcross;
+fem.mesh=meshinit( fem, 'hauto',5, 'hmax',hs1 );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;0];
+  u = postinterp(fem, 'u', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/RunAll.m",".m","144","6","function [f,u2,u1,u0] = RunAll( hAcross )
+
+[f,u2] = RunBeamSMPS( hAcross );
+[f,u1] = RunBeam1SMPS( hAcross );
+[f,u0] = RunBlockSMPS( hAcross );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/BeamStack/SmallMass/BeamSMPS.m",".m","1896","85","function [u] = BeamSMPS( freq, hAcross )
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 466;
+
+b = 5.e-4;
+L = 4.e-2;
+b2 = 5.e-4;
+L2 = 3.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 6.e-3;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base','center', 'pos',[0,L+d/2], 'rot','0');
+gBeam2 = rect2( b2, L2, 'base','center',  'pos',[0,L+d+L2/2], 'rot','0');
+SubBeam = [1,1,1,1];
+
+clear s
+s.objs={gBase,gBeam,gMass,gBeam2};
+s.name={'Base','Beam','Mass','Beam2'};
+s.tags={'gBase','gBeam','gMass','Beam2'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hs1 = b/hAcross;
+hs2 = 10*hs1;
+fem.mesh=meshinit( fem, 'hauto',5, ...
+                  'hmaxsub',[ 1,hs2, 2,hs2, 3,hs1, 4,hs1 ] );
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.loadtype = {'length','area','length'};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1,1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/BeamTest1.m",".m","15110","554","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=rect2('1.e-3','2.e-2','base','corner','pos',{'0','0'},'rot','0');
+carr={curve2([-0.0040,-0.0040],[0.02,0],[1,1])};
+g2=geomcoerce('curve',carr);
+
+% Analyzed geometry
+clear c s
+c.objs={g2};
+c.name={'B1'};
+c.tags={'g2'};
+
+s.objs={g1};
+s.name={'R1'};
+s.tags={'g1'};
+
+fem.draw=struct('c',c,'s',s);
+fem.geom=geomcsg(fem);
+g1=move(g1,[0.0010,0]);
+gg=geomedit(g2);
+gg{1}=beziercurve2([0,0],[0.02,0],[1,1]);
+g3=geomedit(g2,gg);
+
+% Analyzed geometry
+clear c
+c.objs={g3};
+c.name={'B1'};
+c.tags={'g3'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+g4=rect2('1.e-2','1.e-2','base','corner','pos',{'-5.e-3','-1.e-2'},'rot','0');
+
+% Analyzed geometry
+clear c s
+c.objs={g3};
+c.name={'B1'};
+c.tags={'g3'};
+
+s.objs={g4};
+s.name={'R1'};
+s.tags={'g4'};
+
+fem.draw=struct('c',c,'s',s);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, ...
+                  'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {1.e-3,0.1};
+bnd.ind = [2,2,2,1,2,2];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000};
+bnd.ind = [2,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.009816671776424861,0.010816671823922312,-0.017949675520645976,0.0047805525919316,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01184374973527156,0.01184374973527156,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Refine mesh
+fem.mesh=meshrefine(fem, ...
+                    'mcase',0, ...
+                    'rmethod','regular');
+
+% Refine mesh
+fem.mesh=meshrefine(fem, ...
+                    'mcase',0, ...
+                    'rmethod','regular');
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.usage = {0,1};
+bnd.dampingtype = {'nodamping','Rayleigh'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000};
+bnd.ind = [2,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.014856088228826154,0.014856088228826154,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.usage = {0,1};
+bnd.dampingtype = {'nodamping','Rayleigh'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000};
+bnd.ind = [2,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01184374973527156,0.01184374973527156,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'disp_smpn','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: Total displacement [m]   Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01184374973527156,0.01184374973527156,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'disp_smpn','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: Total displacement [m]   Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.009078313050095935,0.009078313050095935,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.009078313050095935,0.009078313050095935,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: x-displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.008978915461956196,0.008978915461956196,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'lindata',{'v','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: y-displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.00901204799133611,0.00901204799133611,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'v','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: y-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.006493975758462785,0.006162650464663664,-0.016261095490540438,0.006911698109154311,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,1.7e-12};
+bnd.A = {0.01,2.e-5};
+bnd.usage = {0,1};
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000};
+bnd.ind = [2,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'v','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: y-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.011686367615139009,0.011355042321339888,-0.012577363649012036,0.010227966267625888,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,1.7e-12};
+bnd.A = {0.01,2.e-5};
+bnd.usage = {0,1};
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'v','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: y-displacement [m]   Boundary: y-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.012379152252076735,0.012047826958277615,-0.01701305008270246,0.007163652701316294,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.00707954892442045,0.006748223630621329,-0.012969257565859456,0.013119860184473276,-1,1]);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/BeamTest4.m",".m","1857","82","function [u] = BeamTest4( freq )
+
+flclear fem
+
+% Geometry
+g1=rect2('1.e-3','2.e-2','base','center','pos',{'0','1.e-2'},'rot','0');
+g2=rect2('1.e-2','1.e-2','base','center','pos',{'0','-5.e-3'},'rot','0');
+g3=rect2('0.01','5.e-3','base','center','pos',{'0','-0.0025'},'rot','0');
+g4=rect2('0.01','0.010','base','center','pos',{'0','-0.005'},'rot','0');
+
+% Analyzed geometry
+clear s
+s.objs={g1,g4};
+s.name={'R1','R2'};
+s.tags={'g1','g4'};
+
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+
+
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{1,[0:.0125:1],2,[0:.0125:1],4,[0:.00625:1],5,[0:.125:1]}, 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+bnd.constrcond = {'free','displacement','displacement'};
+bnd.Rx = {0,0,0.01};
+bnd.Hy = {0,1,0};
+bnd.Hx = {0,0,1};
+bnd.ind = [3,2,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.E = {2.0e15,2.0e11};
+equ.ind = [1,2];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+if( 3 > 2 )
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[freq], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+p1 = [0;0];
+p2 = [0;2.e-2];
+u(1) = postinterp(fem, 'u', p1);
+u(2) = postinterp(fem, 'u', p2);
+
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/RunBeam4.m",".m","111","10","function [freqs,u] = RunBeam4( freqs )
+
+tic
+
+for i = 1:length(freqs)
+  u(i,:) = BeamTest4(freqs(i) );
+end
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/EulerBeamTest.m",".m","44825","1677","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=curve2([0,0],[0,0.04]);
+
+% Analyzed geometry
+clear c
+c.objs={g1};
+c.name={'B1'};
+c.tags={'g1'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+g2=square2('1.e-2','base','center','pos',{'0','5.e-3'},'rot','0');
+g3=square2('0.01','base','center','pos',{'0','-0.0050'},'rot','0');
+
+% Analyzed geometry
+clear c s
+c.objs={g1};
+c.name={'B1'};
+c.tags={'g1'};
+
+s.objs={g3};
+s.name={'SQ1'};
+s.tags={'g3'};
+
+fem.draw=struct('c',c,'s',s);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, ...
+                  'hauto',5);
+
+% Refine mesh
+fem.mesh=meshrefine(fem, ...
+                    'mcase',0, ...
+                    'rmethod','regular');
+
+% Refine mesh
+fem.mesh=meshrefine(fem, ...
+                    'mcase',0, ...
+                    'rmethod','regular');
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,'1.e-3^3*1.e-3/12'};
+bnd.A = {0.01,'1.e-3*1.e-3'};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smeulip(3)=110    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02476014612565621,0.02476014612565621,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,'1.e-3^3*1.e-3/12'};
+bnd.A = {0.01,'1.e-3*1.e-3'};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'title','freq_smeulip(1)=1000    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01994791547767818,0.01994791547767818,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.020156248798593877,0.020156248798593877,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,4.e-2};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: y-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01588509222087653,0.01588509222087653,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: y-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015201862447935602,0.015201862447935602,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.020531187860066427,0.020531187860066427,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[100], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=100    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01588509222087653,0.01588509222087653,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,2.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[100], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=100    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smpn', ...
+                  'plist',[100], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smpn(1)=100    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+appl.var = {'freq','100'};
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femeig(fem, ...
+               'solcomp',{'u','th','v'}, ...
+               'outcomp',{'u','th','v'}, ...
+               'rowscale','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',1, ...
+         'title','eigfreq_smeulip(1)=0    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02470940812130036,0.02470940812130036,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',3, ...
+         'title','eigfreq_smeulip(3)=3228.973465    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.017615460797426208,0.017615460797426208,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+appl.var = {'freq','100'};
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femeig(fem, ...
+               'solcomp',{'u','th','v'}, ...
+               'outcomp',{'u','th','v'}, ...
+               'rowscale','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',1, ...
+         'title','eigfreq_smeulip(1)=0    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',2, ...
+         'title','eigfreq_smeulip(2)=788.250516    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.017781123438154356,0.017781123438154356,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01816252479734628,0.01816252479734628,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+appl.var = {'freq','100'};
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015657348963229553,0.015657348963229553,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015657348963229553,0.015657348963229553,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+appl.var = {'freq','100'};
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+appl.var = {'freq','100'};
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femeig(fem, ...
+               'solcomp',{'u','th','v'}, ...
+               'outcomp',{'u','th','v'}, ...
+               'rowscale','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',1, ...
+         'title','eigfreq_smeulip(1)=0    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015657348963229553,0.015657348963229553,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'solnum',2, ...
+         'title','eigfreq_smeulip(2)=561.550658    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.017781123438154356,0.017781123438154356,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip,freq_smpn', ...
+                  'plist',[1000,1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip,freq_smpn(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.01742235920999361,0.01742235920999361,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.016055899664111757,0.016055899664111757,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'deformscale',1, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015657348963229553,0.015657348963229553,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.015657348963229553,0.015657348963229553,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,4.e-2};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'rowscale','off', ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'lindata',{'u','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Surface: x-displacement [m]   Boundary: x-displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02778467743293099,0.02778467743293099,-0.01249999962747097,0.042499997094273566,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.constrcond = {'free','norot'};
+pnt.ind = [1,1,2,1,1,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = {0.1,1.e-3};
+bnd.Iyy = {8.33e-6,8.33e-14};
+bnd.A = {0.01,1.e-6};
+bnd.usage = {0,1};
+bnd.dampingtype = {'Rayleigh','nodamping'};
+bnd.ind = [1,1,1,2,1,1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+
+% Application mode 2
+clear appl
+appl.mode.class = 'SmePlaneStrain';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smpn';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,0,-1000};
+bnd.constrcond = {'free','displacement','free'};
+bnd.Hy = {0,1,0};
+bnd.ind = [3,2,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1];
+appl.equ = equ;
+fem.appl{2} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/BeamTest2.m",".m","13350","441","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=rect2('1.e-3','2.e-2','base','center','pos',{'0','1.e-2'},'rot','0');
+g2=rect2('1.e-2','1.e-2','base','center','pos',{'0','-5.e-3'},'rot','0');
+g3=rect2('0.01','5.e-3','base','center','pos',{'0','-0.0025'},'rot','0');
+g4=rect2('0.01','0.010','base','center','pos',{'0','-0.005'},'rot','0');
+
+% Analyzed geometry
+clear s
+s.objs={g1,g4};
+s.name={'R1','R2'};
+s.tags={'g1','g4'};
+
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, ...
+                 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{4,[0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1],5,[0 0.3333333333333333 0.6666666666666666 1]}, ...
+                 'hauto',5);
+
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, ...
+                 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{1,[0 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4 0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 0.85 0.875 0.9 0.925 0.95 0.975 1],2,[0 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4 0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 0.85 0.875 0.9 0.925 0.95 0.975 1],4,[0 0.0125 0.025 0.0375 0.05 0.0625 0.075 0.0875 0.1 0.1125 0.125 0.1375 0.15 0.1625 0.175 0.1875 0.2 0.2125 0.225 0.2375 0.25 0.2625 0.275 0.2875 0.3 0.3125 0.325 0.3375 0.35 0.3625 0.375 0.3875 0.4 0.4125 0.425 0.4375 0.45 0.4625 0.475 0.4875 0.5 0.5125 0.525 0.5375 0.55 0.5625 0.575 0.5875 0.6 0.6125 0.625 0.6375 0.65 0.6625 0.675 0.6875 0.7 0.7125 0.725 0.7375 0.75 0.7625 0.775 0.7875 0.8 0.8125 0.825 0.8375 0.85 0.8625 0.875 0.8875 0.9 0.9125 0.925 0.9375 0.95 0.9625 0.975 0.9875 1],5,[0 0.25 0.5 0.75 1]}, ...
+                 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000};
+bnd.ind = [2,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'mises_smps','cont','internal','unit','Pa'}, ...
+         'trimap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: von Mises stress [Pa]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.023745386923123772,0.023745386923123772,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: x-displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.0213124995236285,0.0213124995236285,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'v_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: Disp. amplitude y-dir. [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021093749528517947,0.021093749528517947,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: Disp. amplitude x-dir. [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02121874952572398,0.02121874952572398,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.02121874952572398,0.02121874952572398,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[90:10:110], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.024374999455176294,0.024374999455176294,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'solnum','end', ...
+         'title','freq_smps(3)=110    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021531249518739054,0.021531249518739054,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[10000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'title','freq_smps(1)=10000    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021531249518739054,0.021531249518739054,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[1.806e5], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'title','freq_smps(1)=1.806e5    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021093749528517947,0.021093749528517947,-0.011499999742954969,0.02149999951943755,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[1.8e5], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'title','freq_smps(1)=1.8e5    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021093749528517954,0.021093749528517954,-0.011598360396494152,0.021598360172976724,-1,1]);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = 1.e-3;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','v'}, ...
+                  'outcomp',{'u','v'}, ...
+                  'pname','freq_smps', ...
+                  'plist',[3e5], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'tridata',{'u_amp_smps','cont','internal','unit','m'}, ...
+         'trimap','jet(1024)', ...
+         'deformsub',{'u','v'}, ...
+         'title','freq_smps(1)=3e5    Surface: Disp. amplitude x-dir. [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.021093749528517954,0.021093749528517954,-0.011598360396494152,0.021598360172976724,-1,1]);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Test/Euler1.m",".m","2673","112","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=curve2([0,0],[0,0.04]);
+
+% Analyzed geometry
+clear c
+c.objs={g1};
+c.name={'B1'};
+c.tags={'g1'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, ...
+                  'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear pnt
+pnt.Fx = {0,.01};
+pnt.constrcond = {'free','norot'};
+pnt.m = {0,7.85e-4};
+pnt.ind = [2,1];
+appl.pnt = pnt;
+clear bnd
+bnd.heightz = 1.e-3;
+bnd.Iyy = 8.33e-14;
+bnd.A = 1.e-6;
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1];
+appl.bnd = bnd;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, ...
+                  'solcomp',{'u','th','v'}, ...
+                  'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', ...
+                  'plist',[1000], ...
+                  'oldcomp',{}, ...
+                  'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'title','freq_smeulip(1)=1000    Boundary: Total displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.022227742774392753,0.022227742774392753,-0.0019999999552965165,0.041999999061226846,-1,1]);
+
+% Plot solution
+postplot(fem, ...
+         'lindata',{'disp_smeulip','cont','internal','unit','m'}, ...
+         'linmap','jet(1024)', ...
+         'deformbnd',{'u','v'}, ...
+         'title','freq_smeulip(1)=1000    Boundary: Total displacement [m]   Deformation: Displacement [m]', ...
+         'refine',1, ...
+         'axisvisible','off', ...
+         'axis',[-0.016124999639578166,0.016124999639578166,-0.0019999999552965165,0.041999999061226846,-1,1]);
+
+% Integrate
+I1=postint(fem,'u', ...
+           'unit','m', ...
+           'dl',[1], ...
+           'edim',0);
+
+% Integrate
+I2=postint(fem,'u', ...
+           'unit','m', ...
+           'dl',[2], ...
+           'edim',0);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/RunBeamFR.m",".m","630","25","function [freqs,u] = RunBeamFR( hAcross, sType )
+
+tic
+freqs = [100:50:400,405:5:700,710:10:790,800:100:3000,3000:10:3100,3100:5:3300,3350:10:3500,3550:50:4500];
+%freqs = [100:50:400,405:5:700,710:10:800];
+freqs = [100:2:500];
+n = length(freqs)
+for i = 1:n
+  u(i) = BeamFR( freqs(i), hAcross, sType );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flbase = 'Data/BeamFR';
+flend = '.dat';
+flmid = num2str( sType );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/RunBeamAngleFR.m",".m","412","23","function [freqs,u,v] = RunBeamFR( hAcross, sType )
+
+tic
+freqs = [10:5:10000];
+size(freqs)
+
+for i = 1:length(freqs)
+  [a,b] = BeamAngleFR( freqs(i), hAcross, sType );
+  u(i) = a;
+  v(i) = b;
+end
+
+flbase = 'Data/BeamAngleFR';
+flend = '.dat';
+flmid = num2str( sType );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; u; v];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/RunBeamSMPS.m",".m","321","15","function [freqs,u] = RunBeam3( freqs )
+
+tic
+%freqs = [100:50:450,455:5:600,600:100:3000,3000:10:3100,3100:5:3300,3350:10:3500,3550:50:4000];
+freqs = [100:2:500];
+n = length(freqs);
+for i = 1:n
+  u(i) = BeamSMPS( freqs(i) );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/BeamAngleFR.m",".m","2202","107","function [u,v] = BeamAngleFR( freq, hAcross, sType )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+
+%hAcross = 8;
+%freq = 1000;
+%sType = 2;
+
+b = 1.e-3;
+L = 4.e-2;
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 1.e-2;
+omega = 2*pi*freq;
+F = -1000 * th;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBeam=rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass=rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+
+% Analyzed geometry
+clear s
+s.objs={gBeam,gMass};
+s.name={'Beam','Mass'};
+s.tags={'gBeam','gMass'};
+
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+h1 = 0:(hscl/10):1;
+h4 = 0:(hscl/40):1;
+h5 = 0:hscl:1;
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{1,h1,2,h1,4,h4,5,h5}, 'hauto',5);
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,0,{'F*u_test';'F*v_test'}};
+bnd.constr = {0,{0;'v'},0};
+bnd.ind = [3,2,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)/2*K3*(uy+vx)/2';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+% Subdomain settings
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = [1,1];
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+fem.const = { 'th',th, 'K1',K1, 'K2',K2, 'K3',K3, 'rho',rho, 'omega',omega, 'F', F };
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+  v = postinterp(fem, 'v', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/BeamFR.m",".m","2334","116","function [u] = BeamFR( freq, hAcross, sType )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+%hAcross = 8;
+%freq = 1000;
+%sType = 2;
+
+b = 5.e-4;
+L = 4.e-2;
+th = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 5.e-3;
+omega = 2*pi*freq;
+F = -1000 * th;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBeam=rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass=rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+SubBeam = [2,1];
+
+clear s
+s.objs={gBeam,gMass};
+s.name={'Beam','Mass'};
+s.tags={'gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+h1 = 0:(hscl/10):1;
+h4 = 0:(hscl/80):1;
+h5 = 0:hscl:1;
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{1,h1,2,h1,4,h4,5,h5}, 'hauto',5);
+
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,0,'F*u_test'};
+bnd.constr = {0,{0;'v'},0};
+bnd.ind = [3,2,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = [1,1];
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rho,rho}, 'K1',{K1,K1}, 'K2',{K2,K2}, 'K3',{K3,K3} };
+fem.const = { 'th',th, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/NoMass/BeamSMPS.m",".m","1620","80","function [u] = BeamSMPS( freq )
+
+flclear fem
+
+b = 5.e-4;
+L = 4.e-2;
+h = 1.e-3;
+rho = 7850;
+blk = 5.e-3;
+
+% Geometry
+g1=rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+g2=rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+
+% Analyzed geometry
+clear s
+s.objs={g1,g2};
+s.name={'Beam','Base'};
+s.tags={'g1','g2'};
+
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+
+% Create mapped quad mesh
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[9],[8, 5, 3],[1]},{}}, ...
+                 'edgelem',{1,[0:.0125:1],2,[0:.0125:1],4,[0:(.003125/2):1],5,[0:.125:1]}, 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+clear bnd
+bnd.Fx = {0,-1000,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.loadtype = {'length','area','length'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = h;
+equ.ind = [1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+
+if( 3 > 2 )
+
+% Extend mesh
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+p = [blk/2;-blk/2];
+u = postinterp(fem, 'u', p);
+
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/EndMass/RunBeamFR.m",".m","528","24","function [freqs,u] = RunBeamFR( hAcross, sType )
+
+tic
+freqs = [3000:10:4000];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BeamFR( freqs(i), hAcross, sType );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flbase = 'Data/BeamFR';
+flend = '.dat';
+flmid = num2str( sType );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/EndMass/RunBeamSMPS.m",".m","534","24","function [freqs,u] = RunBeamSMPS( hAcross, sType )
+
+tic
+freqs = [3000:10:4000];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  u(i) = BeamSMPS( freqs(i), hAcross, sType );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flbase = 'Data/BeamSMPS';
+flend = '.dat';
+flmid = num2str( sType );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; abs(u); real(u); imag(u) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/EndMass/BeamFR.m",".m","2642","124","%function [u,v] = BeamFR( freq, hAcross, sType )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 0;
+hAcross = 8;
+freq = 1900;
+sType = 2;
+
+b = 5.e-4;
+L = 4.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 6.e-3;
+omega = 2*pi*freq;
+F = -1000 * th;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base', 'center', 'pos',[0,L+d/2], 'rot','0');
+SubBeam = [1,1,2];
+
+clear s
+s.objs={gBase,gBeam,gMass};
+s.name={'Base','Beam','Mass'};
+s.tags={'gBase','gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+hblk = 0:(b/blk*hscl):1;
+hmx = 0:(b/l*hscl):1;
+hmy = 0:(b/d*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(b/L*hscl):1;
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[14],[11, 8, 3],[1]},{[5, 9, 12],[13],[6],[4]},{[8],[10],[9],[7]}}, ...
+                 'edgelem',{ 1,hblk, 2,hblk, 4,hmy, 6,hmx, 7,hby, 8,hbx}, 'hauto',5);
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'F*u_test',0};
+bnd.constr = {0,0,{0;'v'}};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rho,rho}, 'K1',{K1,K1}, 'K2',{K2,K2}, 'K3',{K3,K3} };
+fem.const = { 'th',th, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [blk/2;-blk/2];
+  y = 0:(L/100):L;
+  x = zeros(size(y));
+  pv = [x;y];
+  u = postinterp(fem, 'u', p);
+  v = postinterp(fem, 'u', pv);
+end
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Single/EndMass/BeamSMPS.m",".m","1973","88","%function [u] = BeamSMPS( freq, hAcross, sType )
+
+flclear fem;
+
+bSolve = 0;
+hAcross = 8;
+freq = 1900;
+sType = 2;
+
+b = 5.e-4;
+L = 4.e-2;
+th = 1.e-3;
+l = 5.e-3;
+d = 1.e-3;
+rho = 7850;
+E = 2.0e11;
+nu = 0.33;
+blk = 6.e-3;
+omega = 2*pi*freq;
+F = -1000;
+
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base', 'center', 'pos',[0,L+d/2], 'rot','0');
+SubBeam = [1,1,2];
+
+clear s
+s.objs={gBase,gBeam,gMass};
+s.name={'Base','Beam','Mass'};
+s.tags={'gBase','gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+hscl = 1/hAcross;
+hblk = 0:(b/blk*hscl):1;
+hmx = 0:(b/l*hscl):1;
+hmy = 0:(b/d*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(b/L*hscl):1;
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[14],[11, 8, 3],[1]},{[5, 9, 12],[13],[6],[4]},{[8],[10],[9],[7]}}, ...
+                 'edgelem',{ 1,hblk, 2,hblk, 4,hmy, 6,hmx, 7,hby, 8,hbx}, 'hauto',5);
+
+clear appl
+appl.mode.class = 'SmePlaneStress';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smps';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear bnd
+bnd.Fx = {0,F,0};
+bnd.constrcond = {'free','free','displacement'};
+bnd.loadtype = {'length','area','length'};
+bnd.Hy = {0,0,1};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.thickness = th;
+equ.ind = [1,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem,'solcomp',{'u','v'}, 'outcomp',{'u','v'}, ...
+                  'pname','freq_smps','plist',[freq], ...
+                  'oldcomp',{}, 'nonlin','off', 'linsolver','spooles');
+  fem0 = fem;
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Enclosed/RunBeamFR.m",".m","436","23","function [freqs,u,v] = RunBeamFR( hAcross, sType, Fphi )
+
+tic
+
+freqs = [100:50:400,405:5:700,710:10:800];
+size(freqs)
+for i = 1:length(freqs)
+  [a,b] = BeamFR( freqs(i), hAcross, sType, Fphi );
+  u(i) = a;
+  v(i) = b;
+end
+
+flbase = 'Data/BeamFR';
+flend = '.dat';
+flmid = num2str( sType );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; u; v ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/Enclosed/BeamFR.m",".m","4002","145","function [u,v] = BeamFR( freq, hAcross, sType, Fphi )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+%Fphi = 0;
+%hAcross = 4;
+%freq = 65;
+%sType = 2;
+
+F = -1000;
+dScale = 1.e-1;                           %  Size Scaling - 1.0 is a meter resonator
+
+%  Dimensions
+mx = dScale * 1.0;                        %  Matrix Unit Side
+mt = dScale * 2.e-1;                      %  Matrix Unit Wall Thickness
+bx = dScale * 2.e-2;                      %  Beam Width
+by = dScale * 5.5e-1;                     %  Beam Height
+hz = dScale * 1.0;                        %  Depth
+
+%  Material Parameters
+rhob = 7850;
+rhom = rhob;
+Eb = 2.0e11;
+Em = Eb;
+nub = 0.33;
+num = nub;
+
+%  Stiffness Parameters
+if( sType == 1 )
+  Db = Eb/(1-nub^2);
+  Dm = Em/(1-num^2);
+  Kb = [Db, Db*nub, Db*(1-nub)/2];
+  Km = [Dm, Dm*num, Dm*(1-num)/2];
+else
+  Db = Eb/(1+nub)/(1-2*nub);
+  Dm = Em/(1+num)/(1-2*num);
+  Kb = [Db*(1-nub), Db*nub, Db*(1-2*nub)/2];
+  Km = [Dm*(1-num), Dm*num, Dm*(1-2*num)/2];
+end
+
+omega = 2*pi*freq;
+Fx = -F * cos( Fphi ) * hz;
+Fy = -F * sin( Fphi ) * hz;
+
+% Geometry
+mi = mx - 2*mt;
+gMat1 = rect2( mt,mt, 'base','corner', 'pos',[ 0, 0 ], 'rot','0' );
+gMat2 = rect2( mt,mi, 'base','corner', 'pos',[ 0, mt], 'rot','0' );
+gMat3 = rect2( mt,mt, 'base','corner', 'pos',[ 0, mi+mt], 'rot','0' );
+gMat4 = rect2( (mi-bx)/2,mt, 'base','corner', 'pos',[mt,0], 'rot','0' );
+gMat5 = rect2( bx,mt, 'base','corner', 'pos',[(mx-bx)/2,0], 'rot','0' );
+gMat6 = rect2( (mi-bx)/2,mt, 'base','corner', 'pos',[(mx+bx)/2,0], 'rot','0' );
+gMat7 = rect2( mt,mt, 'base','corner', 'pos',[mx-mt,0], 'rot','0' );
+gMat8 = rect2( mt,mi, 'base','corner', 'pos',[mx-mt,mt], 'rot','0' );
+gMat9 = rect2( mi,mt, 'base','corner', 'pos',[mt,mx-mt], 'rot','0' );
+gMat10 = rect2( mt,mt, 'base','corner', 'pos',[mx-mt,mx-mt], 'rot','0' );
+gBeam = rect2( bx,by, 'base','corner', 'pos',[(mx-bx)/2,mt], 'rot','0' );
+SubBeam = [2,2,2,2,2,2,2,2,2,2,1];
+
+clear s
+s.objs={gMat1,gMat2,gMat3,gMat4,gMat5,gMat6,gMat7,gMat8,gMat9,gMat10,gBeam};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hs0 = 1/hAcross;
+bs = bx*2;
+hs1 = hs0 * 1/ceil(mt/bs);                   %  Corner
+hs2 = hs0 * 1/ceil(mi/bs);                   %  Sides
+hs3 = hs0 * 1/ceil((mi-bx)/2/bs);            %  Bottom            
+hs4 = hs0 * 1/ceil(by/bs);                   %  Beam
+h0 = 0:hs0:1;
+h1 = 0:hs1:1;
+h2 = 0:hs2:1;
+h3 = 0:hs3:1;
+h4 = 0:hs4:1;
+fem.mesh=meshmap(fem, 'edgelem',{ 1,h1, 2,h1, 3,h2, 5,h1, 9,h3, 13,h2, 16,h0, ...
+				  17,h4, 21,h3, 25,h1, 26,h2}, 'hauto',5);
+
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'Fx*u_test',{0;'Fy*v_test'}};
+bnd.constr = 0;
+bnd.ind = [2,3,2,1,2,1,1,1,3,1,1,1,1,1,1,3,1,1,1,1,3,1,1,1,3,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)/2*K3*(uy+vx)/2';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rhob,rhom}, 'K1',{Kb(1),Km(1)}, 'K2',{Kb(2),Km(2)}, 'K3',{Kb(3),Km(3)} };
+fem.const = { 'th',hz, 'omega',omega, 'Fx', Fx, 'Fy',Fy };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [mx;mx/2];
+  u = postinterp( fem, 'u', p );
+  v = postinterp( fem, 'v', p );
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/NewBeamCentered.m",".m","2792","128","function [u] = NewBeamCentered( freq )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+hAcross = 12;
+%freq = 460;
+sType = 2;
+
+bx = 1.6e-3;
+by = 14.0e-3;
+cx = 5.0e-3;
+cy = 16.0e-3;
+mx = 15.e-3;
+my = 15.e-3;
+z = 19.0e-3;
+
+omega = 2*pi*freq;
+F = -1000 * z;
+
+rho1 = 7700;
+rho2 = ( cx*cy*z*rho1+260.e-3 ) / (cx*cy*z);
+E = 2.0e11;
+nu = 0.33;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBase = rect2( cx,cy, 'base','center', 'pos',[0,0],             'rot','0' );
+gbTop = rect2( bx,by, 'base','center', 'pos',[0, (by+cy)/2],    'rot','0' );
+gbBot = rect2( bx,by, 'base','center', 'pos',[0,-(by+cy)/2],    'rot','0' );
+gmTop = rect2( mx,my, 'base','center', 'pos',[0, by+(my+cy)/2], 'rot','0' );
+gmBot = rect2( mx,my, 'base','center', 'pos',[0,-by-(my+cy)/2], 'rot','0' );
+
+SubBeam = [2,1,1,1,1];
+
+clear s
+s.objs={gBase,gbTop,gbBot,gmTop,gmBot};
+s.name={'Base','bTop','bBot','mTop','mBot'};
+s.tags={'gBase','gbTop','gbBot','gmTop','gmBot'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+hcx = 0:(bx/((cx-bx)/2)*hscl):1;
+hcy = 0:(bx/cy*hscl):1;
+hmx = 0:(bx/mx*hscl):1;
+hmy = 0:(bx/my*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(bx/by*hscl):1;
+fem.mesh=meshmap(fem, 'edgelem',{1,hmy,2,hmx,4,hmy,6,hmx,7,hcy,8,hcx,10,hby,11,hbx,13,hby,14,hbx,18,hcx}, 'hauto',5 );
+
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'F*u_test'};
+bnd.constr = {0,0};
+bnd.ind = [1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rho1,rho2}, 'K1',K1, 'K2',K2, 'K3',K3 };
+fem.const = { 'th',z, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [0;0];
+  u = postinterp(fem, 'u', p);
+end
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/BMTheory.m",".m","1739","83","function [u,ml,ms,A] = BMTheory( f )
+%  m = Mass on beam
+%  J = Moment of inertia of m
+
+omega = 2 * pi * f;
+Fin = 1000;
+
+%  Dimensions
+%  Thickness
+th = 19.0e-3;
+
+%  Base (Center)
+cx = 5.e-3;
+cy = 16.e-3;
+Ac = cy*th;
+Vc = Ac*cx;
+
+%  Beam
+bx = 1.6e-3;
+by = 14.0e-3;
+Ab = by*th;
+Vb = Ab*bx;
+Ib = bx^3*th/12;
+
+%  Mass
+mx = 15.0e-3;
+my = 15.0e-3;
+Am = my*th;
+Vm = Am*mx;
+
+%  Material Properties
+E = 2.0e11;
+rho0 = 7700;
+rhoc = ( Vc*rho0+260.e-3 ) / Vc;
+rhob = rho0;
+rhom = rho0;
+
+%  Mass Properties
+m = rhom * Vm;
+J = m/12 * ( mx^2 + my^2 );
+M0 = rhoc * Vc;
+F = Fin * Ac;
+
+%  Constants
+lambda = ( rhob * Ab / E / Ib * 4 * pi^2 .* f.^2 ) .^ (1/4);
+a = lambda * by;
+b = m / rhob / Ab / by;
+k = sqrt(J./m);
+l = my/2;
+if( m == 0 )
+    k = 0;
+end
+
+R1 = b .* a.^3 .* ( ( k^2 + l^2 ) / by^2 );
+R2 = b .* a.^2 .* ( l / by );
+s = sin(a);
+sh = sinh(a);
+c = cos(a);
+ch = cosh(a);
+
+F1 = 1 + c.*ch - s.*sh - 2*b.*a.*s.*ch - 2*R1.*c.*sh + (R1.*b.*a-R2.^2).*(1-c.*ch+s.*sh);
+F2 = 1 + c.*ch + s.*sh + 2*b.*a.*c.*sh - 2*R1.*s.*ch + (R1.*b.*a-R2.^2).*(1-c.*ch-s.*sh);
+F3 = (s.*ch+c.*sh).*(1-R1.*b.*a+R2.^2) + 2*( c.*ch.*b.*a - R1.*s.*sh - R2.*(s.*ch-c.*sh) );
+D = 1 + c.*ch - b.*a.*(s.*ch-c.*sh) - R1.*( s.*ch + c.*sh - b.*a.*(1-c.*ch) ) - 2*R2.*s.*sh - R2.^2.*(1-c.*ch);
+
+uL = 1./(2*D) .* ( F1.*c + F2.*ch + F3.*(s-sh) );
+uyL = 1./(2*D) .* lambda .* ( -F1.*s + F2.*sh + F3.*(c-ch) );
+uyyL = 1./(2*D) .* lambda.^2 .* ( -F1.*c + F2.*ch - F3.*(s+sh) );
+uyyyL = 1./(2*D) .* lambda.^3 .* ( F1.*s + F2.*sh - F3.*(c+ch) );
+
+M1 = rhob*Ab./(2*D.*lambda) .* ( F1.*s + F2.*sh - F3.*(c+ch-2) );
+M = M0 + 2 * ( M1 + m*uL + m*(my/2)*uyL ); 
+
+M1(length(M1));
+uL(length(uL));
+uyL(length(uyL));
+
+u = F ./ M ./ omega.^2;
+ml = Vc*rhoc + Vb*rhob + Vm*rhom;
+ms = Vc*rhoc;
+A = Ac;
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/Euler.m",".m","1529","85","function [u,v] = Euler( freqs )
+
+tic
+
+flclear fem
+
+b = 1.55e-3;
+L = 50.e-3;
+th = 19.e-3;
+rho = 7700;
+E = 2.0e11;
+nu = 0.33;
+blk = 12.e-3;
+
+F = -1000 * th * blk;
+M0 = blk*blk*th*rho;
+I = b^3*th/12;
+A = b*th;
+
+% Geometry
+gBeam=curve2([0,0],[0,L]);
+
+% Analyzed geometry
+clear c
+c.objs={gBeam};
+c.name={'Beam'};
+c.tags={'gBeam'};
+
+fem.draw=struct('c',c);
+fem.geom=geomcsg(fem);
+
+% Initialize mesh
+fem.mesh=meshinit(fem, 'hauto',5);
+
+% (Default values are not included)
+
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeInPlaneEulerBeam';
+appl.module = 'SME';
+appl.gporder = 6;
+appl.cporder = 1;
+appl.assignsuffix = '_smeulip';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear pnt
+pnt.Fx = {0,F};
+pnt.Fy = {0,F};
+pnt.constrcond = {'free','norot'};
+pnt.m = {0,M0};
+pnt.ind = [2,1];
+appl.pnt = pnt;
+
+clear bnd
+bnd.heightz = th;
+bnd.Iyy = I;
+bnd.A = A;
+bnd.dampingtype = 'nodamping';
+bnd.ind = [1];
+appl.bnd = bnd;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+fem.xmesh=meshextend(fem);
+
+% Solve problem
+fem.sol=femstatic(fem, 'solcomp',{'u','th','v'}, 'outcomp',{'u','th','v'}, ...
+                  'pname','freq_smeulip', 'plist',freqs, ...
+                  'oldcomp',{}, 'nonlin','off');
+
+% Save current fem structure for restart purposes
+fem0=fem;
+
+solnums = 1:length(freqs);
+u = postint(fem,'u', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+v = postint(fem,'v', 'unit','m', 'dl',[1], 'edim',0, 'solnum',solnums);
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/BoltBeam.m",".m","2665","102","function [w] = BoltBeam( freq )
+
+tic
+flclear fem
+
+%freq = 500;
+
+xStrip = 0.0292;
+xStrip = 0.037;
+yStrip = 0.019;
+zStrip = 0.00155;
+dsx = xStrip/2;
+pF = 1/yStrip/zStrip;
+
+xHex = 0.0159;
+zHex = 0.00695;
+dhx = 0.02125;
+dhz = xStrip - zHex/2;
+sc = sqrt(3)/3;
+
+rho = 7700;
+E = 2.1e11;
+nu = 0.33;
+
+gHex11=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,dhz],'axis',{'0','0','1'},'rot','0' );
+gHex21=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,dhz],'axis',{'0','0','1'},'rot','60' );
+gHex31=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,dhz],'axis',{'0','0','1'},'rot','120' );
+gHex1=geomcomp( {gHex11,gHex21,gHex31},'ns',{'g1','g2','g3'},'sf','g1+g2+g3','face','none','edge','all' );
+gHex12=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,-dhz],'axis',{'0','0','1'},'rot','0' );
+gHex22=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,-dhz],'axis',{'0','0','1'},'rot','60' );
+gHex32=block3( xHex,sc*xHex,zHex,'base','center','pos',[dhx,0,-dhz],'axis',{'0','0','1'},'rot','120' );
+gHex2=geomcomp( {gHex12,gHex22,gHex32},'ns',{'g1','g2','g3'},'sf','g1+g2+g3','face','none','edge','all' );
+gStrip=block3( xStrip,yStrip,zStrip,'base','center','pos',[dsx,0,0],'axis',{'0','0','1'},'rot','0' );
+
+% Geometry objects
+clear s
+s.objs={gStrip,gHex1,gHex2};
+s.name={'Strip','Hex1','Hex2'};
+s.tags={'gStrip','gHex1','gHex2'};
+
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );
+BndAll = ones( 1, size(ft,1) );            
+
+hh = zeros(1,54);
+hh(1:2:53) = 1:27;
+hh(2) = zStrip;
+hh(4:2:54) = 0.02;
+
+% Initialize mesh
+fem.mesh=meshinit(fem, 'hauto',6, 'hmaxsub',hh, 'point',[], 'edge',[], 'face',[], 'subdomain',[1:27]) ;
+fem.border = 1;
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = rho;
+equ.nu = nu;
+equ.E = E;
+appl.equ = equ;
+
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.Fz = {0,pF};
+bnd.ind = [BndAll];
+bnd.ind(1) = 2;
+appl.bnd = bnd;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+% Solve problem
+fem.xmesh=meshextend(fem);
+fem.sol = femstatic( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, 'pname','freq_smsld', ...
+		     'plist',[freq], 'oldcomp',{}, 'linsolver','spooles' );
+fem0=fem;
+
+p = [0;0;0];
+w = postinterp(fem, 'w', p);
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/Beam.m",".m","2231","114","function [u] = Beam( freq )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+hAcross = 15;
+%freq = 1900;
+sType = 1;
+
+b = 1.55e-3;
+L = 50.0e-3;
+th = 19.0e-3;
+rho = 7700;
+E = 2.0e11;
+nu = 0.33;
+blk = 12.e-3;
+omega = 2*pi*freq;
+F = -1000 * th;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+SubBeam = [1,1];
+
+clear s
+s.objs={gBase,gBeam};
+s.name={'Base','Beam'};
+s.tags={'gBase','gBeam'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+hblk = 0:(b/blk*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(b/L*hscl):1;
+fem.mesh=meshmap(fem, 'edgelem',{1,hblk,2,hblk,4,hby,6,hbx}, 'hauto',5 );
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'F*u_test',0};
+bnd.constr = {0,0,{0;'v'}};
+bnd.ind = [2,3,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',rho, 'K1',K1, 'K2',K2, 'K3',K3 };
+fem.const = { 'th',th, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [blk/2;-blk/2];
+  u = postinterp(fem, 'u', p);
+end
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/GetBeamTheory.m",".m","983","46","function [u,ut] = GetBeamTheory( f, u );
+
+P0 = 1000;
+[ut,ml,ms,A] = BMTheory(f);
+
+Atube = pi/4 * (8.9e-2)^2;
+F = P0*A;
+P = F/Atube;
+
+omega = 2 * pi * f;
+vt = i * omega .* ut;
+v  = i * omega .* u;
+
+zt   = P./vt;
+z   = P./v;
+zm1 = i * omega * ml / Atube;
+zm2 = i * omega * ms / Atube;
+
+rho = 1.2;
+c = 344;
+
+tl   = 20 * log10( abs( 1 + 1/2 * z   / rho / c ) );
+tlt  = 20 * log10( abs( 1 + 1/2 * zt  / rho / c ) );
+tlm1 = 20 * log10( abs( 1 + 1/2 * zm1 / rho / c ) );
+tlm2 = 20 * log10( abs( 1 + 1/2 * zm2 / rho / c ) );
+
+
+figure(1)
+plot(f,tl,f,tlt,f,tlm1,f,tlm2);
+legend('FEM','Beam Theory','Total Mass','Container Mass')
+xlabel('Frequency (Hz)')
+ylabel('Transmission Loss (dB)')
+grid on;
+
+%figure(2)
+%plot(f,tl,f,tlm1,f,tlm2);
+%legend('FEM','Total Mass','Container Mass')
+%xlabel('Frequency (Hz)')
+%ylabel('Transmission Loss (dB)')
+
+%figure(3)
+%plot(f,tlt,f,tlm1,f,tlm2);
+%legend('Beam Theory','Total Mass','Container Mass')
+%xlabel('Frequency (Hz)')
+%ylabel('Transmission Loss (dB)')
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/RunBeam.m",".m","435","21","function [freqs,w] = RunBeam
+
+tic
+freqs = [1:4,5:5:495,500:0.5:600,605:5:1200];
+strcat(num2str(length(freqs)),' iterations')
+n = length(freqs);
+for i = 1:n
+  w(i) = NewBeamCentered( freqs(i) );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/BeamCenterFine.dat';
+Fout = [ freqs; abs(w); w ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e \n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/BeamMass.m",".m","2656","126","%function [u,v] = BeamMass( freq )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 0;
+hAcross = 8;
+freq = 1900;
+sType = 1;
+
+b = 1.55e-3;
+L = 13.3e-3;
+th = 19.0e-3;
+l = 11.6e-3;
+d = 15.9e-3;
+rho = 7700;
+E = 2.0e11;
+nu = 0.33;
+blk = 12.e-3;
+omega = 2*pi*freq;
+F = -1000 * th;
+
+m = l*d*th*rho;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBase = rect2( blk,blk, 'base','center','pos',[0,-blk/2], 'rot','0' );
+gBeam = rect2( b,L, 'base','center', 'pos',[0,L/2], 'rot','0' );
+gMass = rect2( l,d, 'base', 'center', 'pos',[0,L+d/2], 'rot','0');
+SubBeam = [1,1,2];
+
+clear s
+s.objs={gBase,gBeam,gMass};
+s.name={'Base','Beam','Mass'};
+s.tags={'gBase','gBeam','gMass'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+hblk = 0:(b/blk*hscl):1;
+hmx = 0:(b/l*hscl):1;
+hmy = 0:(b/d*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(b/L*hscl):1;
+fem.mesh=meshmap(fem, 'edgegroups',{{[2],[14],[11, 8, 3],[1]},{[5, 9, 12],[13],[6],[4]},{[8],[10],[9],[7]}}, ...
+                 'edgelem',{ 1,hblk, 2,hblk, 4,hmy, 6,hmx, 7,hby, 8,hbx}, 'hauto',5);
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'F*u_test',0};
+bnd.constr = {0,0,{0;'v'}};
+bnd.ind = [2,3,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rho,rho}, 'K1',{K1,K1}, 'K2',{K2,K2}, 'K3',{K3,K3} };
+fem.const = { 'th',th, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [blk/2;-blk/2];
+  y = 0:(L/100):L;
+  x = zeros(size(y));
+  pv = [x;y];
+  u = postinterp(fem, 'u', p);
+  v = postinterp(fem, 'u', pv);
+end
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Beam/SymmetricBeams/NewBeam.m",".m","2986","131","function [u] = NewBeam( freq )
+%  freq:  Input Frequency,  hAcross:  Number of Elements Across Beam
+%  sType = 1:  Plane Stress
+%          2:  Plane Strain  
+
+flclear fem;
+bSolve = 1;
+hAcross = 6;
+%freq = 460;
+sType = 2;
+
+bx = 1.6e-3;
+by = 14.0e-3;
+cx = 5.0e-3;
+cy = 16.0e-3;
+mx = 15.e-3;
+my = 15.e-3;
+z = 19.0e-3;
+
+omega = 2*pi*freq;
+F = -1000 * z;
+
+rho1 = 7700;
+rho2 = ( cx*cy*z*rho1+260.e-3 ) / (cx*cy*z);
+E = 2.0e11;
+nu = 0.33;
+
+if( sType == 1 )
+  D = E/(1-nu^2);
+  K1 = D;
+  K2 = D*nu;
+  K3 = D*(1-nu)/2;
+else
+  D = E/(1+nu)/(1-2*nu);
+  K1 = D*(1-nu);
+  K2 = D*nu;
+  K3 = D*(1-2*nu)/2;
+end
+
+% Geometry
+gBase = rect2( cx,cy, 'base','center', 'pos',[-cx/2,0],             'rot','0' );
+gbTop = rect2( bx,by, 'base','center', 'pos',[-bx/2, (by+cy)/2],    'rot','0' );
+gbBot = rect2( bx,by, 'base','center', 'pos',[-bx/2,-(by+cy)/2],    'rot','0' );
+gmTop = rect2( mx,my, 'base','center', 'pos',[-mx/2, by+(my+cy)/2], 'rot','0' );
+gmBot = rect2( mx,my, 'base','center', 'pos',[-mx/2,-by-(my+cy)/2], 'rot','0' );
+
+SubBeam = [2,1,1,1,1];
+
+clear s
+s.objs={gBase,gbTop,gbBot,gmTop,gmBot};
+s.name={'Base','bTop','bBot','mTop','mBot'};
+s.tags={'gBase','gbTop','gbBot','gmTop','gmBot'};
+fem.draw=struct('s',s);
+fem.geom=geomcsg(fem);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+SubAll = ones( 1, size(st,1) );            
+
+
+%  Create Mapped Mesh
+hscl = 1/hAcross;
+hcx = 0:(bx/(cx-bx)*hscl):1;
+hcy = 0:(bx/cy*hscl):1;
+hmx = 0:(bx/mx*hscl):1;
+hmy = 0:(bx/my*hscl):1;
+hbx = 0:hscl:1;
+hby = 0:(bx/by*hscl):1;
+%fem.mesh=meshmap(fem, 'edgegroups',{{[2],[14],[11, 8, 3],[1]},{[5, 9, 12],[13],[6],[4]},{[8],[10],[9],[7]}}, ...
+%                 'edgelem',{ 1,hblk, 2,hblk, 4,hmy, 6,hmx, 7,hby, 8,hbx}, 'hauto',5);
+
+fem.mesh=meshmap(fem, 'edgelem',{1,hmy,2,hmx,4,hmy,6,hmx,7,hcy,8,hcx,10,hby,11,hbx,13,hby,15,hbx}, 'hauto',5 );
+
+
+%  Weak PDE Application Mode 
+clear appl
+appl.mode.class = 'FlPDEW';
+appl.dim = {'u','v','u_t','v_t'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_w';
+
+
+%  Boundary Settings
+clear bnd
+bnd.weak = {0,'F*u_test'};
+bnd.constr = {0,0};
+bnd.ind = [1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Subdomain settings
+pdeStart = 'th*(';
+pde1 = '-ux_test*(K1*ux+K2*vy)';
+pde2 = '-vy_test*(K2*ux+K1*vy)';
+pde3 = '-(uy_test+vx_test)*K3*(uy+vx)';
+pde4 = '+rho*omega^2*(u_test*u+v_test*v)';
+pdeEnd = ')';
+pde = strcat(pdeStart,pde1,pde2,pde3,pde4,pdeEnd);
+
+clear equ
+equ.weak = pde;
+equ.dweak = 0;
+equ.ind = SubAll;
+equ.dim = {'u','v'};
+appl.equ = equ;
+
+clear equ
+equ.ind( SubInd ) = SubBeam;
+equ.expr = { 'rho',{rho1,rho2}, 'K1',K1, 'K2',K2, 'K3',K3 };
+fem.const = { 'th',z, 'omega',omega, 'F', F };
+fem.equ = equ;
+
+
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+fem=multiphysics(fem);
+
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'u','v'},'outcomp',{'u','v'},'linsolver','spooles');
+  fem0=fem;
+
+  p = [0;0];
+  u = postinterp(fem, 'u', p);
+end
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/InPlane/RunFreqs.m",".m","519","26","function [freqs,w] = RunFreqsNoFixed( mScale )
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqsNoFixed(freqs(i), mScale);
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flbase = 'Data/FreqRespNF';
+flend = '-2.dat';
+flmid = num2str( mScale );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; w ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e\n', Fout );
+fclose( fl );
+
+toc
+%exit
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/InPlane/SymGeom.m",".m","1682","30","function [s] = SymmetricGeom( rStl, rSil, rEpx, rD )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2-rD/2,rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4+rD/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/InPlane/SymEigs.m",".m","2296","87","%function [freq] = SymEigs( ESil, ERes )
+
+bSolve = 0;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023, 0.001 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .001 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/InPlane/SymFreqs.m",".m","2380","93","function [w] = SymFreqsNoFixed( freq, mScl )
+bSolve = 1;
+%mScl = 1;
+%freq = 1000;
+%bSolve = 0;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005*mScl, 0.00775*mScl, 0.023 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .003, .0015, .003 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{});
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlane/RunFreqs.m",".m","494","29","function [freqs,w,w0,w1] = RunFreqs
+
+tic
+
+freqs = [100:10:300,301:600,610:10:1000];
+freqs = [1:10:91];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [ww,ww0,ww1] = SymFreqs( freqs(i) );
+  w(i) = ww;
+  w0(i) = ww0;
+  w1(i) = ww1;
+  
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqResp.dat';
+Fout = [ freqs; w; w0; w1 ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlane/SymGeom.m",".m","1456","27","function [s] = SymmetricGeom( rStl, rSil, rEpx, rD )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rD, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlane/SymEigs.m",".m","2322","90","%function [freq] = SymEigs( ESil, ERes )
+
+bSolve = 0;
+ESil = 1.175e5;
+ERes = 4.35e9;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023, 0.001 ];
+E = [ 2.0696e11, ESil, ERes ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0015, .0008 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4) );
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),3,h(3)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.ind = [2,2,1,2,2,1,1,1,2,1,1,1,1,1,2,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlane/SymFreqs.m",".m","2528","95","function [w,w0,w1] = SymFreqs( freq )
+
+bSolve = 1;
+%freq = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023, 0.001 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .003, .0015, .001 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4) );
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),3,h(3)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,1,3,2,1,1,1,3,1,2,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.shape = {[1;2;3;4],[1;2;3],[1;2;3]};
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{});
+  fem0=fem;
+  p0 = [0;0;-r(2)];
+  w0 = postinterp(fem, 'w', p0);
+  p1 = [r(3)/2;r(3)/2;-r(4)/2];
+  w1 = postinterp(fem, 'w', p1);
+  w = postint( fem, 'nz*w', 'unit','m^3', 'dl',[3,11], 'edim',2 );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlaneDamped/RunFreqs.m",".m","519","29","function [freqs,w,w0,w1] = RunFreqs
+
+tic
+
+freqs = [100:10:300,301:600,610:10:1000];
+freqs = [100:10:300,305:5:600,610:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [ww,ww0,ww1] = SymFreqs( freqs(i) );
+  w(i) = ww;
+  w0(i) = ww0;
+  w1(i) = ww1;
+  
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqResp.dat';
+Fout = [ freqs; w; w0; w1 ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlaneDamped/SymGeom.m",".m","1456","27","function [s] = SymmetricGeom( rStl, rSil, rEpx, rD )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rD, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlaneDamped/SymEigs.m",".m","2322","90","%function [freq] = SymEigs( ESil, ERes )
+
+bSolve = 0;
+ESil = 1.175e5;
+ERes = 4.35e9;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023, 0.001 ];
+E = [ 2.0696e11, ESil, ERes ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0015, .0008 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4) );
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),3,h(3)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.ind = [2,2,1,2,2,1,1,1,2,1,1,1,1,1,2,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Membrane/OutPlaneDamped/SymFreqs.m",".m","2706","97","function [w,w0,w1] = SymFreqs( freq )
+
+bSolve = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023, 0.001 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .003, .0015, .001 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4) );
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),3,h(3)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,1,3,2,1,1,1,3,1,2,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+equ.dampingtype = {'nodamping','Rayleigh','nodamping'};
+equ.betadK = {0,0,0};
+equ.alphadM = {0,1,0};
+equ.mixedform = {0,1,0};
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+		    'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, 'linsolver','spooles');
+  fem0=fem;
+  p0 = [0;0;-r(2)];
+  w0 = postinterp(fem, 'w', p0);
+  p1 = [r(3)/2;r(3)/2;-r(4)/2];
+  w1 = postinterp(fem, 'w', p1);
+  w = postint( fem, 'nz*w', 'unit','m^3', 'dl',[3,11], 'edim',2 );
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/SymComplexE.m",".m","2877","115","function [w] = SymComplexE( freq, eta, bUP )
+
+bSolve = 1;
+%alpha = 10;
+%beta = 5.e-4;
+%freq = 1;
+
+flclear fem;
+ESil = 2*1.175e5 * ( 1 + eta * j );
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, ESil, 4.35e9 ];
+nu = [ 0.3, 0.4999, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .005, .002, .005 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+if( bUP )
+  appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+end
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+
+if( bUP )
+  equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+  equ.mixedform = {0,1,0};
+end
+
+equ.dampingtype = {'nodamping'};
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  if( bUP )
+    fem.sol=femstatic(fem, 'solcomp',{'w','u','p','v'}, 'outcomp',{'w','u','p','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  else
+     fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  end
+  
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/RunFreqs.m",".m","571","26","function [freqs,w,v] = RunFreqs( betak )
+
+tic
+
+freqs = [10:10:800,820:20:1000,1050:50:1200,1300:100:2000];
+eta = betak * 2*pi * 492;
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  w(i) = SymComplexE( freqs(i), eta, 0 );
+  v(i) = SymComplexE( freqs(i), eta, 1 );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = strcat( 'Data/FR--', num2str(betak), '.dat' );
+Fout = [ freqs; real(w); imag(w); real(v); imag(v) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/SymGeom.m",".m","1454","27","function [s] = SymmetricGeom( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/SymRayleigh.m",".m","2891","116","function [w] = SymRayleigh( freq, betak, bUP )
+
+bSolve = 1;
+%alpha = 10;
+%beta = 5.e-4;
+%freq = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, 2*1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.4999, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .006, .0025, .006 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+if( bUP )
+  appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+end
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+
+if( bUP )
+  equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+  equ.mixedform = {0,1,0};
+end
+
+equ.dampingtype = {'Rayleigh'};
+equ.betadK = { betak };
+equ.alphadM = { 0 };
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  if( bUP )
+    fem.sol=femstatic(fem, 'solcomp',{'w','u','p','v'}, 'outcomp',{'w','u','p','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  else
+     fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  end
+
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/SymEigs.m",".m","2422","94","function [freq] = SymEigs
+
+bSolve = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, 2*1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.499, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .0025 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+%appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+%equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+%equ.mixedform = {0,1,0};
+equ.dampingtype = {'nodamping'};
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind(SubInd) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/RunFreqsUP.m",".m","606","27","function [freqs,w,v] = RunFreqsUP( betak )
+
+tic
+
+freqs = [10:100:810,820:10:1000,1020:20:1100,1200:100:2000];
+beta0 = betak * 1.e-5;
+eta = beta0 * 2*pi * 942;
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  w(i) = SymRayleigh( freqs(i), beta0, 1 );
+  v(i) = SymComplexE( freqs(i), eta, 1 );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = strcat( 'Data/FRUP-', num2str(betak), '.dat' );
+Fout = [ freqs; real(w); imag(w); real(v); imag(v) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/DampTest.m",".m","164","13","function [f,w,v] = DampTest
+
+beta = [0:30];
+n = length(beta);
+
+for k = 1:n
+  [f0,w0,v0] = RunFreqsUP( beta(k) ); 
+  f(k,:) = f0;
+  w(k,:) = w0;
+  v(k,:) = v0;
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/Data/MakePlot.m",".m","441","25","d0 = load('FRUP-0.dat');
+d1 = load('FRUP-1.dat');
+d2 = load('FRUP-2.dat');
+d3 = load('FRUP-3.dat');
+d4 = load('FRUP-4.dat');
+d5 = load('FRUP-5.dat');
+
+f = d0(:,1);
+a = (2*pi*f).^2;
+
+r0 = a .* abs( d0(:,2) );
+r1 = a .* abs( d1(:,2) + j*d1(:,3) );
+r2 = a .* abs( d2(:,2) + j*d2(:,3) );
+r3 = a .* abs( d3(:,2) + j*d3(:,3) );
+r4 = a .* abs( d4(:,2) + j*d4(:,3) );
+r5 = a .* abs( d5(:,2) + j*d5(:,3) );
+
+
+semilogy(f,r1,f,r2,f,r3,f,r4,f,r5);
+
+
+
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/TestESil/RunFreqs.m",".m","1930","78","function RunFreqs
+
+tic
+
+f = [200:5:1200];
+
+n = length(f);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  w0(i) = FrSingle( f(i), 7780, 0.005, 1.5e5, 7.e-5 );
+  w1(i) = FrSingle( f(i), 11340, 0.005, 1.5e5, 7.e-5 );
+  w2(i) = FrSingle( f(i), 7780, 0.003, 1.5e5, 7.e-5 );
+  w3(i) = FrSingle( f(i), 11340, 0.0053, 1.5e5, 7.e-5 );
+
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = strcat( 'Data/ESil7.dat' );
+Fout = [ f; real(w0); imag(w0); real(w1); imag(w1); real(w2); imag(w2); real(w3); imag(w3) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+clear
+
+f = [200:5:1200];
+
+n = length(f);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  w0(i) = FrSingle( f(i), 7780, 0.005, 1.4e5, 8.e-5 );
+  w1(i) = FrSingle( f(i), 11340, 0.005, 1.4e5, 8.e-5 );
+  w2(i) = FrSingle( f(i), 7780, 0.003, 1.4e5, 8.e-5 );
+  w3(i) = FrSingle( f(i), 11340, 0.0053, 1.4e5, 8.e-5 );
+
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = strcat( 'Data/ESil8.dat' );
+Fout = [ f; real(w0); imag(w0); real(w1); imag(w1); real(w2); imag(w2); real(w3); imag(w3) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+clear
+
+f = [200:5:1200];
+
+n = length(f);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  w0(i) = FrSingle( f(i), 7780, 0.005, 1.5e5, 10.e-5 );
+  w1(i) = FrSingle( f(i), 11340, 0.005, 1.5e5, 10.e-5 );
+  w2(i) = FrSingle( f(i), 7780, 0.003, 1.5e5, 10.e-5 );
+  w3(i) = FrSingle( f(i), 11340, 0.0053, 1.5e5, 10.e-5 );
+
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = strcat( 'Data/ESil10.dat' );
+Fout = [ f; real(w0); imag(w0); real(w1); imag(w1); real(w2); imag(w2); real(w3); imag(w3) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e %e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/TestESil/SymGeom.m",".m","1454","27","function [s] = SymmetricGeom( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/TestESil/SymEigs.m",".m","2422","94","function [freq] = SymEigs
+
+bSolve = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, 2*1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.499, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .0025 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+%appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+%equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+%equ.mixedform = {0,1,0};
+equ.dampingtype = {'nodamping'};
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind(SubInd) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/Free/TestESil/FrSingle.m",".m","2602","102","function [w] = FrSingle( freq, rhoStl, rStl, Esil, betak )
+
+bSolve = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ rStl, 0.00775, 0.023 ];
+E = [ 2.0696e11, Esil, 4.35e9 ];
+nu = [ 0.3, 0.49, 0.368 ];
+rho = [ rhoStl, 1300, 1180 ];
+h = [ .006, .0025, .006 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+
+equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+equ.mixedform = {0,1,0};
+
+equ.dampingtype = {'Rayleigh'};
+equ.betadK = { betak };
+equ.alphadM = { 0 };
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','p','v'}, 'outcomp',{'w','u','p','v'}, ...
+                    'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                    'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/FullCube/FullEigs.m",".m","2231","85","%function [freq] = FullEigs
+
+bSolve = 0;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+offStl = [ r(2)-1.1*r(1), 0, 0 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .005, .0025, .006 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = FullGeom( r(1), r(2), r(3), offStl );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+equ.mixedform = {0,1,0};
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/FullCube/RunFreqs.m",".m","435","25","function [freqs,w] = RunFreqs
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+freqs = [500];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = FullFreqs( freqs(i) );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqResp.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/FullCube/FullGeom.m",".m","635","17","function [s] = SymmetricGeom( rStl, rSil, rEpx, offStl )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',offStl, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Damped/FullCube/FullFreqs.m",".m","2632","106","function [w] = FullFreqs( freq )
+
+bSolve = 1;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+alpha = 0;
+beta = 2.25e-4;
+ESil = 1.175e5 * ( 1 + beta * j * 2*pi*freq );
+r = [ 0.005, 0.00775, 0.023 ];
+d = (r(2) - 1.1*r(1)) / sqrt(3);
+offStl = [ d, d, d ];
+E = [ 2.0696e11, ESil, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .01, .003, .01 ];
+
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = FullGeom( r(1), r(2), r(3), offStl );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+%fem.mesh=meshinit(fem, 'hauto',5, ...
+%                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+fem.mesh=meshinit(fem, 'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.Fz = {0,1000};
+bnd.ind = [1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+
+equ.dampingtype = 'nodamping';
+%equ.dampingtype = 'Rayleigh';
+%equ.betadK = { beta };
+%equ.alphadM = { alpha };
+
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Array/RunFreqs.m",".m","372","21","function [freqs,w] = RunFreqs( mScale )
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+for i = 1:length(freqs)
+	    w(i) = SymFreqs(freqs(i), mScale);
+end
+
+flbase = 'Data/FreqResp';
+flend = '.dat';
+flmid = num2str( mScale );
+flname = strcat( flbase, flmid, flend );
+Fout = [ freqs; w ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e\n', Fout );
+fclose( fl );
+
+toc
+exit
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Array/SymFreqs.m",".m","2413","93","function [w] = SymFreqs( freq, mScl )
+bSolve = 1;
+%freq = 1000;
+%bSolve = 0;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005*mScl, 0.00775*mScl, 0.023 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .003, .0012, .003 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymmetricGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,2,2];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Array/SymmetricGeom.m",".m","1454","27","function [s] = SymmetricGeom( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Free/RunFreqs.m",".m","827","43","function [freqs,w,w1] = RunFreqs
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqs(freqs(i), 1);
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqRespUMF.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+w1 = w;
+toc
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqs(freqs(i), 0);
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqRespSP.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+%exit
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Free/SymGeom.m",".m","1454","27","function [s] = SymmetricGeom( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx,rEpx, 'base','center', 'pos',[-rEpx/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx,rEpx/2,rEpx, 'base','center', 'pos',[0,-rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Free/SymEigs.m",".m","2459","97","function [freq] = SymEigs( bUMF )
+
+bSolve = 1;
+ESil = 1.175e5;
+ERes = 4.35e9;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, ESil, ERes ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .0025 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.Fz = {0,0};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+
+  if( bUMF )
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'neigs',2 );
+  else
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  end
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/Free/SymFreqs.m",".m","2567","100","function [w] = SymFreqs( freq, bUMF )
+
+bSolve = 1;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .003, .0012, .003 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  if( bUMF )
+    fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{});
+  else
+    fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  end
+
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/FullCube/FullEigs.m",".m","2231","85","%function [freq] = FullEigs
+
+bSolve = 0;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+offStl = [ r(2)-1.1*r(1), 0, 0 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .005, .0025, .006 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = FullGeom( r(1), r(2), r(3), offStl );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.shape = {'shlag(2,''u'')','shlag(2,''v'')','shlag(2,''w'')','shlag(1,''p'')'};
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.shape = {[1;2;3],[1;2;3;4],[1;2;3]};
+equ.mixedform = {0,1,0};
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+
+  fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/FullCube/RunFreqs.m",".m","827","43","function [freqs,w,w1] = RunFreqs
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqs(freqs(i), 1);
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqRespUMF.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+w1 = w;
+toc
+
+tic
+
+freqs = [100:10:300,301:500,510:10:1000];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqs(freqs(i), 0);
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqRespSP.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+%exit
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/FullCube/FullGeom.m",".m","635","17","function [s] = SymmetricGeom( rStl, rSil, rEpx, offStl )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',offStl, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/BoundaryConds/Undamped/FullCube/FullFreqs.m",".m","2384","92","%function [w] = FullFreqs( freq )
+
+bSolve = 0;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+offStl = [ 0, 0, 0 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .004, .002, .005 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = FullGeom( r(1), r(2), r(3), offStl );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.Fz = {0,1000};
+bnd.ind = [1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/AnalyticStacks/Layers.m",".m","922","53","function [f,w,tl,w0,tl0,ws,tls,D,M0] = Layers( n, h, d, eta, omega0 )
+
+P = 1;
+rc = 1.2 * 344 / 2;
+
+L = 1.0;
+A = L^2;
+F = P * A;
+D = n*d + (n-1)*h;
+
+f = 1:10:15000;
+omega = 2*pi*f;
+
+rho1 = 2700;
+rho2 = 1300;
+rhos = 7850;
+m1 = d*A * rho1;
+m2 = h*A * rho2;
+
+omega0 = 2*pi*ones(size(f)) * omega0;
+
+k = 1.175e5 * A/h * ( 1 + eta*i*omega0 );
+
+for j = 1:length(f)
+  m0 = 2 * k(j) - omega(j).^2 * m1;
+  k0 = -k(j);
+  K = diag( m0*ones(n,1) ) + diag( k0*ones(n-1,1), 1 ) + diag( k0*ones(n-1,1), -1 );
+  K(1,1) = m0 + k0;
+  K(n,n) = m0 + k0;
+  
+  b = zeros(n,1);
+  b(1) =  F;
+
+  w_temp = K\b;
+  w(j) = w_temp(n);
+end
+
+M0 = n * m1 + (n-1) * m2;
+w0 = -F ./ M0 ./ omega.^2;
+v0 = i*omega.*w0;
+z0 = P./v0;
+tl0 = 20 * log10( abs( 1 + z0/rc ) );
+
+v = i*omega.*w;
+z = P./v;
+tl = 20 * log10( abs( 1 + z/rc ) );
+
+Ms = rhos * A * ( n*d + (n-1)*h );
+ws = -F / Ms ./ omega.^2;
+vs = i*omega.*ws;
+zs = P./vs;
+tls = 20 * log10( abs( 1 + zs/rc ) );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/AnalyticStacks/Stacks.m",".m","1267","61","function [f,w,tl,w0,tl0,ws,tls,L] = Stacks( nRes, h, eta )
+
+rf = load('Rho_f.mat');
+rho = rf.rho1(1:1500);
+f = rf.f(1:1500);
+
+m1 = 0.0041;
+m2 = 0.0041;
+M0 = 0.0139;
+F = 1000*.023*.023;
+w0 = 400;
+
+%f = 1:0.1:2000;
+omega = 2*pi*f;
+
+k1 = 000*(1+eta*i*omega);
+k2 = 0000*(1+eta*i*omega);
+K2 = 1.175e5*.023^2/h*(1+eta*i*omega);
+
+%Meff = M0 + 2*k1*m1./(2*k1-m1*omega.^2) + 2*k2*m2./(2*k2-m2*omega.^2);
+Meff = 0.023^3 * rho;
+Meff = 0.023^3 * 1180 * ones(size(f));
+
+Msil = (nRes-1)*1300*.023^2*h;
+
+for j = 1:length(f)
+  m0 = 2*K2(j) - omega(j)*Meff(j);
+  k0 = -K2(j);
+  K = diag( m0*ones(nRes,1) ) + diag( k0*ones(nRes-1,1), 1 ) + diag( k0*ones(nRes-1,1), -1 );
+  K(1,1) = m0 + k0;
+  K(nRes,nRes) = m0 + k0;
+  
+  b = zeros(nRes,1);
+  b(1)           =  F;
+
+  w_temp = K\b;
+  w(j) = w_temp(nRes);
+end
+
+w0 = - F ./ (nRes*(M0+m1+m2)) ./ omega.^2;
+Mtot = Meff(1)*nRes + Msil+9*Meff(f);
+w0 = -F ./ Mtot ./ omega.^2;
+v0 = i*omega.*w0;
+z0 = 1000./v0;
+tl0 = 20 * log10( abs( 1 + .5/1.2/344 * z0 ) );
+
+
+v = i*omega.*w;
+z = 1000./v;
+tl = 20 * log10( abs( 1 + .5/1.2/344 * z ) );
+
+a = -omega.^2.*w;
+
+Ms = 19000 * .023*.023* ( nRes*.023 + (nRes-1)*h )
+ws = -F ./ Ms ./ omega.^2;
+vs = i*omega.*ws;
+zs = 1000./vs;
+tls = 20 * log10( abs( 1 + .5/1.2/344 * zs ) );
+
+L = nRes*.023 + (nRes-1)*h;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/RunFreqResp.m",".m","325","15","function [f,w_bnds,w_ctrs] = RunFreqResp( nRes )
+
+tic
+
+f = [100,200:10:240,350:2:404,405:415,416:2:464,465:475,476:2:500,510:10:700,750:50:900];
+for i = 1:length(f)
+  [ w_bnds(i,:), w_ctrs(i,:)] = FreqResp( f(i), nRes );
+end
+
+flname = strcat('Data/FR_',num2str(nRes),'.mat');
+save(flname,'f','w_bnds','w_ctrs','-mat');
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/Geom1.m",".m","1154","26","function [s,subs,bnds,outBnd,outCtr] = Geom1( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+subs = [1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-rEpx/2];
+outCtr = [0];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/Geom3.m",".m","2276","42","function [s,subs,bnds,outBnd,outCtr] = Geom3( rStl, rSil, rEpx )
+
+d = rEpx;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,d], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,d], 'axis',{'0','0','1'}, 'rot','0' );
+g1c = sphere3( rStl, 'pos',[0,0,-d], 'axis',{'0','0','1'}, 'rot','0' );
+g2c = sphere3( rSil, 'pos',[0,0,-d], 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,3*rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlc = g1c;
+gSilc = geomcomp( {g2c,g1c}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {g3,g2a,g2b,g2c}, 'ns',{'obj1','obj2','obj3','obj4'}, ...
+		  'sf','obj1-obj2-obj3-obj4', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,3*rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlc = geomcomp( {gStlc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilc = geomcomp( {gSilc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {gEpx, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gStlc,gSilc,gEpx};
+s.name={'Stla','Sila','Stlb','Silb','Stlc','Silc','Epx'};
+s.tags={'gStla','gSila','gStlb','gSilb','gStlc','gSilc','gEpx'};
+
+subs = [1,2,1,2,1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,3,2,2];
+outCtr = [-d,0,d];
+outBnd = outCtr - d/2;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/Geom4.m",".m","2847","49","function [s,subs,bnds,outBnd,outCtr] = Geom4( rStl, rSil, rEpx )
+
+d = rEpx;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1c = sphere3( rStl, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2c = sphere3( rSil, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1d = sphere3( rStl, 'pos',[0,0,-3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2d = sphere3( rSil, 'pos',[0,0,-3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,4*rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlc = g1c;
+gSilc = geomcomp( {g2c,g1c}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStld = g1d;
+gSild = geomcomp( {g2d,g1d}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {g3,g2a,g2b,g2c,g2d}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1-obj2-obj3-obj4-obj5', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,4*rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlc = geomcomp( {gStlc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilc = geomcomp( {gSilc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStld = geomcomp( {gStld,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSild = geomcomp( {gSild,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {gEpx, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gStlc,gSilc,gStld,gSild,gEpx};
+s.name={'Stla','Sila','Stlb','Silb','Stlc','Silc','Stld','Sild','Epx'};
+s.tags={'gStla','gSila','gStlb','gSilb','gStlc','gSilc','gStld','gSild','gEpx'};
+
+subs = [1,2,1,2,1,2,1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-2*d,-d,0,d];
+outCtr = outBnd + d/2;
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/FreqResp.m",".m","2816","110","function [w_bnds,w_ctrs] = FreqResp( freq, nRes )
+
+bSolve = 1;
+%freq = 400;
+%nRes = 4;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r =   [ 0.005, 0.008, 0.023 ];
+E =   [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu =  [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h =   [ .004, .0018, .004 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+switch nRes
+  case 2
+    [s,subs,bnds,outBnds,outCtrs] = Geom2( r(1), r(2), r(3) );
+  case 3
+    [s,subs,bnds,outBnds,outCtrs] = Geom3( r(1), r(2), r(3) );
+  case 4
+    [s,subs,bnds,outBnds,outCtrs] = Geom4( r(1), r(2), r(3) );
+  otherwise
+    [s,subs,bnds,outBnds,outCtrs] = Geom1( r(1), r(2), r(3) );
+end
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h(subs(i));
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  xc = zeros(size(outCtrs));
+  pb = [xb;xb;outBnds];
+  pc = [xc;xc;outCtrs];
+  w_bnds = postinterp( fem, 'w', pb );
+  w_ctrs = postinterp( fem, 'w', pc );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Epx/Geom2.m",".m","1734","35","function [s,subs,bnds,outBnd,outCtr] = Geom2( rStl, rSil, rEpx )
+
+d = rEpx;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,2*rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2a,g2b}, 'ns',{'obj1','obj2','obj3'}, 'sf','obj1-obj2-obj3', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,2*rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {gEpx, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gEpx};
+s.name={'Stla','Sila','Stlb','Silb','Epx'};
+s.tags={'gStla','gSila','gStlb','gSilb','gEpx'};
+
+subs = [1,2,1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-d,0];
+outCtr = outBnd + d/2;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoStack/RunFreqResp.m",".m","251","18","function [f,w] = RunFreqResp( nRes )
+
+tic
+
+input = load('Rho_f.mat');
+f = 1:1500;
+rho = input.rho;
+
+for i = 1:length(f)
+  w(i) = FreqResp( f(i), rho(i), nRes );
+end
+
+flname = strcat('Data/FR_',num2str(nRes),'.mat');
+save(flname,'f','w','-mat');
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoStack/Geom.m",".m","771","33","function [s,subs,bnds,outBnd] = Geom( rRes, rSpr, nRes )
+
+d = rRes+rSpr;
+
+% Geometry
+for i = 1:nRes
+  gRes{i} = block3( rRes/2,rRes/2,rRes, 'base','center', 'pos',[0,0,(i-1)*d], 'axis',{'0','0','1'}, 'rot','0' );
+  gResName{i} = strcat( 'Res', num2str(i) );
+  if( i < nRes )
+    gSpr{i} = block3( rRes/2,rRes/2,rSpr, 'base','center', 'pos',[0,0,(i-1/2)*d], 'axis',{'0','0','1'}, 'rot','0' );
+    gSprName{i} = strcat( 'Spr', num2str(i) );
+  end
+end
+
+s.objs = { gRes{:}, gSpr{:} };
+s.name = { gResName{:}, gSprName{:} };
+s.tags = { gResName{:}, gSprName{:} };
+
+subs = [ ones(1,nRes), 2*ones(1,nRes-1) ];
+
+nbnds = 5*(2*nRes-1)+1;
+for i = 1:(2*nRes-1)
+  bnd2(i) = 3*i;
+end
+bnd3  = 3*(2*nRes-1)+1;
+
+bnds = ones(1,nbnds);
+bnds(bnd2) = 2;
+bnds(bnd3) = 3;
+outBnd = [-rRes/2];
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoStack/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoStack/FreqResp.m",".m","2676","104","function [w] = FreqResp( freq, rho_in, nRes )
+
+bSolve = 1;
+%freq = 1;
+%rho_in = 1000;
+%nRes = 2;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r0  = [ 0.005, 0.008, 0.023 ];
+E0  = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu0 = [ 0.3, 0.469, 0.368 ];
+v   = [ 4/3*pi*r0(1)^3, 4/3*pi*(r0(2)^3-r0(1)^3), r0(3)^3-4/3*pi*r0(2)^3 ] / r0(3)^3;
+
+r   = [ 0.023, 0.005 ];
+E   = [ v(1)*E0(1)+v(2)*E0(2)+v(3)*E0(3), 1.175e5 ];
+nu  = [ v(1)*nu0(1)+v(2)*nu0(2)+v(3)*nu0(3), 0.469 ];
+rho = [ rho_in, 1300 ];
+h   = [ .01, 0.005 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( r(1), r(2), nRes );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h( subs( find(SubInd==i) ) );
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  pb = [xb;xb;outBnds];
+  w = postinterp( fem, 'w', pb );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/RunFreqResp.m",".m","303","20","function [f,w,rho] = RunFreqResp
+
+tic
+
+f = 1:1500;
+
+for i = 1:length(f)
+  [ w1(i),  rho1(i)  ] = FreqResp( f(i), 1, 1.e-5 );
+  [ w10(i), rho10(i) ] = FreqResp( f(i), 1, 1.e-4 );
+end
+
+w = [w1;w10];
+rho = [rho1;rho10];
+
+flname = 'Data/Damped.mat';
+save(flname,'f','w1','rho1','w10','rho10','-mat');
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/Geom1.m",".m","1133","25","function [s,subs,bnds,outBnd] = Geom1( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+subs = [1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-rEpx/2];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/FreqResp.m",".m","2580","102","function [w,rho_fr] = FreqResp( freq, mScale, eta )
+
+bSolve = 1;
+%freq = 400;
+%nRes = 4;
+
+eScale = 1 + i*2*pi*freq*eta;
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r =   [ mScale * 0.005, 0.008, 0.023 ];
+E =   [ 2.0696e11, 1.175e5*eScale, 4.35e9 ];
+nu =  [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h =   [ .004, .0018, .004 ];
+P =   1000;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom1( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h(subs(i));
+end
+clear i;
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,P};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  pb = [xb;xb;outBnds];
+  w = postinterp( fem, 'w', pb );
+  a = (2*pi*i*freq)^2 * w;
+  rho_fr = P / a / r(3);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/Scale/Geom1.m",".m","1133","25","function [s,subs,bnds,outBnd] = Geom1( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+subs = [1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-rEpx/2];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/Scale/RunResFreq.m",".m","284","17","function [fl,fc] = RunResFreq
+
+tic
+
+bScale = 0.1:0.1:1;
+sScale = bScale.^(1/3);
+flname = 'Data/Scale.mat';
+
+for i = 1:length(bScale)
+  fl(i) = ResFreq( bScale(i), bScale(i) );
+  fc(i)  = ResFreq( bScale(i), sScale(i) );
+  save( flname, 'fl','fc','bScale','sScale','-mat' );
+end
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Single/Scale/ResFreq.m",".m","2369","95","%function [freq] = ResFreq( bScale, sScale )
+
+bSolve = 0;
+bScale = 1;
+sScale = 1;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r =   [ bScale * 0.005, sScale * 0.008, sScale * 0.023 ];
+E =   [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu =  [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h =   [ sScale * .0018, sScale * .0018, sScale * .004 ];
+P =   1000;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom1( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h(subs(i));
+end
+clear i;
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','fixed'};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, 'linsolver','spooles' );
+  fem0=fem;
+
+  freq = fem.sol.lambda(1)/(-2*j*pi);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/RunFreqResp.m",".m","339","16","function [f,w_bnds,w_ctrs] = RunFreqResp( nRes )
+
+tic
+
+f = [100,200:10:240,350:2:404,405:415,416:2:464,465:475,476:2:500,510:10:700,750:50:900];
+f = 100:1000;
+for i = 1:length(f)
+  [ w_bnds(i,:), w_ctrs(i,:)] = FreqResp( f(i), nRes );
+end
+
+flname = strcat('Data/FR_',num2str(nRes),'.mat');
+save(flname,'f','w_bnds','w_ctrs','-mat');
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/Geom1.m",".m","1154","26","function [s,subs,bnds,outBnd,outCtr] = Geom1( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+subs = [1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-rEpx/2];
+outCtr = [0];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/Geom3.m",".m","2276","42","function [s,subs,bnds,outBnd,outCtr] = Geom3( rStl, rSil, rEpx )
+
+d = rEpx;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,d], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,d], 'axis',{'0','0','1'}, 'rot','0' );
+g1c = sphere3( rStl, 'pos',[0,0,-d], 'axis',{'0','0','1'}, 'rot','0' );
+g2c = sphere3( rSil, 'pos',[0,0,-d], 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,3*rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlc = g1c;
+gSilc = geomcomp( {g2c,g1c}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {g3,g2a,g2b,g2c}, 'ns',{'obj1','obj2','obj3','obj4'}, ...
+		  'sf','obj1-obj2-obj3-obj4', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,3*rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlc = geomcomp( {gStlc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilc = geomcomp( {gSilc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {gEpx, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gStlc,gSilc,gEpx};
+s.name={'Stla','Sila','Stlb','Silb','Stlc','Silc','Epx'};
+s.tags={'gStla','gSila','gStlb','gSilb','gStlc','gSilc','gEpx'};
+
+subs = [1,2,1,2,1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,3,2,2];
+outCtr = [-d,0,d];
+outBnd = outCtr - d/2;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/Geom4.m",".m","2847","49","function [s,subs,bnds,outBnd,outCtr] = Geom4( rStl, rSil, rEpx )
+
+d = rEpx;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1c = sphere3( rStl, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2c = sphere3( rSil, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1d = sphere3( rStl, 'pos',[0,0,-3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2d = sphere3( rSil, 'pos',[0,0,-3*d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,4*rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStlc = g1c;
+gSilc = geomcomp( {g2c,g1c}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gStld = g1d;
+gSild = geomcomp( {g2d,g1d}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {g3,g2a,g2b,g2c,g2d}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1-obj2-obj3-obj4-obj5', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,4*rEpx, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlc = geomcomp( {gStlc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilc = geomcomp( {gSilc,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStld = geomcomp( {gStld,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSild = geomcomp( {gSild,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpx  = geomcomp( {gEpx, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gStlc,gSilc,gStld,gSild,gEpx};
+s.name={'Stla','Sila','Stlb','Silb','Stlc','Silc','Stld','Sild','Epx'};
+s.tags={'gStla','gSila','gStlb','gSilb','gStlc','gSilc','gStld','gSild','gEpx'};
+
+subs = [1,2,1,2,1,2,1,2,3];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,1,1,3,2,2];
+outBnd = [-2*d,-d,0,d];
+outCtr = outBnd + d/2;
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/FreqResp.m",".m","2829","110","function [w_bnds,w_ctrs] = FreqResp( freq, nRes )
+
+bSolve = 1;
+%freq = 400;
+%nRes = 2;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r =   [ 0.005, 0.008, 0.023, 0.005 ];
+E =   [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu =  [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h =   [ .004, .0018, .004 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+switch nRes
+  case 2
+    [s,subs,bnds,outBnds,outCtrs] = Geom2( r(1), r(2), r(3), r(4) );
+  case 3
+    [s,subs,bnds,outBnds,outCtrs] = Geom3( r(1), r(2), r(3) );
+  case 4
+    [s,subs,bnds,outBnds,outCtrs] = Geom4( r(1), r(2), r(3) );
+  otherwise
+    [s,subs,bnds,outBnds,outCtrs] = Geom1( r(1), r(2), r(3) );
+end
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h(subs(i));
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  xc = zeros(size(outCtrs));
+  pb = [xb;xb;outBnds];
+  pc = [xc;xc;outCtrs];
+  w_bnds = postinterp( fem, 'w', pb );
+  w_ctrs = postinterp( fem, 'w', pc );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/Stacks_Sil/Geom2.m",".m","2339","42","function [s,subs,bnds,outBnd,outCtr] = Geom2( rStl, rSil, rEpx, rSpr )
+
+d = rEpx+rSpr;
+
+% Geometry
+g1a = sphere3( rStl, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2a = sphere3( rSil, 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g1b = sphere3( rStl, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g2b = sphere3( rSil, 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g3a = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',[0,0,d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g3b = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',[0,0,-d/2], 'axis',{'0','0','1'}, 'rot','0' );
+g4 = block3( rEpx,rEpx,rSpr, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+
+gStla = g1a;
+gSila = geomcomp( {g2a,g1a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpxa = g3a;
+gStlb = g1b;
+gSilb = geomcomp( {g2b,g1b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpxa = g3b;
+gEpxa = geomcomp( {g3a,g2a}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpxb = geomcomp( {g3b,g2b}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpx/2,rEpx/2,2*rEpx+rSpr, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStla = geomcomp( {gStla,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSila = geomcomp( {gSila,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStlb = geomcomp( {gStlb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSilb = geomcomp( {gSilb,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpxa  = geomcomp( {gEpxa, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gEpxb  = geomcomp( {gEpxb, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSpr = geomcomp( {g4, gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s;
+s.objs={gStla,gSila,gStlb,gSilb,gEpxa,gEpxb,gSpr};
+s.name={'Stla','Sila','Stlb','Silb','Epxa','Epxb','Spr'};
+s.tags={'gStla','gSila','gStlb','gSilb','gEpxa','gEpxb','Spr'};
+
+subs = [1,2,1,2,3,3,2];
+bnds = [2,2,1,2,2,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,2,2,1,1,1,3,2,2,2,2,2,2];
+outBnd = [-rEpx-rSpr/2,0];
+outCtr = [-d/2,d/2];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoDampStack/RunFreqResp.m",".m","322","21","function [f,w] = RunFreqResp( nRes )
+
+tic
+
+input = load('Rho_f.mat');
+f = 1:1500;
+rho = input.rho10;
+flname = strcat('Data/FR_',num2str(nRes),'.mat');
+
+for i = 1:length(f)
+  w(i) = FreqResp( f(i), rho(i), nRes, 1.e-4 );
+  if( mod(i,10) == 0 )
+    save(flname,'f','w','-mat');
+  end
+end
+
+save(flname,'f','w','-mat');
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoDampStack/Geom.m",".m","771","33","function [s,subs,bnds,outBnd] = Geom( rRes, rSpr, nRes )
+
+d = rRes+rSpr;
+
+% Geometry
+for i = 1:nRes
+  gRes{i} = block3( rRes/2,rRes/2,rRes, 'base','center', 'pos',[0,0,(i-1)*d], 'axis',{'0','0','1'}, 'rot','0' );
+  gResName{i} = strcat( 'Res', num2str(i) );
+  if( i < nRes )
+    gSpr{i} = block3( rRes/2,rRes/2,rSpr, 'base','center', 'pos',[0,0,(i-1/2)*d], 'axis',{'0','0','1'}, 'rot','0' );
+    gSprName{i} = strcat( 'Spr', num2str(i) );
+  end
+end
+
+s.objs = { gRes{:}, gSpr{:} };
+s.name = { gResName{:}, gSprName{:} };
+s.tags = { gResName{:}, gSprName{:} };
+
+subs = [ ones(1,nRes), 2*ones(1,nRes-1) ];
+
+nbnds = 5*(2*nRes-1)+1;
+for i = 1:(2*nRes-1)
+  bnd2(i) = 3*i;
+end
+bnd3  = 3*(2*nRes-1)+1;
+
+bnds = ones(1,nbnds);
+bnds(bnd2) = 2;
+bnds(bnd3) = 3;
+outBnd = [-rRes/2];
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoDampStack/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Homogenize/HomoDampStack/FreqResp.m",".m","2725","105","function [w] = FreqResp( freq, rho_in, nRes, eta )
+
+bSolve = 1;
+%freq = 1;
+%rho_in = 1000;
+%nRes = 2;
+eScale = 1 + i*2*pi*freq*eta;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r0  = [ 0.005, 0.008, 0.023 ];
+E0  = [ 2.0696e11, 1.175e5*eScale, 4.35e9 ];
+nu0 = [ 0.3, 0.469, 0.368 ];
+v   = [ 4/3*pi*r0(1)^3, 4/3*pi*(r0(2)^3-r0(1)^3), r0(3)^3-4/3*pi*r0(2)^3 ] / r0(3)^3;
+
+r   = [ 0.023, 0.005 ];
+E   = [ v(1)*E0(1)+v(2)*E0(2)+v(3)*E0(3), 1.175e5*eScale ];
+nu  = [ v(1)*nu0(1)+v(2)*nu0(2)+v(3)*nu0(3), 0.469 ];
+rho = [ rho_in, 1300 ];
+h   = [ .01, 0.005 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( r(1), r(2), nRes );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h( subs( find(SubInd==i) ) );
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  pb = [xb;xb;outBnds];
+  w = postinterp( fem, 'w', pb );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/SiliconData/DMTA/LoadData.m",".m","1899","121","D71 = load('Sil70-1.dat');
+D72 = load('Sil70-2.dat');
+D73 = load('Sil70-3.dat');
+D74 = load('Sil70-4.dat');
+
+D81 = load('Sil83-1.dat');
+D82 = load('Sil83-2.dat');
+D83 = load('Sil83-3.dat');
+D84 = load('Sil83-4.dat');
+
+D91 = load('Sil99-1.dat');
+D92 = load('Sil99-2.dat');
+D93 = load('Sil99-3.dat');
+D94 = load('Sil99-4.dat');
+
+f = D71(:,1);
+
+e71 = D71(:,2);
+e72 = D72(:,2);
+e73 = D73(:,2);
+e74 = D74(:,2);
+
+e81 = D81(:,2);
+e82 = D82(:,2);
+e83 = D83(:,2);
+e84 = D84(:,2);
+
+e91 = D91(:,2);
+e92 = D92(:,2);
+e93 = D93(:,2);
+e94 = D94(:,2);
+
+e7 = (e71+e72+e73+e74)/4;
+e8 = (e81+e82+e83+e84)/4;
+e9 = (e91+e92+e93+e94)/4;
+
+g71 = D71(:,3);
+g72 = D72(:,3);
+g73 = D73(:,3);
+g74 = D74(:,3);
+
+g81 = D81(:,3);
+g82 = D82(:,3);
+g83 = D83(:,3);
+g84 = D84(:,3);
+
+g91 = D91(:,3);
+g92 = D92(:,3);
+g93 = D93(:,3);
+g94 = D94(:,3);
+
+g7 = (g71+g72+g73+g74)/4;
+g8 = (g81+g82+g83+g84)/4;
+g9 = (g91+g92+g93+g94)/4;
+
+t71 = D71(:,4);
+t72 = D72(:,4);
+t73 = D73(:,4);
+t74 = D74(:,4);
+
+t81 = D81(:,4);
+t82 = D82(:,4);
+t83 = D83(:,4);
+t84 = D84(:,4);
+
+t91 = D91(:,4);
+t92 = D92(:,4);
+t93 = D93(:,4);
+t94 = D94(:,4);
+
+t7 = (t71+t72+t73+t74)/4;
+t8 = (t81+t82+t83+t84)/4;
+t9 = (t91+t92+t93+t94)/4;
+
+p71 = D71(:,5);
+p72 = D72(:,5);
+p73 = D73(:,5);
+p74 = D74(:,5);
+
+p81 = D81(:,5);
+p82 = D82(:,5);
+p83 = D83(:,5);
+p84 = D84(:,5);
+
+p91 = D91(:,5);
+p92 = D92(:,5);
+p93 = D93(:,5);
+p94 = D94(:,5);
+
+p7 = (p71+p72+p73+p74)/4;
+p8 = (p81+p82+p83+p84)/4;
+p9 = (p91+p92+p93+p94)/4;
+
+figure(1);
+subplot(2,1,1);
+plot(f,e7,f,e8,f,e9);
+subplot(2,1,2);
+plot(f,g7,f,g8,f,g9);
+
+figure(2);
+subplot(3,1,1);
+plot(f,e71,f,e72,f,e73,f,e74);
+subplot(3,1,2);
+plot(f,e81,f,e82,f,e83,f,e84);
+subplot(3,1,3);
+plot(f,e91,f,e92,f,e93,f,e94);
+
+figure(3);
+subplot(3,1,1);
+plot(f,g71,f,g72,f,g73,f,g74);
+subplot(3,1,2);
+plot(f,g81,f,g82,f,g83,f,g84);
+subplot(3,1,3);
+plot(f,g91,f,g92,f,g93,f,g94);
+
+figure(4);
+plot(f,t7,f,t8,f,t9);
+
+figure(5);
+plot(f,p7,f,p8,f,p9);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/SiliconData/DMTA/FitLine.m",".m","323","21","function [yf, yff] = FitLine( ff )
+
+D1 = load('Sil83-1.dat');
+D2 = load('Sil83-2.dat');
+D3 = load('Sil83-3.dat');
+D4 = load('Sil83-4.dat');
+
+f = D1(:,1);
+e1 = D1(:,2);
+e2 = D2(:,2);
+e3 = D3(:,2);
+e4 = D4(:,2);
+e = (e1+e2+e3+e4)/4;
+
+[x,y] = polyfit(log(f),log(e),5);
+
+fl = log( ff );
+
+yff = polyval( x, fl );
+yf = exp(yff);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/Xavier/loadh.m",".m","288","14","function [h,x,f]=loadh(fnum)
+
+x = [-256 -186 -136 136 186 256].' *1e-3;
+
+dpath = 'Z:\xavierbremaud On My Mac\Stage NZ\mesure-tube\';
+
+load([dpath 'trac' int2str(fnum(1)) '.mat']);
+f = o2i1x;
+h = o2i1;
+
+for n=2:length(fnum)
+	load([dpath 'trac' int2str(fnum(n)) '.mat']);
+	h = [h,o2i1];
+end","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/Xavier/model.m",".m","399","22","clc, clear all, close all
+w=0:1:10000;
+s=size(w);
+m2(1,1:s(1,2))=0.300;
+m1(1,1:s(1,2))=0.400;
+
+c2(1,1:s(1,2))=0.3;
+k2(1,1:s(1,2))=200000;
+k2=k2+i.*c2.*sqrt(k2.*m2).*w;
+
+c1(1,1:s(1,2))=0.01;
+k1(1,1:s(1,2))=200000;
+k1=k1+i.*c1.*sqrt(k1.*m1).*w;
+
+
+
+Z=(-w.*w.*m2+k2)./((k2-m2.*w.*w).*(k1+k2-m1.*w.*w)-k2.*k2);
+rho=1.2;
+c=344;
+t=(2*Z*i.*w*rho*c)./(1+2*Z*i.*w*rho*c);
+T=20*log10(1./t);
+plot(w./(2*pi()),T)","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/Xavier/transcoeff10.m",".m","3687","151","function [t12]=transcoeff10(fnum,d)
+
+[h1,x,f]=loadh(fnum)
+% NB x are the microphone locations with no sample
+
+% [A1,B1,C1,D1,A2,B2,C2,D2]=transcoeff5(h1,h2,x,d,f)
+
+% [t12,t21,r11,r22]=transcoeff5(h1,h2,x,d,f)
+
+% h1,h2 are the same size and each contain 5 column vectors of transfer
+% functions measured at 5 microphone locations for each of two different tube
+% terminations. Vector x contains the 6 microphone locations and the sample
+% is assumed to lie between x=0 and x=d;
+
+
+
+
+tc = 18;									% ambient temperature (degrees Celsius)
+pa = 101.4;								% barometric pressure (kPa)
+[c0,rho]=rhoc(tc,pa);
+
+idx = find(f==0.);
+f(idx) = ones(size(idx))*NaN;
+
+x([4 5 6]) = x([4 5 6])+d;			% adjust mic locations for thickness of sample
+
+% Acoustic wavenumber with estimated attenuation in air
+diam = 0.083;
+k = 2*pi*f/c0 - i*1.94e-2*sqrt(f)/(c0*diam);
+
+
+% -------------------------------------------------------------------------
+% Wave amplitudes at x=0, 'anechoic' termination
+
+h1 = [ones(size(h1,1),1) h1];
+%h2 = [ones(size(h2,1),1) h2];
+
+[A1,B1] = waveamp3(h1(:,[1 2 3]),x([1 2 3].'),k);		% source side of sample
+[C1,D1] = waveamp3(h1(:,[4 5 6]),x([4 5 6].'),k);		% receiver side of sample
+
+% Wave amplitudes at x=0, 'rigid' termination
+%[A2,B2] = waveamp3(h2(:,[1 2 3]),x([1 2 3].'),k);		% source side of sample
+%[C2,D2] = waveamp3(h2(:,[4 5 6]),x([4 5 6].'),k);		% receiver side of sample
+
+% C1=A1;D1=B1;
+% C2=A2;D2=B2;
+% eps = .1;
+% C1 = A1.*(1+randc(size(A1))*eps);  D1 = B1.*(1+randc(size(B1))*eps); 
+% C2 = A2.*(1+randc(size(A2))*eps);  D2 = B2.*(1+randc(size(B2))*eps); 
+
+
+
+% -------------------------------------------------------------------------
+% Transmission coefficients
+
+% Cd,Dd amplitudes on receiver side of sample at x=d
+Cd1 = C1.*exp(-i*k*d);
+Dd1 = D1.*exp(i*k*d);
+
+%Cd2 = C2.*exp(-i*k*d);
+%Dd2 = D2.*exp(i*k*d);
+
+% t = zeros(length(f),1);
+t12 = zeros(length(f),1);
+% t21 = zeros(length(f),1);
+% r11 = zeros(length(f),1);
+% r22 = zeros(length(f),1);
+
+z=zeros(size(f));
+for nf=1:length(f)
+%	BB = [B1(nf) B2(nf); Cd1(nf) Cd2(nf)];
+%	AA = [A1(nf) A2(nf); Dd1(nf) Dd2(nf)];
+% 	T = lsq_symmetric(AA,BB);
+%	T = BB / AA;
+T = B1(nf) / A1(nf);
+
+% 	r11(nf) = T(1,1);
+	t12(nf) = T(1,1);
+% 	t21(nf) = T(2,1);
+% 	r22(nf) = T(2,2);
+end
+
+
+
+
+
+
+
+
+
+
+
+
+
+% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+function [A,B]=waveamp3(p,x,k)
+% Estimate 2 least squares best fit wave amplitudes from pressure
+% measurements taken at 3 locations
+
+% p is a n X 3 matrix of pressure spectra at 3 locations, x is a 3 X 1
+% vector of locations and k is the wavenumber
+
+amp = zeros(2,size(p,1));
+for n=1:size(p,1)
+	amp(:,n) = [exp(-i*k(n)*x), exp(i*k(n)*x)] \ p(n,:).';
+end
+A = amp(1,:).';
+B = amp(2,:).';
+
+
+
+% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+function [c,rho]=rhoc(tc,pa)
+% Estimate speed of sound and air density at given temperature tc in degC
+% and atmospheric pressure pa (kPa)
+
+t0 = 293;								% standard temperature ( ~ 20 degC)
+tk = 273.15 + tc;						% temperature in degrees Kelvin
+
+c = 343.2  * sqrt(tk/t0);
+
+rho0 = 1.186;							% kg/m^3
+p0 = 101.325;							% kPa
+rho = rho0 * pa*t0/(p0*tk);
+
+
+% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+% function X=lsq_symmetric(A,B)
+% % Least squares best fit symmetric X that satisfies B = XA
+% 
+% % Initial estimate of X
+% X = B/A;
+% X = (X+X.')/2;
+% 
+% S = B - X*A;
+% R = S*A.' + A*S.';
+% P = R;
+% 
+% n = 0;
+% 
+% while ((norm(B-X*A,'fro') > eps) & (n < 1000))
+% 	n = n+1;
+% 	alfa = trace(R*P) / trace(A.'*P*P*A)/2;
+% 	X = X + alfa*P;
+% 
+% 	R = R - alfa*(P*A*A.' + A*A.'*P);
+% 	beta = trace(A.'*P*R*A) / trace(A.'*P*P*A)/2;
+% 	P = R - beta*P;
+% end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/Xavier/BryanMethod.m",".m","2537","69","cd /media/Storage/'Imaging and Detecting Platform'/'Project Files'/FRST/'Acoustically Efficient Buildings'/'Xavier Bremaud''s data and report'/mesure-tube/
+
+a='trac501';
+b='trac536';
+c='trac537';
+d='trac538';
+e='trac539';
+g='trac540';
+ep=0.01; %epaisseur de l'échantillon testé'
+
+%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+load(a)
+HP1R=o2i1;HP1R=zeros(801,1);HP1R(:,:)=1;
+load(b)
+HP2R=o2i1;
+load(e)
+HP3R=o2i1;
+load(g)
+HP4R=o2i1;
+f=o2i1x;
+
+cd ~/Matlab/Xavier/
+
+k=2*pi().*f/cs;
+
+x1=-0.257;
+x2=-0.187;
+x3=-x2+ep;
+x4=-x1+ep;
+
+rho=1.2;
+
+%==============TRANSFERT FUNCTIONS==========================
+%figure, plot(f,HP1R,'r'), hold on, plot(f,HP2R,'g'), hold on, plot(f,HP3R,'b'), hold on, plot(f,HP4R,'c'), hold on, grid on,
+%figure,subplot(2,2,1),plot(f,-20*log10(HP1R),'r'), title('transfert function (in dB) 1(red),2(green),3(blue),4(cyan)'),hold on,plot(f,-20*log10(HP2R),'g'),hold on,plot(f,-20*log10(HP3R),'b'),hold on,plot(f,-20*log10(HP4R),'c'),hold on,grid on
+
+%==============AMPLITUDES===================================
+A=i*(HP1R.*exp(i.*k.*x2)-HP2R.*exp(i.*k.*x1))./(2*sin(k.*(x1-x2)));
+B=i*(HP2R.*exp(-i.*k.*x1)-HP1R.*exp(-i.*k.*x2))./(2*sin(k.*(x1-x2)));
+C=i*(HP3R.*exp(i.*k.*x4)-HP4R.*exp(i.*k.*x3))./(2*sin(k.*(x3-x4)));
+D=i*(HP4R.*exp(-i.*k.*x3)-HP3R.*exp(-i.*k.*x4))./(2*sin(k.*(x3-x4)));
+%D=0;
+%subplot(2,2,2),plot(f,A,'r'), title('Amplitudes A(red),B(green),C(blue),D(cyan)'), hold on,plot(f,B,'g'), hold on,plot(f,C,'b'), hold on,plot(f,D,'c'), hold on,grid on
+%subplot(2,2,3),plot(f,(A-C)./A),title('% of difference between A & C'),grid on,axis([0 1600 -5 100]),
+
+%==============Pression & Velocity==========================
+
+p0=A+B; 
+v0=(A-B)/(rho*cs);
+pd=C.*exp(-i.*k.*ep)+D.*exp(i.*k.*ep);
+vd=(C.*exp(-i.*k.*ep)-D.*exp(i.*k.*ep))/(rho*cs);
+%subplot(2,2,4),plot(f,(pd-p0)./pd),title('% of difference between p0 & pd'),grid on,axis([0 1600 -5 100]),
+
+%==============Transfert matrix=============================
+T11=(pd.*vd+p0.*v0)./(p0.*vd+pd.*v0);
+T12=p0.*p0-pd.*pd;
+T21=v0.*v0-vd.*vd;
+T22=T11;
+%subplot(2,2,4), plot(f,T11,'b'),grid on, hold on, axis([0 1600 -5 5]),title('T12 in red & T11 in blue'),plot(f,T12,'r'),hold on, 
+
+%==============Transmission loss coefficient=============================
+TL=10*log10((1/4)*abs(T11+T12/(rho*cs)+rho*cs*T21+T22).*abs(T11+T12/(rho*cs)+rho*cs*T21+T22));
+%subplot(2,2,3),semilogx(f,TL),title('TL (in dB) in function of the frequency'),grid on
+
+T=(B.*D-A.*C)./(D.*D-A.*A);
+R=(A.*B-C.*D)./(A.*A-D.*D);
+lim=-0.5:0.01:1.5;
+IA1=10*log(1./abs(T));
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Misc/Xavier/power_spectrum.m",".m","245","10","clc, clear all, close all
+load trac627
+AA=20*log10(c2);
+load trac628
+BB=20*log10(c2);
+load trac637
+CC=20*log10(c2);
+load trac638
+DD=20*log10(c2);
+plot(c2x,AA,'b'),hold on,plot(c2x,BB,'r'),hold on,plot(c2x,CC,'g'),hold on,plot(c2x,DD,'c'),grid on","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Evanescent/RunFreqResp.m",".m","186","15","function [f,w] = RunFreqResp
+
+tic
+
+f = 100:2:1500;
+
+for i = 1:length(f)
+  w = FreqResp( f(i) );
+  flname = strcat('Data/FR_',num2str(i),'.mat');
+  save(flname,'f','w','-mat');
+end
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Evanescent/Geom1.m",".m","647","18","function [s,subs] = Geom1( rStl, rSil, rEpx )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpx,rEpx,rEpx, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+subs = [1,2,3];
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/Evanescent/FreqResp.m",".m","4314","160","function [w] = FreqResp( freq )
+
+bSolve = 1;
+%freq = 400;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r =   [ 0.005, 0.008, 0.023 ];
+E =   [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu =  [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [.005, .0025, .005 ];
+P =   1000;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs] = Geom1( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h(subs(i));
+end
+clear i;
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.Fz = {0,1000};
+bnd.ind = [1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+% Coupling variable elements
+clear elemcpl
+% Extrusion coupling variables
+clear elem
+elem.elem = 'elcplextr';
+elem.g = {'1'};
+src = cell(1,1);
+clear bnd
+bnd.expr = {{{},{},'u'},{{},{},'v'},{{},{},'w'},{'u',{},{}},{'v',{},{}},{'w', ...
+  {},{}}};
+bnd.map = {{'1','1','1'},{'1','1','1'},{'1','1','1'},{'1','1','1'},{'1','1', ...
+  '1'},{'1','1','1'}};
+bnd.ind = {{'1'},{'2','3','4','6','7','8','9','10','11','12','13','14', ...
+  '15','16','17','18','19','20','21','22'},{'5'}};
+src{1} = {{},{},bnd,{}};
+elem.src = src;
+geomdim = cell(1,1);
+clear bnd
+bnd.map = {{{},'2',{}},{{},'2',{}},{{},'2',{}},{{},{},'3'},{{},{},'3'}, ...
+  {{},{},'3'}};
+bnd.ind = {{'1','3','4','5','6','7','8','9','10','11','12','13','14', ...
+  '15','16','17','18','19','20','21'},{'2'},{'22'}};
+geomdim{1} = {{},{},bnd,{}};
+elem.geomdim = geomdim;
+elem.var = {'pconstr1','pconstr2','pconstr3','pconstr4','pconstr5', ...
+  'pconstr6'};
+map = cell(1,3);
+clear submap
+submap.type = 'unit';
+map{1} = submap;
+clear submap
+submap.type = 'linear';
+submap.sg = '1';
+submap.sv = {'2','18','17','1'};
+submap.dg = '1';
+submap.dv = {'4','20','19','3'};
+map{2} = submap;
+clear submap
+submap.type = 'linear';
+submap.sg = '1';
+submap.sv = {'20','19','17','18'};
+submap.dg = '1';
+submap.dv = {'4','3','1','2'};
+map{3} = submap;
+elem.map = map;
+elemcpl{1} = elem;
+% Point constraint variables (used for periodic conditions)
+clear elem
+elem.elem = 'elpconstr';
+elem.g = {'1'};
+clear bnd
+bnd.constr = {{'pconstr1-(u)','pconstr2-(v)','pconstr3-(w)','0','0','0'},{'0', ...
+  '0','0','pconstr4-(u)','pconstr5-(v)','pconstr6-(w)'}};
+bnd.cpoints = {{'2','2','2','2','2','2'},{'2','2','2','2','2','2'}};
+bnd.ind = {{'2'},{'22'}};
+elem.geomdim = {{{},{},bnd,{}}};
+elemcpl{2} = elem;
+fem.elemcpl = elemcpl;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+
+  rEpx = r(3);
+  dx = rEpx/200;
+  [x,y] = meshgrid(-rEpx/2:dx:rEpx/2,-rEpx/2:dx:rEpx/2);
+  z = -rEpx/2 * ones(size(x));
+  p = [x(:)'; y(:)';z(:)'];  %'
+  w = postinterp(fem, 'w', p);
+  w = reshape(w, size(x));
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/GetGeoms.m",".m","294","6","function [gStl,gSil] = GetGeoms(x,y,rStl,rSil)
+
+gStl = sphere3( rStl, 'pos',[x,y,0], 'axis',{'0','0','1'}, 'rot','0' );
+gTmp = sphere3( rSil, 'pos',[x,y,0], 'axis',{'0','0','1'}, 'rot','0' ); 
+gSil = geomcomp( {gTmp,gStl}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/RunFreqs.m",".m","478","26","function [freqs,w,wi] = RunFreqs
+
+tic
+
+freqs = [10,20:20:2200];
+%freqs = 100:20:120;
+%freqs = 10;
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi) ];
+fl = fopen( 'Data/FRFine.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/TestPlot.m",".m","1299","49","function [x,y] = GetCenters( panel_len, coating_dia )
+
+panel_len = 12.7e-2;
+coating_dia = 15.0e-3;
+
+num_rows = 9;
+num_rows_1 = 5;
+num_rows_2 = num_rows_1 - 1;
+num_cols = 8;
+num_cols_1 = 8;
+num_cols_2 = num_cols_1 - 1;
+row_start_dist_1 = 8.0e-3;
+row_start_dist_2 = row_start_dist_1 + 1.05 * coating_dia * sin(pi/3)
+col_start_dist_1 = 8.0e-3;
+col_start_dist_2 = col_start_dist_1 + coating_dia * sin(pi/6)
+
+row_width_1 = panel_len - 2 * row_start_dist_1;
+row_width_2 = panel_len - 2 * row_start_dist_2;
+col_width_1 = panel_len - 2 * col_start_dist_1;
+col_width_2 = panel_len - 2 * col_start_dist_2;
+row_inc_1 = row_width_1 / ( num_rows_1 - 1 );
+row_inc_2 = row_width_2 / ( num_rows_2 - 1 );
+col_inc_1 = col_width_1 / ( num_cols_1 - 1 );
+col_inc_2 = col_width_2 / ( num_cols_2 - 1 );
+
+
+a = 1.05*coating_dia*sin(pi/3)
+b = coating_dia*sin(pi/6)
+c = row_inc_1 / 2
+d = row_inc_2 / 2
+
+k = 0;
+for jj =  1:num_cols_1
+  for ii = 1:num_rows_1
+    k = k + 1;
+    x(k) = col_start_dist_1 + (jj-1) * col_inc_1 - panel_len/2;
+    y(k) = row_start_dist_1 + (ii-1) * row_inc_1 - panel_len/2;
+   end
+end
+for jj = 1:num_cols_2
+  for ii = 1:num_rows_2
+    k = k + 1;
+    x(k) = col_start_dist_2 + (jj-1) * col_inc_2 - panel_len/2;
+    y(k) = row_start_dist_2 + (ii-1) * row_inc_2 - panel_len/2;
+  end
+end
+
+plot(x,y,'o');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/SymGeom.m",".m","6707","136","function [s,Subs] = SymGeom
+
+rStl = 5.e-3;
+rSil = 7.e-3;
+rEpx = 12.7e-2;
+rFor = 4.45e-2;
+tEpx = 1.9e-2;
+
+rOut = 0.1085/2;
+rIn = 0.1015/2;
+rTh = 0.0035;
+
+[x,y,xb,yb] = GetCenters( rEpx, 2*rSil );
+rEpx = 0.13;
+
+gEpx = block3( rEpx,rEpx,tEpx, 'base','center', 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+
+gRng1 = cylinder3( rOut,rTh, 'pos',[0,0,tEpx/2],   'axis',{'0','0','1'}, 'rot','0' );
+gRng2 = cylinder3( rOut,rTh, 'pos',[0,0,-rTh-tEpx/2],  'axis',{'0','0','1'}, 'rot','0' );
+gTmp1 = cylinder3( rIn, rTh, 'pos',[0,0,tEpx/2],  'axis',{'0','0','1'}, 'rot','0' );
+gTmp2 = cylinder3( rIn, rTh, 'pos',[0,0,-rTh-tEpx/2], 'axis',{'0','0','1'}, 'rot','0' );
+gRng1 = geomcomp( {gRng1,gTmp1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRng2 = geomcomp( {gRng2,gTmp2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+[gStl1, gSil1] =  GetGeoms( x(1),  y(1),  rStl, rSil );
+[gStl2, gSil2] =  GetGeoms( x(2),  y(2),  rStl, rSil );
+[gStl3, gSil3] =  GetGeoms( x(3),  y(3),  rStl, rSil );
+[gStl4, gSil4] =  GetGeoms( x(4),  y(4),  rStl, rSil );
+[gStl5, gSil5] =  GetGeoms( x(5),  y(5),  rStl, rSil );
+[gStl6, gSil6] =  GetGeoms( x(6),  y(6),  rStl, rSil );
+[gStl7, gSil7] =  GetGeoms( x(7),  y(7),  rStl, rSil );
+[gStl8, gSil8] =  GetGeoms( x(8),  y(8),  rStl, rSil );
+[gStl9, gSil9] =  GetGeoms( x(9),  y(9),  rStl, rSil );
+[gStl10,gSil10] = GetGeoms( x(10), y(10), rStl, rSil );
+[gStl11,gSil11] = GetGeoms( x(11), y(11), rStl, rSil );
+[gStl12,gSil12] = GetGeoms( x(12), y(12), rStl, rSil );
+[gStl13,gSil13] = GetGeoms( x(13), y(13), rStl, rSil );
+[gStl14,gSil14] = GetGeoms( x(14), y(14), rStl, rSil );
+[gStl15,gSil15] = GetGeoms( xb(1), yb(1), rStl, rSil );
+[gStl16,gSil16] = GetGeoms( xb(2), yb(2), rStl, rSil );
+[gStl17,gSil17] = GetGeoms( xb(3), yb(3), rStl, rSil );
+[gStl18,gSil18] = GetGeoms( xb(4), yb(4), rStl, rSil );
+[gStl19,gSil19] = GetGeoms( xb(5), yb(5), rStl, rSil );
+[gStl20,gSil20] = GetGeoms( xb(6), yb(6), rStl, rSil );
+
+gTmp = block3( rEpx/2,rEpx/2,tEpx+2*rTh, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRng1 = geomcomp( {gRng1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRng2 = geomcomp( {gRng2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+gStl15 = geomcomp( {gStl15,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl16 = geomcomp( {gStl16,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl17 = geomcomp( {gStl17,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl18 = geomcomp( {gStl18,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl19 = geomcomp( {gStl19,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl20 = geomcomp( {gStl20,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil15 = geomcomp( {gSil15,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil16 = geomcomp( {gSil16,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil17 = geomcomp( {gSil17,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil18 = geomcomp( {gSil18,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil19 = geomcomp( {gSil19,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil20 = geomcomp( {gSil20,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+
+gTmp = geomcomp( {gSil1,gStl1,gSil2,gStl2}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil3,gStl3,gSil4,gStl4}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil5,gStl5,gSil6,gStl6}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil7,gStl7,gSil8,gStl8}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil9,gStl9,gSil10,gStl10}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil11,gStl11,gSil12,gStl12}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil13,gStl13,gSil14,gStl14}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil15,gStl15,gSil16,gStl16}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil17,gStl17,gSil18,gStl18}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil19,gStl19,gSil20,gStl20}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+
+% Analyzed geometry
+clear s
+s.objs={gEpx,gRng1,gRng2,...
+	gStl1,gStl2,gStl3,gStl4,gStl5,gStl6,gStl7,gStl8,gStl9,gStl10,...
+	gStl11,gStl12,gStl13,gStl14,gStl15,gStl16,gStl17,gStl18,gStl19,gStl20,...
+	gSil1,gSil2,gSil3,gSil4,gSil5,gSil6,gSil7,gSil8,gSil9,gSil10,...
+	gSil11,gSil12,gSil13,gSil14,gSil15,gSil16,gSil17,gSil18,gSil19,gSil20};
+
+sEpx = 1;
+sRng = [4,4];
+sStl = 3*ones(1,20);
+sSil = 2*ones(1,20);
+Subs = [sEpx,sRng,sStl,sSil];
+
+
+
+
+%=========================================================
+%  Temporary
+
+%fem.draw=struct('s',s);
+%[g,st] = geomcsg(fem);
+%[SubInd,s0] = find(st);   
+%fem.geom = geomcsg(fem);
+%size(SubInd)
+
+
+% (Default values are not included)
+% Application mode 1
+%clear appl
+%appl.mode.class = 'SmeSolid3';
+%appl.module = 'SME';
+%appl.gporder = 4;
+%appl.cporder = 2;
+%appl.assignsuffix = '_smsld';
+
+%fem.appl{1} = appl;
+%fem.frame = {'ref'};
+%fem.border = 1;
+%clear units;
+%units.basesystem = 'SI';
+%fem.units = units;
+
+% Multiphysics
+%fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Test.m",".m","1485","61","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=sphere3('1','pos',{'-2','-2','0'},'axis',{'0','0','1'},'rot','0');
+g2=sphere3('1','pos',{'2','2','0'},'axis',{'0','0','1'},'rot','0');
+g3=geomcomp({g1,g2},'ns',{'SPH1','SPH2'},'sf','SPH1+SPH2','face','none','edge','all');
+g4=sphere3('1','pos',{'-2','2','0'},'axis',{'0','0','1'},'rot','0');
+g5=geomcomp({g3,g4},'ns',{'CO1','SPH1'},'sf','SPH1+CO1','face','none','edge','all');
+g6=sphere3('0.5','pos',{'1','-1','0'},'axis',{'0','0','1'},'rot','0');
+g7=sphere3('0.5','pos',{'-4','-4','0'},'axis',{'0','0','1'},'rot','0');
+
+% Analyzed geometry
+clear s
+s.objs={g5,g6,g7};
+s.name={'CO2','SPH4','SPH5'};
+s.tags={'g5','g6','g7'};
+
+fem.draw=struct('s',s);
+[g,st] = geomcsg(fem);
+[SubInd,s0] = find(st)    
+fem.geom = geomcsg(fem);
+
+
+% (Default values are not included)
+a = 2
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+
+clear equ
+equ.E = { 2.0e11, 1.0e11, 4.e5 };
+equ.ind(SubInd) = s0;
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/SymFreqs.m",".m","3337","117","function [w,wi] = SymFreqs( freq )
+
+bSolve = 1;
+%freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+rStl = 5.e-3;
+rSil = 7.e-3;
+rEpx = 12.7e-2;
+rFor = 4.45e-2;
+tEpx = 1.9e-2;
+
+rOut = 0.1085/2;
+rIn = 0.1015/2;
+rTh = 0.0035;
+E = [ 4.35e9, 1.175e5, 2.0696e11, 1.0e6 ];
+nu = [ 0.368, 0.469, 0.3, 0.45 ];
+rho = [ 1180, 1300, 7780, 1200 ];
+h = [ .1, .0035, .02, 0.003 ];
+%h = [ .015, .002, .005, 0.003 ];
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,Subs] = SymGeom;
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(Subs)
+  dh(2*i-1) = SubInd(i);
+  dh(2*i)   = h( Subs(i) );
+end
+
+fem.mesh=meshinit(fem, 'hmaxsub',dh );
+%fem.mesh = meshinit( fem, 'hauto', 6 );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','fixed','sym','free'};
+bnd.Fz = {0,0,0,1000};
+bnd.ind = [3,3,1,4,3,1,1,3,1,1,1,1,1,1,3,1,1,3,1,1,1,1,1,1,3,1,2,1,3,1, ...
+  1,2,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,3,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3), rho(4) };
+equ.nu = { nu(1), nu(2), nu(3), nu(4) };
+equ.E = { E(1), E(2), E(3), E(4) };
+equ.ind( SubInd ) = Subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+
+  fem0=fem;
+  p = [0;0;-(1.9e-2)/2];
+  w = postinterp(fem, 'w', p);
+  A = pi*rIn^2/4;
+  wi = 1/A * postint(fem,'w', 'unit','m^3', 'dl',[3], 'edim',2);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/GetCenters.m",".m","1564","61","function [x,y,xb,yb] = GetCenters( panel_len, coating_dia )
+
+num_rows = 9;
+num_rows_1 = 5;
+num_rows_2 = num_rows_1 - 1;
+num_cols = 8;
+num_cols_1 = 8;
+num_cols_2 = num_cols_1 - 1;
+row_start_dist_1 = 8.0e-3;
+row_start_dist_2 = row_start_dist_1 + 1.05 * coating_dia * sin(pi/3);
+col_start_dist_1 = 8.0e-3;
+col_start_dist_2 = col_start_dist_1 + coating_dia * sin(pi/6);
+
+row_width_1 = panel_len - 2 * row_start_dist_1;
+row_width_2 = panel_len - 2 * row_start_dist_2;
+col_width_1 = panel_len - 2 * col_start_dist_1;
+col_width_2 = panel_len - 2 * col_start_dist_2;
+row_inc_1 = row_width_1 / ( num_rows_1 - 1 );
+row_inc_2 = row_width_2 / ( num_rows_2 - 1 );
+col_inc_1 = col_width_1 / ( num_cols_1 - 1 );
+col_inc_2 = col_width_2 / ( num_cols_2 - 1 );
+
+k = 0;
+kb = 0;
+r0 = coating_dia/2;
+for jj =  1:num_cols_1
+  for ii = 1:num_rows_1
+    x0 = col_start_dist_1 + (jj-1) * col_inc_1 - panel_len/2;
+    y0 = row_start_dist_1 + (ii-1) * row_inc_1 - panel_len/2;
+    if(( x0 > 0 ) & ( y0 > 0 ))
+      k = k + 1;
+      x(k) = x0;
+      y(k) = y0;
+    else 
+      if(( x0 > -r0 ) & ( y0 > -r0 ))
+        kb = kb + 1;
+        xb(kb) = x0;
+        yb(kb) = y0;
+      end
+    end
+  end
+end
+
+for jj = 1:num_cols_2
+  for ii = 1:num_rows_2
+    x0 = col_start_dist_2 + (jj-1) * col_inc_2 - panel_len/2;
+    y0 = row_start_dist_2 + (ii-1) * row_inc_2 - panel_len/2;
+    if(( x0 > 0 ) & ( y0 > 0 ))
+      k = k + 1;
+      x(k) = x0;
+      y(k) = y0;
+    else
+      if(( x0 > -r0 ) & ( y0 > -r0 ))
+        kb = kb + 1;
+        xb(kb) = x0;
+        yb(kb) = y0;
+      end
+    end
+  end
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/FreqRho/RunFreqs.m",".m","671","41","function [freqs,w,wi] = RunFreqs
+
+tic
+
+ld = load('../Single/Data/FreqResp.dat');
+freqs = ld(:,1);
+P = 1000;
+A = .016*.028/4;
+V = A * .019;
+f = freqs;
+u = ld(:,2);
+a = -(2*pi*f).^2 .* u;
+m = P * A ./ a;
+rho = m / V;
+
+freqs = transpose( freqs );
+rho = transpose( rho );
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i), rho(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi); rho ];
+fl = fopen( 'Data/FreqResp.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+
+
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/FreqRho/SymFreqs.m",".m","4148","130","function [w,wi] = SymFreqs( freq, rho0 )
+bSolve = 1;
+%rho0 = 1068;
+
+flclear fem;
+
+Vtot = 0.13^2 * 0.019;
+Vres = 68 * ( 4/3 * pi * .007^3 );
+Vbal = 68 * ( 4/3 * pi * .005^3 );
+Vsil = Vres - Vbal;
+Vepx = Vtot - Vres;
+
+eEpx = ( 4.35e9 * Vepx + 2.0696e11 * Vbal + 1.175e5 * Vsil ) / Vtot;
+nuEpx = ( 0.368 * Vepx + 0.3 * Vbal + 0.469 * Vsil ) / Vtot;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+E = [5.e5,eEpx];
+nu = [0.4, nuEpx];
+rho = [1200,rho0];
+h = [0.003,0.015];
+r = 0.1085/2;
+ri = 0.1015/2;
+rt = 0.0035;
+l = 0.130;
+th = 0.019;
+
+tmpl = .130/2;
+tmpth = th+2*rt;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+gPlate = block3( l,l,th, 'base','center','pos',[0,0,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+
+gRing1 = cylinder3( r,rt, 'pos',[0,0,th],   'axis',{'0','0','1'}, 'rot','0' );
+gRing2 = cylinder3( r,rt, 'pos',[0,0,-rt],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp1 = cylinder3( ri,rt, 'pos',[0,0,th],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp2 = cylinder3( ri,rt, 'pos',[0,0,-rt], 'axis',{'0','0','1'}, 'rot','0' );
+gRing1 = geomcomp( {gRing1,gTemp1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTemp2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( tmpl,tmpl,tmpth, 'base','center','pos',[tmpl/2,tmpl/2,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+gPlate = geomcomp( {gPlate,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRing1 = geomcomp( {gRing1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gPlate,gRing1,gRing2};
+s.name={'Plate','Ring1','Ring2'};
+s.tags={'gPlate','gRing1','gRing2'};
+
+fem.draw=struct('s',s);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),2,h(1)]);
+%fem.mesh=meshinit(fem, 'hauto',5);
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','free','sym','fixed'};
+bnd.Fz = {0,1000,0,0};
+bnd.ind = [3,3,1,2,3,1,4,1,3,1,1,4,1,1,1,1,1,3,3,1];
+appl.bnd = bnd;
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+%equ.dampingtype = {'Rayleigh','nodamping'};
+%equ.betadK = {1.e-5,0};
+%equ.alphadM = {0,0};
+equ.dampingtype = {'nodamping'};
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind = [2,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;0;];
+  w = postinterp(fem, 'w', p);
+  wi = postint(fem,'w', 'unit','m^3', 'dl',[3], 'edim',2);
+  p2 = [.04/sqrt(2);.04/sqrt(2);0];
+  w2 = postinterp(fem, 'w', p2);
+  c = (l-.001)/2;
+  p3 = [c;c;0];
+  w3 = abs( postinterp(fem, 'w', p3) ) + abs( postinterp(fem,'u', p3) ) + abs( postinterp(fem,'v',p3) );
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/FreqRho/RunFreqs2.m",".m","907","51","function [freqs,w,wi] = RunFreqs2
+
+cd ../Single
+[f,ww] = RunFreqs;
+cd ../FreqRho
+
+tic
+
+Vtot = 0.13^2 * 0.019;
+Vres = 68 * ( 4/3 * pi * .007^3 );
+Vbal = 68 * ( 4/3 * pi * .005^3 );
+Vsil = Vres - Vbal;
+Vepx = Vtot - Vres;
+m0 = Vsil * 1300 + Vepx * 1180 + Vbal * 7780;
+
+ld = load('../Single/Data/FreqResp0.dat');
+freqs = ld(:,1);
+u = ld(:,2);
+A = .015*.015/4;
+a = -(2*pi*freqs).^2 .* u;
+m = 1000 * A ./ a;
+m = m * m0/m(1);
+rho = m / Vtot;
+
+freqs = transpose( freqs );
+rho = transpose( rho );
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+if( 3 > 2 )
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i), rho(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi); rho ];
+fl = fopen( 'Data/FreqResp.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e %e\n', Fout );
+fclose( fl );
+end
+toc
+
+
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Single/RunFreqs.m",".m","400","26","function [freqs,w] = RunFreqs
+
+tic
+
+freqs = [2:2:2200];
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+
+  w(i) = SymFreqs( freqs(i) );
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+
+end
+
+flname = 'Data/FreqResp0.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Single/SymGeom.m",".m","1480","27","function [s] = SymmetricGeom( rStl, rSil, rEpxX, rEpxY, rEpxZ )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpxX,rEpxY,rEpxZ, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpxX/2,rEpxY,rEpxZ, 'base','center', 'pos',[-rEpxX/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpxX,rEpxY/2,rEpxZ, 'base','center', 'pos',[0,-rEpxY/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Single/SymEigs.m",".m","2459","97","function [freq] = SymEigs( bUMF )
+
+bSolve = 1;
+ESil = 1.175e5;
+ERes = 4.35e9;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, ESil, ERes ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .0025 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.Fz = {0,0};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+
+  if( bUMF )
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'neigs',2 );
+  else
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  end
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Single/SymFreqs.m",".m","2461","98","function [w] = SymFreqs( freq )
+
+bSolve = 1;
+%freq = 2000;
+
+flclear fem;
+
+wv = sqrt(1.175e5/1300) / freq / 4;
+
+hs = min( 0.0015, wv );
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.007, 0.015, .015, .019 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .004, hs, .004 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4), r(5) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/10mmSteel/Single/TransLoss.m",".m","226","15","function [tl] = TransLoss( f, a, P )
+% f - frequency vector
+% a - acceleration 
+% P - applied pressure
+
+rho = 1.2;
+c = 344;
+
+omega = 2*pi * f;
+v = a ./ (j * omega);
+
+z = P ./ v;
+
+tl = 20 * log10( abs( 1 + z / 2 / rho / c ) );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/Blank/RunFreqs.m",".m","450","24","function [freqs,w,wi] = RunFreqs
+
+tic
+
+freqs = [100:10:2200];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i), 1068 );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi) ];
+fl = fopen( 'Data/FreqResp.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/Blank/SymFreqs.m",".m","4085","121","%function [w,wi] = SymFreqs( freq, rho0 )
+bSolve = 0;
+%freq = 1000;
+%bSolve = 0;
+rho0 = 1068;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+E = [1.e6,4.35e9];
+nu = [0.45, 0.368];
+rho = [1200,rho0];
+h = [0.003,0.015];
+r = 0.1085/2;
+ri = 0.1015/2;
+rt = 0.0035;
+l = 0.127;
+th = 0.019;
+
+tmpl = .127;
+tmpth = th+2*rt;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+gPlate = block3( l,l,th, 'base','center','pos',[0,0,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+gRing1 = cylinder3( r,rt, 'pos',[0,0,th],   'axis',{'0','0','1'}, 'rot','0' );
+gRing2 = cylinder3( r,rt, 'pos',[0,0,-rt],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp1 = cylinder3( ri,rt, 'pos',[0,0,th],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp2 = cylinder3( ri,rt, 'pos',[0,0,-rt], 'axis',{'0','0','1'}, 'rot','0' );
+gRing1 = geomcomp( {gRing1,gTemp1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTemp2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( tmpl,tmpl,tmpth, 'base','center','pos',[0,-tmpl/2,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+gPlate = geomcomp( {gPlate,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing1 = geomcomp( {gRing1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( tmpl,tmpl,tmpth, 'base','center','pos',[-tmpl/2,0,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+gPlate = geomcomp( {gPlate,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing1 = geomcomp( {gRing1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gPlate,gRing1,gRing2};
+s.name={'Plate','Ring1','Ring2'};
+s.tags={'gPlate','gRing1','gRing2'};
+
+fem.draw=struct('s',s);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),3,h(1)]);
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','fixed','free','sym'};
+bnd.Fz = {0,0,1000,0};
+bnd.ind = [4,4,1,3,4,1,2,1,4,1,1,2,1,1,1,1,1,4,4,1];
+appl.bnd = bnd;
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+%equ.dampingtype = {'Rayleigh','nodamping'};
+%equ.betadK = {1.e-5,0};
+%equ.alphadM = {0,0};
+equ.dampingtype = {'nodamping'};
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind = [2,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;0;];
+  w = postinterp(fem, 'w', p);
+  wi = postint(fem,'w', 'unit','m^3', 'dl',[3], 'edim',2);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/GetMass.m",".m","392","28","function [m] = GetMass
+
+l = 0.13;
+th = 0.019;
+rStl = 0.003;
+rSil = 0.007;
+
+nRes = 68;
+
+pStl = 7780;
+pSil = 1300;
+pEpx = 1180;
+
+vBlank = l^2 * th
+v1Res = 4/3 * pi * rSil^3;
+v1Stl = 4/3 * pi * rStl^3;
+v1Sil = v1Res - v1Stl;
+
+vEpx = vBlank - nRes * v1Res
+vSil = nRes * v1Sil;
+vStl = nRes * v1Stl;
+
+mEpx = vEpx * pEpx / 4;
+mSil = vSil * pSil / 4;
+mStl = vStl * pStl / 4;
+
+m = mEpx + mSil + mStl;
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/GetGeoms.m",".m","294","6","function [gStl,gSil] = GetGeoms(x,y,rStl,rSil)
+
+gStl = sphere3( rStl, 'pos',[x,y,0], 'axis',{'0','0','1'}, 'rot','0' );
+gTmp = sphere3( rSil, 'pos',[x,y,0], 'axis',{'0','0','1'}, 'rot','0' ); 
+gSil = geomcomp( {gTmp,gStl}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/RunFreqs.m",".m","533","27","function [freqs,w,wi,w2] = RunFreqs
+
+tic
+
+freqs = [100:20:2200];
+%freqs = 100:20:120;
+freqs = [100:5:700];
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [w0,wi0,w20] = SymFreqs( freqs(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  w2(i) = w20;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi); real(w2); imag(w2) ];
+fl = fopen( 'Data/FRBalls.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/TestPlot.m",".m","1299","49","function [x,y] = GetCenters( panel_len, coating_dia )
+
+panel_len = 12.7e-2;
+coating_dia = 15.0e-3;
+
+num_rows = 9;
+num_rows_1 = 5;
+num_rows_2 = num_rows_1 - 1;
+num_cols = 8;
+num_cols_1 = 8;
+num_cols_2 = num_cols_1 - 1;
+row_start_dist_1 = 8.0e-3;
+row_start_dist_2 = row_start_dist_1 + 1.05 * coating_dia * sin(pi/3)
+col_start_dist_1 = 8.0e-3;
+col_start_dist_2 = col_start_dist_1 + coating_dia * sin(pi/6)
+
+row_width_1 = panel_len - 2 * row_start_dist_1;
+row_width_2 = panel_len - 2 * row_start_dist_2;
+col_width_1 = panel_len - 2 * col_start_dist_1;
+col_width_2 = panel_len - 2 * col_start_dist_2;
+row_inc_1 = row_width_1 / ( num_rows_1 - 1 );
+row_inc_2 = row_width_2 / ( num_rows_2 - 1 );
+col_inc_1 = col_width_1 / ( num_cols_1 - 1 );
+col_inc_2 = col_width_2 / ( num_cols_2 - 1 );
+
+
+a = 1.05*coating_dia*sin(pi/3)
+b = coating_dia*sin(pi/6)
+c = row_inc_1 / 2
+d = row_inc_2 / 2
+
+k = 0;
+for jj =  1:num_cols_1
+  for ii = 1:num_rows_1
+    k = k + 1;
+    x(k) = col_start_dist_1 + (jj-1) * col_inc_1 - panel_len/2;
+    y(k) = row_start_dist_1 + (ii-1) * row_inc_1 - panel_len/2;
+   end
+end
+for jj = 1:num_cols_2
+  for ii = 1:num_rows_2
+    k = k + 1;
+    x(k) = col_start_dist_2 + (jj-1) * col_inc_2 - panel_len/2;
+    y(k) = row_start_dist_2 + (ii-1) * row_inc_2 - panel_len/2;
+  end
+end
+
+plot(x,y,'o');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/SymGeom.m",".m","6706","136","function [s,Subs] = SymGeom
+
+rStl = 3.e-3;
+rSil = 7e-3;
+rEpx = 12.7e-2;
+rFor = 4.45e-2;
+tEpx = 1.9e-2;
+
+rOut = 0.1085/2;
+rIn = 0.1015/2;
+rTh = 0.0035;
+
+[x,y,xb,yb] = GetCenters( rEpx, 2*rSil );
+rEpx = 0.13;
+
+gEpx = block3( rEpx,rEpx,tEpx, 'base','center', 'pos',[0,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+
+gRng1 = cylinder3( rOut,rTh, 'pos',[0,0,tEpx/2],   'axis',{'0','0','1'}, 'rot','0' );
+gRng2 = cylinder3( rOut,rTh, 'pos',[0,0,-rTh-tEpx/2],  'axis',{'0','0','1'}, 'rot','0' );
+gTmp1 = cylinder3( rIn, rTh, 'pos',[0,0,tEpx/2],  'axis',{'0','0','1'}, 'rot','0' );
+gTmp2 = cylinder3( rIn, rTh, 'pos',[0,0,-rTh-tEpx/2], 'axis',{'0','0','1'}, 'rot','0' );
+gRng1 = geomcomp( {gRng1,gTmp1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRng2 = geomcomp( {gRng2,gTmp2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+[gStl1, gSil1] =  GetGeoms( x(1),  y(1),  rStl, rSil );
+[gStl2, gSil2] =  GetGeoms( x(2),  y(2),  rStl, rSil );
+[gStl3, gSil3] =  GetGeoms( x(3),  y(3),  rStl, rSil );
+[gStl4, gSil4] =  GetGeoms( x(4),  y(4),  rStl, rSil );
+[gStl5, gSil5] =  GetGeoms( x(5),  y(5),  rStl, rSil );
+[gStl6, gSil6] =  GetGeoms( x(6),  y(6),  rStl, rSil );
+[gStl7, gSil7] =  GetGeoms( x(7),  y(7),  rStl, rSil );
+[gStl8, gSil8] =  GetGeoms( x(8),  y(8),  rStl, rSil );
+[gStl9, gSil9] =  GetGeoms( x(9),  y(9),  rStl, rSil );
+[gStl10,gSil10] = GetGeoms( x(10), y(10), rStl, rSil );
+[gStl11,gSil11] = GetGeoms( x(11), y(11), rStl, rSil );
+[gStl12,gSil12] = GetGeoms( x(12), y(12), rStl, rSil );
+[gStl13,gSil13] = GetGeoms( x(13), y(13), rStl, rSil );
+[gStl14,gSil14] = GetGeoms( x(14), y(14), rStl, rSil );
+[gStl15,gSil15] = GetGeoms( xb(1), yb(1), rStl, rSil );
+[gStl16,gSil16] = GetGeoms( xb(2), yb(2), rStl, rSil );
+[gStl17,gSil17] = GetGeoms( xb(3), yb(3), rStl, rSil );
+[gStl18,gSil18] = GetGeoms( xb(4), yb(4), rStl, rSil );
+[gStl19,gSil19] = GetGeoms( xb(5), yb(5), rStl, rSil );
+[gStl20,gSil20] = GetGeoms( xb(6), yb(6), rStl, rSil );
+
+gTmp = block3( rEpx/2,rEpx/2,tEpx+2*rTh, 'base','center', 'pos',[rEpx/4,rEpx/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRng1 = geomcomp( {gRng1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRng2 = geomcomp( {gRng2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+gStl15 = geomcomp( {gStl15,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl16 = geomcomp( {gStl16,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl17 = geomcomp( {gStl17,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl18 = geomcomp( {gStl18,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl19 = geomcomp( {gStl19,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gStl20 = geomcomp( {gStl20,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil15 = geomcomp( {gSil15,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil16 = geomcomp( {gSil16,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil17 = geomcomp( {gSil17,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil18 = geomcomp( {gSil18,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil19 = geomcomp( {gSil19,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gSil20 = geomcomp( {gSil20,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+
+gTmp = geomcomp( {gSil1,gStl1,gSil2,gStl2}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil3,gStl3,gSil4,gStl4}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil5,gStl5,gSil6,gStl6}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil7,gStl7,gSil8,gStl8}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil9,gStl9,gSil10,gStl10}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil11,gStl11,gSil12,gStl12}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil13,gStl13,gSil14,gStl14}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil15,gStl15,gSil16,gStl16}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = geomcomp( {gSil17,gStl17,gSil18,gStl18}, 'ns',{'obj1','obj2','obj3','obj4'}, 'sf','obj1+obj2+obj3+obj4', 'face','none', 'edge','all' );
+gTmp = geomcomp( {gTmp,gSil19,gStl19,gSil20,gStl20}, 'ns',{'obj1','obj2','obj3','obj4','obj5'}, ...
+		  'sf','obj1+obj2+obj3+obj4+obj5', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+
+% Analyzed geometry
+clear s
+s.objs={gEpx,gRng1,gRng2,...
+	gStl1,gStl2,gStl3,gStl4,gStl5,gStl6,gStl7,gStl8,gStl9,gStl10,...
+	gStl11,gStl12,gStl13,gStl14,gStl15,gStl16,gStl17,gStl18,gStl19,gStl20,...
+	gSil1,gSil2,gSil3,gSil4,gSil5,gSil6,gSil7,gSil8,gSil9,gSil10,...
+	gSil11,gSil12,gSil13,gSil14,gSil15,gSil16,gSil17,gSil18,gSil19,gSil20};
+
+sEpx = 1;
+sRng = [4,4];
+sStl = 3*ones(1,20);
+sSil = 2*ones(1,20);
+Subs = [sEpx,sRng,sStl,sSil];
+
+
+
+
+%=========================================================
+%  Temporary
+
+%fem.draw=struct('s',s);
+%[g,st] = geomcsg(fem);
+%[SubInd,s0] = find(st);   
+%fem.geom = geomcsg(fem);
+%size(SubInd)
+
+
+% (Default values are not included)
+% Application mode 1
+%clear appl
+%appl.mode.class = 'SmeSolid3';
+%appl.module = 'SME';
+%appl.gporder = 4;
+%appl.cporder = 2;
+%appl.assignsuffix = '_smsld';
+
+%fem.appl{1} = appl;
+%fem.frame = {'ref'};
+%fem.border = 1;
+%clear units;
+%units.basesystem = 'SI';
+%fem.units = units;
+
+% Multiphysics
+%fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Test.m",".m","1485","61","% COMSOL Multiphysics Model M-file
+% Generated by COMSOL 3.3a (COMSOL 3.3.0.511, $Date: 2007/02/02 19:05:58 $)
+
+flclear fem
+
+% COMSOL version
+clear vrsn
+vrsn.name = 'COMSOL 3.3';
+vrsn.ext = 'a';
+vrsn.major = 0;
+vrsn.build = 511;
+vrsn.rcs = '$Name:  $';
+vrsn.date = '$Date: 2007/02/02 19:05:58 $';
+fem.version = vrsn;
+
+% Geometry
+g1=sphere3('1','pos',{'-2','-2','0'},'axis',{'0','0','1'},'rot','0');
+g2=sphere3('1','pos',{'2','2','0'},'axis',{'0','0','1'},'rot','0');
+g3=geomcomp({g1,g2},'ns',{'SPH1','SPH2'},'sf','SPH1+SPH2','face','none','edge','all');
+g4=sphere3('1','pos',{'-2','2','0'},'axis',{'0','0','1'},'rot','0');
+g5=geomcomp({g3,g4},'ns',{'CO1','SPH1'},'sf','SPH1+CO1','face','none','edge','all');
+g6=sphere3('0.5','pos',{'1','-1','0'},'axis',{'0','0','1'},'rot','0');
+g7=sphere3('0.5','pos',{'-4','-4','0'},'axis',{'0','0','1'},'rot','0');
+
+% Analyzed geometry
+clear s
+s.objs={g5,g6,g7};
+s.name={'CO2','SPH4','SPH5'};
+s.tags={'g5','g6','g7'};
+
+fem.draw=struct('s',s);
+[g,st] = geomcsg(fem);
+[SubInd,s0] = find(st)    
+fem.geom = geomcsg(fem);
+
+
+% (Default values are not included)
+a = 2
+% Application mode 1
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+
+clear equ
+equ.E = { 2.0e11, 1.0e11, 4.e5 };
+equ.ind(SubInd) = s0;
+appl.equ = equ;
+
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+
+% Multiphysics
+fem=multiphysics(fem);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/SymFreqs.m",".m","3878","136","%function [w,wi,w2] = SymFreqs( freq )
+
+bSolve = 0;
+freq = 1000;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+rStl = 3.e-3;
+rSil = 7e-3;
+rEpx = 12.7e-2;
+rFor = 4.45e-2;
+tEpx = 1.9e-2;
+
+rOut = 0.1085/2;
+rIn = 0.1015/2;
+rTh = 0.0035;
+E = [ 4.35e9, 1.175e5, 2.0696e11, 2.5e5 ];
+nu = [ 0.368, 0.469, 0.3, 0.45 ];
+rho = [ 1180, 1300, 7780, 1200 ];
+h = [ .07, .0037, .1, 0.003 ];
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,Subs] = SymGeom;
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains (Coarse)
+%========================================================
+for i = 1:length(Subs)
+  dh(2*i-1) = SubInd(i);
+  dh(2*i)   = h( Subs(i) );
+end
+
+fem.mesh=meshinit(fem, 'hmaxsub',dh );
+%fem.mesh=meshinit(fem, 'hauto',6);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','fixed','sym','free'};
+bnd.Fz = {0,0,0,1000};
+bnd.ind = [3,3,1,4,3,1,1,3,1,1,1,1,1,1,3,1,1,3,1,1,1,1,1,1,3,1,2,1,3,1, ...
+  1,2,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, ...
+  1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,3,1,1, ...
+  1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3), rho(4) };
+equ.nu = { nu(1), nu(2), nu(3), nu(4) };
+equ.E = { E(1), E(2), E(3), E(4) };
+equ.ind( SubInd ) = Subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-(1.9e-2)/2];
+  w = postinterp(fem, 'w', p);
+  p2 = [.13/2;.13/2;-.019/2];
+  w2 = postinterp(fem, 'w', p);
+  A = pi*rIn^2/4;
+  wi = 1/A * postint(fem,'w', 'unit','m^3', 'dl',[3], 'edim',2);
+
+  rEpx = 12.7e-2;
+  rSil = 7.0e-3;
+  rStl = 3.0e-3;
+  [x,y,xb,yb] = GetCenters( rEpx, 2*rSil );
+  x = [x,xb];
+  y = [y,yb];
+  z = zeros(size(x));
+  p1 = [x;y;z];
+  w0 = postinterp(fem, 'w', p1);
+  p1 = [x;y+rStl;z];
+  u1 = postinterp(fem, 'u', p1);
+  v1 = postinterp(fem, 'v', p1);
+  w1 = postinterp(fem, 'w', p1);
+  Fout = [ x; y; w0; u1; v1; w1 ];
+  flname = strcat( 'Data/Balls/Balls-', num2str(freq), '.dat' );
+  fl = fopen( flname, 'wt' );
+  fprintf( fl, '%e %e %e %e %e %e\n', Fout );
+  fclose( fl );
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/GetCenters.m",".m","1564","61","function [x,y,xb,yb] = GetCenters( panel_len, coating_dia )
+
+num_rows = 9;
+num_rows_1 = 5;
+num_rows_2 = num_rows_1 - 1;
+num_cols = 8;
+num_cols_1 = 8;
+num_cols_2 = num_cols_1 - 1;
+row_start_dist_1 = 8.0e-3;
+row_start_dist_2 = row_start_dist_1 + 1.05 * coating_dia * sin(pi/3);
+col_start_dist_1 = 8.0e-3;
+col_start_dist_2 = col_start_dist_1 + coating_dia * sin(pi/6);
+
+row_width_1 = panel_len - 2 * row_start_dist_1;
+row_width_2 = panel_len - 2 * row_start_dist_2;
+col_width_1 = panel_len - 2 * col_start_dist_1;
+col_width_2 = panel_len - 2 * col_start_dist_2;
+row_inc_1 = row_width_1 / ( num_rows_1 - 1 );
+row_inc_2 = row_width_2 / ( num_rows_2 - 1 );
+col_inc_1 = col_width_1 / ( num_cols_1 - 1 );
+col_inc_2 = col_width_2 / ( num_cols_2 - 1 );
+
+k = 0;
+kb = 0;
+r0 = coating_dia/2;
+for jj =  1:num_cols_1
+  for ii = 1:num_rows_1
+    x0 = col_start_dist_1 + (jj-1) * col_inc_1 - panel_len/2;
+    y0 = row_start_dist_1 + (ii-1) * row_inc_1 - panel_len/2;
+    if(( x0 > 0 ) & ( y0 > 0 ))
+      k = k + 1;
+      x(k) = x0;
+      y(k) = y0;
+    else 
+      if(( x0 > -r0 ) & ( y0 > -r0 ))
+        kb = kb + 1;
+        xb(kb) = x0;
+        yb(kb) = y0;
+      end
+    end
+  end
+end
+
+for jj = 1:num_cols_2
+  for ii = 1:num_rows_2
+    x0 = col_start_dist_2 + (jj-1) * col_inc_2 - panel_len/2;
+    y0 = row_start_dist_2 + (ii-1) * row_inc_2 - panel_len/2;
+    if(( x0 > 0 ) & ( y0 > 0 ))
+      k = k + 1;
+      x(k) = x0;
+      y(k) = y0;
+    else
+      if(( x0 > -r0 ) & ( y0 > -r0 ))
+        kb = kb + 1;
+        xb(kb) = x0;
+        yb(kb) = y0;
+      end
+    end
+  end
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/FreqRho/RunFreqs.m",".m","671","41","function [freqs,w,wi] = RunFreqs
+
+tic
+
+ld = load('../Single/Data/FreqResp.dat');
+freqs = ld(:,1);
+P = 1000;
+A = .016*.028/4;
+V = A * .019;
+f = freqs;
+u = ld(:,2);
+a = -(2*pi*f).^2 .* u;
+m = P * A ./ a;
+rho = m / V;
+
+freqs = transpose( freqs );
+rho = transpose( rho );
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i), rho(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi); rho ];
+fl = fopen( 'Data/FreqResp.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e %e\n', Fout );
+fclose( fl );
+
+toc
+
+
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/FreqRho/SymFreqs.m",".m","4148","130","%function [w,wi] = SymFreqs( freq, rho0 )
+bSolve = 0;
+rho0 = 1068;
+
+flclear fem;
+
+Vtot = 0.13^2 * 0.019;
+Vres = 68 * ( 4/3 * pi * .007^3 );
+Vbal = 68 * ( 4/3 * pi * .003^3 );
+Vsil = Vres - Vbal;
+Vepx = Vtot - Vres;
+
+eEpx = ( 4.35e9 * Vepx + 2.0696e11 * Vbal + 1.175e5 * Vsil ) / Vtot;
+nuEpx = ( 0.368 * Vepx + 0.3 * Vbal + 0.469 * Vsil ) / Vtot;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+E = [5.e5,eEpx];
+nu = [0.4, nuEpx];
+rho = [1200,rho0];
+h = [0.003,0.015];
+r = 0.1085/2;
+ri = 0.1015/2;
+rt = 0.0035;
+l = 0.130;
+th = 0.019;
+
+tmpl = .130/2;
+tmpth = th+2*rt;
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+gPlate = block3( l,l,th, 'base','center','pos',[0,0,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+
+gRing1 = cylinder3( r,rt, 'pos',[0,0,th],   'axis',{'0','0','1'}, 'rot','0' );
+gRing2 = cylinder3( r,rt, 'pos',[0,0,-rt],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp1 = cylinder3( ri,rt, 'pos',[0,0,th],  'axis',{'0','0','1'}, 'rot','0' );
+gTemp2 = cylinder3( ri,rt, 'pos',[0,0,-rt], 'axis',{'0','0','1'}, 'rot','0' );
+gRing1 = geomcomp( {gRing1,gTemp1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTemp2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( tmpl,tmpl,tmpth, 'base','center','pos',[tmpl/2,tmpl/2,th/2], 'axis',{'0','0','1'}, 'rot','0' );
+gPlate = geomcomp( {gPlate,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRing1 = geomcomp( {gRing1,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+gRing2 = geomcomp( {gRing2,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1*obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gPlate,gRing1,gRing2};
+s.name={'Plate','Ring1','Ring2'};
+s.tags={'gPlate','gRing1','gRing2'};
+
+fem.draw=struct('s',s);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(2),2,h(1),2,h(1)]);
+%fem.mesh=meshinit(fem, 'hauto',5);
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','free','sym','fixed'};
+bnd.Fz = {0,1000,0,0};
+bnd.ind = [3,3,1,2,3,1,4,1,3,1,1,4,1,1,1,1,1,3,3,1];
+appl.bnd = bnd;
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+%equ.dampingtype = {'Rayleigh','nodamping'};
+%equ.betadK = {1.e-5,0};
+%equ.alphadM = {0,0};
+equ.dampingtype = {'nodamping'};
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind = [2,1,1];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;0;];
+  w = postinterp(fem, 'w', p);
+  wi = postint(fem,'w', 'unit','m^3', 'dl',[3], 'edim',2);
+  p2 = [.04/sqrt(2);.04/sqrt(2);0];
+  w2 = postinterp(fem, 'w', p2);
+  c = (l-.001)/2;
+  p3 = [c;c;0];
+  w3 = abs( postinterp(fem, 'w', p3) ) + abs( postinterp(fem,'u', p3) ) + abs( postinterp(fem,'v',p3) );
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/FreqRho/RunFreqs2.m",".m","906","51","function [freqs,w,wi] = RunFreqs2
+
+cd ../Single
+[f,ww] = RunFreqs;
+cd ../FreqRho
+
+tic
+
+Vtot = 0.13^2 * 0.019;
+Vres = 68 * ( 4/3 * pi * .007^3 );
+Vbal = 68 * ( 4/3 * pi * .003^3 );
+Vsil = Vres - Vbal;
+Vepx = Vtot - Vres;
+m0 = Vsil * 1300 + Vepx * 1180 + Vbal * 7780;
+
+ld = load('../Single/Data/FreqResp.dat');
+freqs = ld(:,1);
+u = ld(:,2);
+A = .015*.015/4;
+a = -(2*pi*freqs).^2 .* u;
+m = 1000 * A ./ a;
+m = m * m0/m(1);
+rho = m / Vtot;
+
+freqs = transpose( freqs );
+rho = transpose( rho );
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+
+if( 3 > 2 )
+for i = 1:n
+  [w0,wi0] = SymFreqs( freqs(i), rho(i) );
+  w(i) = w0;
+  wi(i) = wi0;
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete' )
+  end
+end
+
+Fout = [ freqs; real(w); imag(w); real(wi); imag(wi); rho ];
+fl = fopen( 'Data/FreqResp.dat', 'wt' );
+fprintf( fl, '%e %e %e %e %e %e\n', Fout );
+fclose( fl );
+end
+toc
+
+
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/Make6mmFaPlot.m",".m","856","43","rho = 1.2;
+c = 344;
+m = 0.621;
+A = (.1015/2)^2 * pi;
+A1 = A/4;
+P = 1000;
+F1 = 1000*A1;
+
+fr0 = load('../Data/FRFine.dat');
+fr1 = load('../FreqRho/Data/FreqResp.dat');
+
+f0 = fr0(:,1);
+w0 = fr0(:,4);
+
+f1 = fr1(:,1);
+w1 = fr1(:,4)/A1;
+n = length(f1);
+f2 = fr1(1:10:n,1);
+
+P = 1000;
+om0 = j*2*pi*f0;
+om1 = j*2*pi*f1;
+om2 = j*2*pi*f2;
+
+z0 = P./(om0.*w0);
+z1 = P./(om1.*w1);
+
+T0 = 20*log10( abs( 1 + 1/2/rho/c*z0 ) );
+T1 = 20*log10( abs( 1 + 1/2/rho/c*z1 ) );
+Tm = 20*log10( abs( 1 + 1/2/rho/c * om2 * m / A ) );
+
+Fa0 = 4 * abs( F1 ./ ( om0.^2 .* w0 ) );
+Fa1 = 4 * abs( F1 ./ ( om1.^2 .* w1 ) );
+
+semilogy( f0,Fa0,'k', f1,Fa1,'--k', f2,m,'-+k', 'LineWidth',1.5 );
+xlim([0 2200]);
+ylim([1.e-3,1.e3]);
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+xlabel('f (Hz)');
+ylabel('F/a (kg)');
+legend('Detailed', 'Homogenized', 'Mass Law', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/Make10mmTcFig.m",".m","785","39","rho = 1.2;
+c = 344;
+m = 0.621;
+A = (.1015/2)^2 * pi;
+A1 = A/4;
+
+fr0 = load('../10mm/Data/FRFine.dat');
+fr1 = load('../10mm/FreqRho/Data/FreqResp.dat');
+
+f0 = fr0(:,1);
+w0 = fr0(:,4);
+
+f1 = fr1(:,1);
+w1 = fr1(:,4)/A1;
+n = length(f1);
+f2 = fr1(1:10:n,1);
+
+P = 1000;
+om0 = j*2*pi*f0;
+om1 = j*2*pi*f1;
+om2 = j*2*pi*f2;
+
+z0 = P./(om0.*w0);
+z1 = P./(om1.*w1);
+
+T0 = 20*log10( abs( 1 + 1/2/rho/c*z0 ) );
+T1 = 20*log10( abs( 1 + 1/2/rho/c*z1 ) );
+Tm = 20*log10( abs( 1 + 1/2/rho/c * om2 * m / A ) );
+
+plot( f0,T0,'k', f1,T1,'--k', f2,Tm,'-+k', 'LineWidth',1.5 );
+axis([0 2200 0 110]);
+grid on;
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+%set(gca,'FontUnits','centimeters');
+xlabel('f (Hz)');
+ylabel('TL (dB)');
+legend('Detailed', 'Homogenized', 'Mass Law', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/Make10mmFaPlot.m",".m","866","43","rho = 1.2;
+c = 344;
+m = 0.621;
+A = (.1015/2)^2 * pi;
+A1 = A/4;
+P = 1000;
+F1 = 1000*A1;
+
+fr0 = load('../10mm/Data/FRFine.dat');
+fr1 = load('../10mm/FreqRho/Data/FreqResp.dat');
+
+f0 = fr0(:,1);
+w0 = fr0(:,4);
+
+f1 = fr1(:,1);
+w1 = fr1(:,4)/A1;
+n = length(f1);
+f2 = fr1(1:10:n,1);
+
+P = 1000;
+om0 = j*2*pi*f0;
+om1 = j*2*pi*f1;
+om2 = j*2*pi*f2;
+
+z0 = P./(om0.*w0);
+z1 = P./(om1.*w1);
+
+T0 = 20*log10( abs( 1 + 1/2/rho/c*z0 ) );
+T1 = 20*log10( abs( 1 + 1/2/rho/c*z1 ) );
+Tm = 20*log10( abs( 1 + 1/2/rho/c * om2 * m / A ) );
+
+Fa0 = 4 * abs( F1 ./ ( om0.^2 .* w0 ) );
+Fa1 = 4 * abs( F1 ./ ( om1.^2 .* w1 ) );
+
+semilogy( f0,Fa0,'k', f1,Fa1,'--k', f2,m,'-+k', 'LineWidth',1.5 );
+xlim([0 2200]);
+ylim([1.e-3,1.e3]);
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+xlabel('f (Hz)');
+ylabel('F/a (kg)');
+legend('Detailed', 'Homogenized', 'Mass Law', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/latexpdf.sh",".sh","54","4","latex FigTest.tex
+dvips FigTest.dvi
+ps2pdf FigTest.ps
+","Shell"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/Make610mmBallPlot.m",".m","555","29","A = .015^2;
+P = 1000;
+F1 = 1000*A;
+
+fr6 = load('../Single/Data/FreqResp.dat');
+fr10 = load('../10mm/Single/Data/FreqResp0.dat');
+
+f0 = fr6(:,1);
+w0 = fr6(:,2);
+
+f1 = fr10(:,1);
+w1 = fr10(:,2);
+
+P = 1000;
+om0 = j*2*pi*f0;
+om1 = j*2*pi*f1;
+
+Fa0 = abs( F1 ./ ( om0.^2 .* w0 ) );
+Fa1 = abs( F1 ./ ( om1.^2 .* w1 ) );
+
+semilogy( f0,Fa0,'k', f1,Fa1,'--k', 'LineWidth',1.5 );
+xlim([0 2200]);
+%ylim([1.e-3,1.e3]);
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+xlabel('f (Hz)');
+ylabel('F/a (kg)');
+legend('6mm Ball', '10mm Ball', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/MakeCircSqrPlot.m",".m","952","44","rho = 1.2;
+c = 344;
+ms = 1068 * .127^2 * .019;
+mc = 1068 * .05^2 * pi * .019;
+A = (.089/2)^2 * pi;
+A1 = A/4;
+
+fr0 = load('../../Blank/Data/FreqRespSquare.dat');
+fr1 = load('../../Blank/Data/FreqRespCircle.dat');
+
+f0 = fr0(:,1);
+w0 = fr0(:,8);
+
+f1 = fr1(:,1);
+w1 = fr1(:,6)/A1;
+n = length(f1);
+f2 = fr1(1:10:n,1);
+
+P = 1000;
+om0 = 2*pi*f0;
+om1 = 2*pi*f1;
+om2 = j*2*pi*f2;
+
+alf0 = ms * om0.^2 .* w0;
+alf1 = mc * om1.^2 .* w1;
+
+z0 = P * (89/101.5)^2 * om0 ./ (-j*alf0);
+z1 = P * om1 ./ (-j*alf1);
+
+T0 = 20*log10( abs( 1 + 1/2/rho/c*z0 ) );
+T1 = 20*log10( abs( 1 + 1/2/rho/c*z1 ) );
+Tm = 20*log10( abs( 1 + 1/2/rho/c * om2 / A ) );
+
+%plot(f0,T0,f1,T1,f2,Tm);
+plot( f0,T0,'k', f1,T1,'--k', f2,Tm,'-+k', 'LineWidth',1.5 );
+axis([0 2400 0 120]);
+grid on;
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+%set(gca,'FontUnits','centimeters');
+xlabel('f (Hz)');
+ylabel('TL (dB)');
+legend('Square Slab', 'Circular Slab', 'Mass Law', 'Location','NorthWest');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/MakeDoubleResFig.m",".m","750","34","A = (.025/4)^2;
+h = 0.025;
+P = 1000;
+F = P*A;
+
+fr0 = load('../../../../Resonator-25/Array/OneMass/Data/FreqResp1.0.dat');
+fr1 = load('../../../../Resonator-25/Array/OneMass/Data/FreqResp1.2.dat');
+fr2 = load('../../../../Resonator-25/Array/TwoMass/Data/FreqResp1.2.dat');
+
+f  = fr0(:,1);
+om = (2*pi*f).^2;
+w0 = fr0(:,2);
+w1 = fr1(:,2);
+w2 = fr2(:,2);
+
+a0 = om.*w0;
+a1 = om.*w1;
+a2 = om.*w2;
+
+r0 = P/h ./ a0;
+r1 = P/h ./ a1;
+rc = (r0 + r1)/2;
+ac = P/h ./ rc;
+
+semilogy( f,abs(F./ac),'k', f,abs(F./a2),'--k','LineWidth',1.5 );
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+%set(gca,'FontUnits','centimeters');
+xlim([f(1),1000]);
+f(1)
+xlabel('f (Hz)');
+ylabel('F/a (kg)');
+legend('Double Resonator', 'Composite Accel.', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/Make6mmTcFig.m",".m","768","38","rho = 1.2;
+c = 344;
+m = 0.440;
+A = (.1015/2)^2 * pi;
+A1 = A/4;
+
+fr0 = load('../Data/FreqResp.dat');
+fr1 = load('../FreqRho/Data/FreqResp.dat');
+
+f0 = fr0(:,1);
+w0 = fr0(:,4);
+
+f1 = fr1(:,1);
+w1 = fr1(:,4)/A1;
+n = length(f1);
+f2 = fr1(1:10:n,1);
+
+P = 1000;
+om0 = j*2*pi*f0;
+om1 = j*2*pi*f1;
+om2 = j*2*pi*f2;
+
+z0 = P./(om0.*w0);
+z1 = P./(om1.*w1);
+
+T0 = 20*log10( abs( 1 + 1/2/rho/c*z0 ) );
+T1 = 20*log10( abs( 1 + 1/2/rho/c*z1 ) );
+Tm = 20*log10( abs( 1 + 1/2/rho/c * om2 * m / A ) );
+
+plot( f0,T0,'k', f1,T1,'--k', f2,Tm,'-+k', 'LineWidth',1.5 );
+axis([0 2200 0 110]);
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+%set(gca,'FontUnits','centimeters');
+xlabel('f (Hz)');
+ylabel('TL (dB)');
+legend('Detailed', 'Homogenized', 'Mass Law', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Figs/MakeTwoResFig.m",".m","524","24","A = (.025/4)^2;
+h = 0.025;
+P = 1000;
+F = P*A;
+
+fr0 = load('../../../../Resonator-25/Array/OneMass/Data/FreqResp1.0.dat');
+fr1 = load('../../../../Resonator-25/Array/OneMass/Data/FreqResp1.2.dat');
+
+f  = fr0(:,1);
+om = (2*pi*f).^2;
+w0 = fr0(:,2);
+w1 = fr1(:,2);
+
+a0 = om.*w0;
+a1 = om.*w1;
+
+semilogy( f,abs(F./a0),'k', f,abs(F./a1),'--k','LineWidth',1.5 );
+set(gca,'FontName','Arial');
+set(gca,'FontSize', 16);
+xlim([f(1),1000]);
+xlabel('f (Hz)');
+ylabel('F/a (kg)');
+legend('10mm Ball', '12mm Ball', 'Location','NorthEast');
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Single/RunFreqs.m",".m","394","23","function [freqs,w] = RunFreqs
+
+tic
+
+freqs = [2:2:2200];
+
+n = length(freqs);
+strcat( num2str(n), ' iterations' )
+for i = 1:n
+  w(i) = SymFreqs(freqs(i));
+  if( mod(i,floor(n/10)) == 0 )
+    strcat( num2str(round(100*i/n)),'% Complete')
+  end
+end
+
+flname = 'Data/FreqResp.dat';
+Fout = [ freqs; real(w); imag(w) ];
+fl = fopen( flname, 'wt' );
+fprintf( fl, '%e %e %e\n', Fout );
+fclose( fl );
+
+toc
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Single/SymGeom.m",".m","1480","27","function [s] = SymmetricGeom( rStl, rSil, rEpxX, rEpxY, rEpxZ )
+
+% Geometry
+g1 = sphere3( rStl, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g2 = sphere3( rSil, 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+g3 = block3( rEpxX,rEpxY,rEpxZ, 'base','center', 'pos',{'0','0','0'}, 'axis',{'0','0','1'}, 'rot','0' );
+gStl = g1;
+gSil = geomcomp( {g2,g1}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {g3,g2}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpxX/2,rEpxY,rEpxZ, 'base','center', 'pos',[-rEpxX/4,0,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+gTmp = block3( rEpxX,rEpxY/2,rEpxZ, 'base','center', 'pos',[0,-rEpxY/4,0], 'axis',{'0','0','1'}, 'rot','0' );
+gStl = geomcomp( {gStl,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gSil = geomcomp( {gSil,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+gEpx = geomcomp( {gEpx,gTmp}, 'ns',{'obj1','obj2'}, 'sf','obj1-obj2', 'face','none', 'edge','all' );
+
+% Analyzed geometry
+clear s
+s.objs={gStl,gSil,gEpx};
+s.name={'Stl','Sil','Epx'};
+s.tags={'gStl','gSil','gEpx'};
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Single/SymEigs.m",".m","2459","97","function [freq] = SymEigs( bUMF )
+
+bSolve = 1;
+ESil = 1.175e5;
+ERes = 4.35e9;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.005, 0.00775, 0.023 ];
+E = [ 2.0696e11, ESil, ERes ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .0025, .0012, .0025 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Eigenvalue Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym'};
+bnd.Fz = {0,0};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,1,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh = meshextend(fem);
+
+  if( bUMF )
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'neigs',2 );
+  else
+    fem.sol = femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+	   'rowscale','off', 'linsolver','spooles', 'neigs',2 );
+  end
+  fem0=fem;
+  freq = fem.sol.lambda(2)/(-2*j*pi);
+end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Ball/FullPanel/6mmSteel/Single/SymFreqs.m",".m","2461","98","%function [w] = SymFreqs( freq )
+
+bSolve = 0;
+freq = 1000;
+
+flclear fem;
+
+wv = sqrt(1.175e5/1300) / freq / 4;
+
+hs = min( 0.0015, wv );
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+r = [ 0.003, 0.007, 0.015, .015, .019 ];
+E = [ 2.0696e11, 1.175e5, 4.35e9 ];
+nu = [ 0.3, 0.469, 0.368 ];
+rho = [ 7780, 1300, 1180 ];
+h = [ .004, hs, .004 ];
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s] = SymGeom( r(1), r(2), r(3), r(4), r(5) );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+fem.mesh=meshinit(fem, 'hauto',5, ...
+                  'hmaxsub',[1,h(3),2,h(2),3,h(1)]);
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'free','sym','free'};
+bnd.Fz = {0,0,1000};
+bnd.ind = [2,2,1,2,2,1,2,2,1,1,1,3,1,1];
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2), rho(3) };
+equ.nu = { nu(1), nu(2), nu(3) };
+equ.E = { E(1), E(2), E(3) };
+equ.ind( SubInd ) = [1,2,3];
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  p = [0;0;-r(3)/2];
+  w = postinterp(fem, 'w', p);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/Hussein-1DBanded/RunFreqResp.m",".m","207","15","function [f,w] = RunFreqResp( n )
+
+tic
+
+f = 0:0.1:50;
+flname = strcat('Data/FR_',num2str(n),'.mat');
+
+for i = 1:length(f)
+  w(i,:) = FreqResp( f(i), 0.8, n, 12, 3 );
+  save(flname,'f','w','-mat');
+end
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/Hussein-1DBanded/ResFreq.m",".m","2360","109","%function [w] = ResFreq( lDepth, lRatio, lNum, propRatio )
+
+
+bSolve = 1;
+Omega = 1;
+lRatio = 0.8;
+lNum = 1;
+E_r = 12;
+Rho_r = 3;
+
+d = 1;
+df = d*lRatio;
+n = lNum;
+
+flclear fem;
+
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+
+E0 = 2.0e11;
+nu0 = 0.3;
+rho0 = 2000;
+
+E   = [ E0, E0 / E_r ];
+nu  = [ nu0, nu0 ];
+rho = [ rho0, rho0/Rho_r ];
+h0   = [ d/100 ];
+
+freq = Omega * sqrt( E(2)/rho(2) ) / d / 2 / pi;
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( d, df, n );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h0;
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','free'};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, 'linsolver','spooles' );
+  fem0=fem;
+
+  freq = fem.sol.lambda(1)/(-2*j*pi);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/Hussein-1DBanded/Geom.m",".m","965","40","function [s,subs,bnds,outBnd] = Geom( d, df, n )
+
+dm = d - df;
+dd = d/50;
+cnr = -dd/2;
+
+gFiber{1} = block3( dd,dd,df/2, 'base','corner', 'pos',[cnr,cnr,0], 'axis',{'0','0','1'}, 'rot','0' );
+gFiberName{1} = 'Fiber1';
+
+% Geometry
+for i = 1:n  
+  gMatrix{i} = block3( dd,dd,dm, 'base','corner', 'pos',[cnr,cnr,(i-1)*d+df/2], 'axis',{'0','0','1'}, 'rot','0' );
+  gMatrixName{i} = strcat( 'Matrix', num2str(i) );
+  
+  dfi = df - df/2 * (i==n);
+  gFiber{i+1} = block3( dd,dd,dfi, 'base','corner', 'pos',[cnr,cnr,(i-1)*d+df/2+dm], 'axis',{'0','0','1'}, 'rot','0' );
+  gFiberName{i+1} = strcat( 'Fiber', num2str(i) );
+end
+
+s.objs = { gFiber{:}, gMatrix{:} };
+s.name = { gFiberName{:}, gMatrixName{:} };
+s.tags = { gFiberName{:}, gMatrixName{:} };
+
+subs = [ ones(1,n+1), 2*ones(1,n) ];
+
+m = n + 1;
+nbnds = 5*(2*m-1)+1;
+for i = 1:(2*m-1)
+  bnd2(i) = 3*i;
+end
+bnd3  = 3*(2*m-1)+1;
+
+bnds = ones(1,nbnds);
+bnds(bnd2) = 2;
+bnds(bnd3) = 3;
+bnds(3) = 4;
+outBnd = [0,(n-1/2)*d];
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/Hussein-1DBanded/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/Hussein-1DBanded/FreqResp.m",".m","2544","111","%function [w] = FreqResp( Omega, lRatio, lNum, E_r, Rho_r )
+
+bSolve = 1;
+%Omega = 2;
+%lRatio = 0.8;
+%lNum = 5;
+%E_r = 12;
+%Rho_r = 3;
+
+d = 1;
+df = d*lRatio;
+n = lNum;
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+E0 = 2.0e11;
+nu0 = 0.3;
+rho0 = 2000;
+
+E   = [ E0, E0 / E_r ];
+nu  = [ nu0, nu0 ];
+rho = [ rho0, rho0/Rho_r ];
+h0   = [ d/50 ];
+P = 1000;
+
+freq = Omega * sqrt( E(2)/rho(2) ) / d / 2 / pi;
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( d, df, n );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h0;
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','fixed','free'};
+bnd.Fz = {0,0,0,P};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  pb = [xb;xb;outBnds];
+  w = postinterp( fem, 'w', pb );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/LayerExpts/RunFreqResp.m",".m","680","29","function [f,wSt1,wSt2,wAl] = RunFreqResp
+
+tic
+
+f = 10:10:1500;
+flname = 'Data/FR_.mat';
+
+beta = [1.e-3; 1.e-2];
+eAl = 70.e9;
+eSt = 200.e9;
+rAl = 2700;
+rSt = 7850;
+
+for i = 1:length(f)
+  k = 2*i-1;
+  wAl(k,:)  = FreqResp( f(i), 3, 0.01,   0.005, eAl, rAl, beta(1) );
+  wSt1(k,:) = FreqResp( f(i), 3, 0.01,   0.005, eSt, rSt, beta(1) );
+  wSt2(k,:) = FreqResp( f(i), 3, 0.0035, 0.005, eSt, rSt, beta(1) );
+
+  k = 2*i;
+  wAl(k,:)  = FreqResp( f(i), 3, 0.01,   0.005, eAl, rAl, beta(2) );
+  wSt1(k,:) = FreqResp( f(i), 3, 0.01,   0.005, eSt, rSt, beta(2) );
+  wSt2(k,:) = FreqResp( f(i), 3, 0.0035, 0.005, eSt, rSt, beta(2) );
+  save(flname,'f','wAl','wSt1','wSt2','-mat');
+end
+
+toc
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/LayerExpts/ResFreq.m",".m","2360","109","%function [w] = ResFreq( lDepth, lRatio, lNum, propRatio )
+
+
+bSolve = 1;
+Omega = 1;
+lRatio = 0.8;
+lNum = 1;
+E_r = 12;
+Rho_r = 3;
+
+d = 1;
+df = d*lRatio;
+n = lNum;
+
+flclear fem;
+
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+
+E0 = 2.0e11;
+nu0 = 0.3;
+rho0 = 2000;
+
+E   = [ E0, E0 / E_r ];
+nu  = [ nu0, nu0 ];
+rho = [ rho0, rho0/Rho_r ];
+h0   = [ d/100 ];
+
+freq = Omega * sqrt( E(2)/rho(2) ) / d / 2 / pi;
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( d, df, n );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h0;
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='eigen';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','free'};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'nodamping';
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femeig( fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, 'linsolver','spooles' );
+  fem0=fem;
+
+  freq = fem.sol.lambda(1)/(-2*j*pi);
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/LayerExpts/Geom.m",".m","898","38","function [s,subs,bnds,outBnd] = Geom( df, dm, n )
+
+d = df + dm;;
+dd = d/8;
+cnr = -dd/2;
+
+gFiber{1} = block3( dd,dd,df, 'base','corner', 'pos',[cnr,cnr,0], 'axis',{'0','0','1'}, 'rot','0' );
+gFiberName{1} = 'Fiber1';
+
+% Geometry
+for i = 1:n  
+  gMatrix{i} = block3( dd,dd,dm, 'base','corner', 'pos',[cnr,cnr,(i-1)*d+df], 'axis',{'0','0','1'}, 'rot','0' );
+  gMatrixName{i} = strcat( 'Matrix', num2str(i) );
+  
+  gFiber{i+1} = block3( dd,dd,df, 'base','corner', 'pos',[cnr,cnr,i*d], 'axis',{'0','0','1'}, 'rot','0' );
+  gFiberName{i+1} = strcat( 'Fiber', num2str(i) );
+end
+
+s.objs = { gFiber{:}, gMatrix{:} };
+s.name = { gFiberName{:}, gMatrixName{:} };
+s.tags = { gFiberName{:}, gMatrixName{:} };
+
+subs = [ ones(1,n+1), 2*ones(1,n) ];
+
+m = n + 1;
+nbnds = 5*(2*m-1)+1;
+for i = 1:(2*m-1)
+  bnd2(i) = 3*i;
+end
+bnd3  = 3*(2*m-1)+1;
+
+bnds = ones(1,nbnds);
+bnds(bnd2) = 2;
+bnds(bnd3) = 3;
+outBnd = [0];
+
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/LayerExpts/RunAll.m",".m","132","5","[f3,wb3,wc3] = RunFreqResp( 3 );
+[f4,wb4,wc4] = RunFreqResp( 4 );
+[f1,wb1,wc1] = RunFreqResp( 1 );
+[f2,wb2,wc2] = RunFreqResp( 2 );
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","Comsol/Layer/LayerExpts/FreqResp.m",".m","2585","117","function [w] = FreqResp( freq, n, d_f, d_m, E_f, rho_f, beta )
+
+bSolve = 1;
+nargin = 1;
+
+if nargin < 1
+  freq = 500;
+  n = 3;
+  d_f = 0.01;
+  d_m = 0.005;
+  E_f = 2.e11;
+  rho_f = 7850;
+  beta = 1.e-3;
+end
+
+flclear fem;
+
+%========================================================
+%  Simulation Properties [ Steel, Silicon, Epoxy ]
+%========================================================
+E_m = 1.175e5;
+nu_m = 0.469;
+rho_m = 1300;
+
+nu_f = 0.3;
+
+E   = [ E_f, E_m ];
+nu  = [ nu_f, nu_m ];
+rho = [ rho_f, rho_m ];
+h0   = [ 0.001 ];
+P = 1000;
+
+
+%========================================================
+%  Load Geometry
+%========================================================
+clear s;
+[s,subs,bnds,outBnds] = Geom( d_f, d_m, n );
+
+fem.draw=struct('s',s);
+[g,st,ft,pt] = geomcsg(fem);
+[SubInd,s0] = find(st);
+fem.geom = geomcsg(fem);
+
+
+%========================================================
+%  Mesh Subdomains
+%========================================================
+for i = 1:length(subs)
+  hm(2*i-1) = i;
+  hm(2*i) = h0;
+end
+fem.mesh=meshinit( fem, 'hauto',5, 'hmaxsub',hm );
+
+
+%========================================================
+%  Frequency Mode
+%========================================================
+clear appl
+appl.mode.class = 'SmeSolid3';
+appl.module = 'SME';
+appl.gporder = 4;
+appl.cporder = 2;
+appl.assignsuffix = '_smsld';
+clear prop
+prop.analysis='freq';
+appl.prop = prop;
+
+
+%========================================================
+%  Boundary Conditions
+%========================================================
+clear bnd
+bnd.constrcond = {'sym','free','free'};
+bnd.Fz = {0,0,P};
+bnd.ind = bnds;
+appl.bnd = bnd;
+
+
+%========================================================
+%  Set Up Solver
+%========================================================
+clear equ
+equ.dampingtype = 'Rayleigh';
+equ.betadK = { beta };
+equ.alphadM = { 0 };
+
+equ.rho = { rho(1), rho(2) };
+equ.nu = { nu(1), nu(2) };
+equ.E = { E(1), E(2) };
+
+equ.ind( SubInd ) = subs;
+appl.equ = equ;
+fem.appl{1} = appl;
+fem.frame = {'ref'};
+fem.border = 1;
+clear units;
+units.basesystem = 'SI';
+fem.units = units;
+fem=multiphysics(fem);
+
+
+%========================================================
+%  Solve
+%========================================================
+if( bSolve )
+  fem.xmesh=meshextend(fem);
+  fem.sol=femstatic(fem, 'solcomp',{'w','u','v'}, 'outcomp',{'w','u','v'}, ...
+                  'pname','freq_smsld', 'plist',[freq], 'oldcomp',{}, ...
+                  'linsolver','spooles');
+  fem0=fem;
+  xb = zeros(size(outBnds));
+  pb = [xb;xb;outBnds];
+  w = postinterp( fem, 'w', pb );
+end
+
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/test.m",".m","1893","83","function test
+
+  %n_mass = 2*10;
+  %n_spring = n_mass + 1;
+  % period = [2 3 4 5 8];
+  % for ii=1:length(period)
+  %   figure;
+  %   plotLayers(n_spring, period(ii));
+  % end
+  
+  n_mass = 2*51;
+  n_spring = n_mass + 1;
+  ratio = [0.1 0.5 0.75 1 1.5 2 10];
+  n_layer = 7;
+  for ii=1:length(ratio)
+    figure;
+    plotLayersU(ii, n_spring, n_layer, ratio(ii));
+  end
+
+function plotLayers(n_spring, period)
+
+  stiff = true;
+  count = 1;
+  for i=1:n_spring-1
+    x0 = (i-1);
+    x1 = i;
+    if (mod(count, period) == 0) 
+      if (stiff == true)
+        stiff = false;
+        count = 1;
+      else
+        stiff = true;
+        count = 1;
+      end
+    end
+    count = count + 1;
+    if (stiff == true)
+      plot([x0 x1 x1 x0 x0], [0 0 1 1 0], 'k-', 'LineWidth', 2); hold on;
+    else
+      plot([x0 x1 x1 x0 x0], [0 0 0.5 0.5 0], 'k-', 'LineWidth', 2); hold on;
+    end
+  end
+  x0 = (n_spring-1);
+  x1 = n_spring;
+  plot([x0 x1 x1 x0 x0], [0 0 1 1 0], 'k-', 'LineWidth', 2); hold on;
+  axis equal
+  axis off
+
+  filenum = num2str(period);
+  filename = strcat('LayerElasBar',filenum,'.eps');
+  print(gcf, '-depsc', filename);
+  
+function plotLayersU(plotnum, n_spring, n_layer, ratio_stiff_soft)
+
+  N = n_spring/n_layer;
+
+  n_stiff = ratio_stiff_soft/(1+ratio_stiff_soft)*N;
+  n_soft = n_spring - n_stiff;
+
+  count = 0;
+  for ii=1:n_spring-1
+    x0 = (ii-1);
+    x1 = ii;
+    count = count + 1;
+    if (count < n_stiff)
+      plot([x0 x1 x1 x0 x0], [0 0 1 1 0], 'k-', 'LineWidth', 2); hold on;
+    else
+      plot([x0 x1 x1 x0 x0], [0 0 0.5 0.5 0], 'k-', 'LineWidth', 2); hold on;
+    end
+    if (count > N)
+      count = 0;
+    end
+  end
+  x0 = (n_spring-1);
+  x1 = n_spring;
+  plot([x0 x1 x1 x0 x0], [0 0 1 1 0], 'k-', 'LineWidth', 2); hold on;
+  axis equal
+  axis off
+  
+  filenum = num2str(plotnum);
+  filename = strcat('LayerElasBarRatio',filenum,'.eps');
+  print(gcf, '-depsc', filename);
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs5.m",".m","3590","198","function Springs5
+
+  ii=10;
+  %for ii=2:4:20
+    Springs5Plot(ii);
+  %end
+%
+% n = number of masses
+%
+function Springs5Plot(n)
+
+  L = 0.05;
+  R = 0.05;
+  A = pi*R^2;
+  A = 1;
+  l = L/n;
+  E_stiff = 70e5;
+  E_soft = 1e5;
+  rho_stiff = 3000;
+  rho_soft = 1200;
+  K_stiff = E_stiff*A/l
+  K_soft = E_soft*A/l
+  K_stiff = K_stiff*(1+0.1*i);
+  K_soft = K_soft*(1+0.1*i);
+  M_stiff = rho_stiff*(A*l)
+  M_soft = rho_soft*(A*l)
+
+  k = ones(n+1,1);
+  count = 0;
+  for ii=1:n+1
+    count = count + 1;
+    if (count < 2)
+      k(ii) = K_stiff;
+    else
+      k(ii) = K_soft;
+    end
+    if (count > 10)
+      count = 0;
+    end
+  end
+  % Harmonic mean k
+  H_k = 0;
+  for ii=1:length(k)
+    H_k = H_k + 1/k(ii);
+  end
+  K0 = 1/H_k
+  
+  M0 = M_stiff;
+  m1 = ones(n,1);
+  count = 0;
+  for ii=1:n
+    count = count + 1;
+    if (count < 5)
+      m1(ii) = M_stiff;
+    else
+      m1(ii) = M_soft;
+    end
+    if (count > 10)
+      count = 0;
+    end
+  end
+
+  f_inp = 1;
+
+  omega = 1:10:2000;
+  [TL] = calcTL(K0, M0, m1, k, n, omega, f_inp);
+  [TL_direct] = calcTLDirect(K0, M0, m1, k, n, omega, f_inp);
+  [TL_mass] = calcMassTL(M0, m1, n, omega);
+
+  figure
+  plot(omega, TL); hold on;
+  plot(omega, TL_mass, 'r-');
+  plot(omega, TL_direct, 'm-');
+  %figure
+  %semilogy(omega, u_sol);
+  %figure
+  %plot(omega, m_u);
+  %plot(omega, log(abs(m_u)));
+
+function [R_tl] = calcTL(K0, M0, m, k, n, omega, f_inp)
+
+  K = zeros(n+2,n+2);
+  M = zeros(n+2,n+2);
+  K(1,1) = k(1) + K0;
+  K(1,2) = -k(1);
+  K(1,n+1) = -K0;
+  M(1,1) = M0;
+  %M(1,1) = 0;
+  for ii=1:n-1
+    alpha = k(ii)+k(ii+1);
+    beta =  m(ii);
+    K(ii+1,ii) = -k(ii);
+    K(ii+1,ii+1) = alpha;
+    K(ii+1,ii+2) = -k(ii+1);
+    M(ii+1,ii+1) = beta;
+  end
+  K(n+1,n) = -k(n);
+  K(n+1,n+1) = k(n) + 2*K0;
+  K(n+1,1) = -K0;
+  M(n+1,n+1) = m(n);
+  %M(n+1,n+1) = 0;
+
+  % Add another spring-mass
+  K(n+1,n+2) = -K0;
+  K(n+2,n+1) = -K0;
+  K(n+2,n+2) = K0;
+  M(n+2,n+2) = M0;
+  
+  K
+  M
+  [usol, omegasq] = eig(K,M);
+  sum(diag(M))
+  
+  f = zeros(n+2,1);
+  f(1) = f_inp;
+  for jj=1:length(omega)
+    uvec = zeros(n+2,1);
+    for ii=1:length(omegasq)
+      y = usol(:,ii)'*f/(omegasq(ii,ii)-omega(jj)^2);
+      uvec = uvec + usol(:,ii)*y;
+    end
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+1,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+  
+function [R_tl] = calcTLDirect(K0, M0, m, k, n, omega, f_inp)
+
+  K = zeros(n+2,n+2);
+  M = zeros(n+2,n+2);
+  K(1,1) = k(1) + K0;
+  K(1,2) = -k(1);
+  K(1,n+1) = -K0;
+  M(1,1) = M0;
+  for ii=1:n-1
+    alpha = k(ii)+k(ii+1);
+    beta =  m(ii);
+    K(ii+1,ii) = -k(ii);
+    K(ii+1,ii+1) = alpha;
+    K(ii+1,ii+2) = -k(ii+1);
+    M(ii+1,ii+1) = beta;
+  end
+  K(n+1,n) = -k(n);
+  K(n+1,n+1) = k(n) + 2*K0;
+  K(n+1,1) = -K0;
+  M(n+1,n+1) = m(n);
+  
+  % Add another spring-mass
+  K(n+1,n+2) = -K0;
+  K(n+2,n+1) = -K0;
+  K(n+2,n+2) = K0;
+  M(n+2,n+2) = M0;
+
+  K;
+  M;
+  [usol, omegasq] = eig(K,M);
+  sum(diag(M))
+  
+  f = zeros(n+2,1);
+  f(1) = f_inp;
+  for jj=1:length(omega)
+    mat = K - omega(jj)^2*M;
+    matinv = inv(mat);
+    uvec = matinv*f;
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+1,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+  
+function [R_mass] = calcMassTL(M0, m1, n, omega)
+
+  mass = M0 + M0;
+  for ii=1:n
+    mass = mass + m1(ii);
+  end
+  mass
+  for jj=1:length(omega)
+    z = i*omega(jj)*mass;
+
+    rho = 1.2;
+    c = 344;
+    R_mass(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs1.m",".m","1250","71","function Springs1
+  %k = 10^5*(1-0.001*i);
+  k = 10^5*1;
+  m = 2;
+  m0 = 1;
+  m1 = 1;
+  G = 10;
+  omega0 = sqrt(2*G/m1);
+  n = 100;
+  u = 1.0e-4;
+  f_inp = 1;
+  omega = 1:1:10000
+
+  for ii=1:length(omega)
+    m = m0 + m1*omega0^2/(omega0^2-omega(ii)^2);
+    [TL(ii), u_sol(ii), m_u(ii)] = calcTL(k, m, n, omega(ii), u, f_inp);
+    [TL_mass(ii)] = calcMassTL(m, n, omega(ii));
+  end 
+  figure
+  semilogy(omega, TL); hold on;
+  semilogy(omega, TL_mass, 'r-');
+  figure
+  semilogy(omega, u_sol);
+  figure
+  plot(omega, m_u);
+  %plot(omega, log(abs(m_u)));
+
+function [R_tl, u_sol, m_u] = calcTL(k, m, n, omega, u, f_inp)
+
+  alpha = 2*k - m*omega^2;
+
+  K = zeros(n,n);
+  K(1,1) = alpha;
+  K(1,2) = -k;
+  for ii=2:n-1
+    K(ii,ii-1) = -k;
+    K(ii,ii) = alpha;
+    K(ii,ii+1) = -k;
+  end
+  K(n,n-1) = -k;
+  K(n,n) = alpha;
+  
+  K;
+  Kinv = inv(K);
+  f = zeros(n,1);
+  f(1) = k*u;
+  f(n) = k*u;
+  
+  uvec = Kinv*f;
+  
+  m_u = m*sum(uvec)/u + 1;
+  
+  u_sol = -f_inp/(m_u*omega^2);
+  
+  v_sol = -i*omega*u_sol;
+  
+  z = f_inp/v_sol;
+  
+  rho = 1.2;
+  c = 344;
+  R_tl = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  
+function [R_mass] = calcMassTL(m, n, omega)
+
+  mass = n*m + 1;
+  z = i*omega*mass;
+
+  rho = 1.2;
+  c = 344;
+  R_mass = 20*log10(abs(1 + 0.5*z/(rho*c)));
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs3.m",".m","4959","214","function Springs3
+
+  %nval = 1;
+  nval = [2 5 7 10 20 100];
+  for ii=1:length(nval)
+    Springs3Plot(0, ii, nval(ii), [0 0 1], 1, 1);
+    Springs3Plot(1.0e-6, ii, nval(ii), [0.25 0.75 0.25], 1, 1);
+    Springs3Plot(1.0e-4, ii, nval(ii), [0.75 0.2 0.5], 1, 1);
+    Springs3Plot(1.0e-3, ii, nval(ii), [0.75 0.25 0.1], 1, 1);
+    Springs3Plot(1.0, ii, nval(ii), [0.1 0.1 0.5], 1, 1);
+  end
+%
+% n = number of masses
+%
+function Springs3Plot(K0fac, plotnum, n, color, damp, scale)
+
+  K0 = 10^9*K0fac;
+  if (damp == 1)
+    %K = 10^5*(1+0.5*i);
+    K = 10^5*(1-0.3*i);
+  else
+    K = 10^5;
+  end
+  %K_plus = 1.10*K;
+  %K_minus = 0.90*K;
+  %K_plus = 10*K;
+  %K_minus = 0.1*K;
+  %k = K_minus + (K_plus - K_minus).*rand(n+1,1);
+  k = K*ones(n+1,1);
+  
+  G = 10^4;
+  M0 = 1;
+  m0 = 1;
+  M = 1;
+  %M_plus = 1.10*M;
+  %M_minus = 0.90*M;
+  %M_plus = 10*M;
+  %M_minus = 0.1*M;
+  %m1 = M_minus + (M_plus - M_minus).*rand(n,1);
+  m1 = M*ones(n,1);
+
+  if (scale ==1)
+    k = (n+1)/2*k;
+    m0 = m0/n;
+    m1 = 1/n*m1;
+  end
+
+  u = 1.0e-4;
+  f_inp = 1;
+
+  omega = 1:1:2000;
+  %[TL_eig, M_eff_eig] = calcTLEig(K0, M0, G, m0, m1, k, n, omega, u, f_inp);
+  [TL_direct, M_eff_dir] = calcTLDirect(K0, M0, G, m0, m1, k, n, omega, u, f_inp);
+  [TL_mass] = calcMassTL(M0, m0, m1, n, omega);
+
+  figure(2*plotnum-1);
+  p0 = plot(omega, TL_mass, 'r--'); hold on;
+  %p1 = plot(omega, TL_eig); 
+  p2 = plot(omega, TL_direct, 'm-');
+  set(p0, 'LineWidth', 3, 'Color', [1 0 0]); 
+  %set(p1, 'LineWidth', 3, 'Color', [0.75 0.25 0.25]);
+  set(p2, 'LineWidth', 3, 'Color', color);
+  xlabel('Frequency', 'FontSize', 16);
+  ylabel('Transmission loss (db)', 'FontSize', 16);
+  set(gca, 'LineWidth', 2, 'FontSize', 16);
+  axis square
+  grid on
+
+  filenum = num2str(n);
+  if (damp == 1)
+    filename = strcat('TLElasBarDamp',filenum,'.eps');
+  else
+    filename = strcat('TLElasBar',filenum,'.eps');
+  end
+  print(gcf, '-depsc', filename);
+
+  figure(2*plotnum);
+  %p3 = plot(omega, M_eff_eig/(2*M0)); hold on;
+  p4 = plot(omega, M_eff_dir/(2*M0)); hold on;
+  %set(p3, 'LineWidth', 3, 'Color', [0.75 0.25 0.25]);
+  set(p4, 'LineWidth', 3, 'Color', color); 
+  xlabel('Frequency', 'FontSize', 16);
+  ylabel('Effective mass (M_{eff}/M_0)', 'FontSize', 16);
+  set(gca, 'LineWidth', 2, 'FontSize', 16);
+  set(gca, 'YLim', [-5 5]);
+  axis square
+  grid on
+
+  if (damp == 1)
+    filename = strcat('EffMElasBarDamp',filenum,'.eps');
+  else
+    filename = strcat('EffMElasBar',filenum,'.eps');
+  end
+  print(gcf, '-depsc', filename);
+
+function [R_tl, M_eff] = calcTLEig(K0, M0, G, m0, m1, k, n, omega, u, f_inp)
+
+  for jj=1:n
+    omega0 = sqrt(2*G/m1(jj));
+    %m(jj) = m0 + (omega0^2/(omega0^2-omega^2))*m1(jj);
+    m(jj) = m0 + m1(jj);
+  end
+
+  K = zeros(n+2,n+2);
+  M = zeros(n+2,n+2);
+  K(1,1) = (k(1) + K0);
+  K(1,2) = -k(1);
+  K(1,n+2) = -K0;
+  M(1,1) = M0;
+  for ii=1:n
+    alpha = k(ii)+k(ii+1);
+    beta =  m(ii);
+    K(ii+1,ii) = -k(ii);
+    K(ii+1,ii+1) = alpha;
+    K(ii+1,ii+2) = -k(ii+1);
+    M(ii+1,ii+1) = beta;
+  end
+  K(n+2,n+1) = -k(n+1);
+  K(n+2,n+2) = k(n+1) + K0;
+  K(n+2,1) = -K0;
+  M(n+2,n+2) = M0;
+  
+  K
+  M
+  [usol, omegasq] = eig(K,M);
+  sum(diag(M))
+  
+  f = zeros(n+2,1);
+  f(1) = f_inp;
+  for jj=1:length(omega)
+    uvec = zeros(n+2,1);
+    for ii=1:length(omegasq)
+      y = usol(:,ii)'*f/(omegasq(ii,ii)-omega(jj)^2);
+      uvec = uvec + usol(:,ii)*y;
+    end
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+2,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+    M_eff(jj) = -i*f_inp/(v_sol*omega(jj));
+  end
+  
+function [R_tl, M_eff] = calcTLDirect(K0, M0, G, m0, m1, k, n, omega, u, f_inp)
+
+  for jj=1:length(omega)
+    for kk=1:n
+      omega0 = sqrt(2*G/m1(kk));
+      %m(kk) = m0 + (omega0^2/(omega0^2-omega(jj)^2))*m1(kk);
+      m(kk) = m0 + m1(kk);
+    end
+
+    K = zeros(n+2,n+2);
+    M = zeros(n+2,n+2);
+    K(1,1) = (k(1) + K0);
+    K(1,2) = -k(1);
+    K(1,n+2) = -K0;
+    M(1,1) = M0;
+    for ii=1:n
+      alpha = k(ii)+k(ii+1);
+      beta =  m(ii);
+      K(ii+1,ii) = -k(ii);
+      K(ii+1,ii+1) = alpha;
+      K(ii+1,ii+2) = -k(ii+1);
+      M(ii+1,ii+1) = beta;
+    end
+    K(n+2,n+1) = -k(n+1);
+    K(n+2,n+2) = k(n+1) + K0;
+    K(n+2,1) = -K0;
+    M(n+2,n+2) = M0;
+  
+    %K
+    %M
+    [usol, omegasq] = eig(K,M);
+    sum(diag(M))
+  
+    f = zeros(n+2,1);
+    f(1) = f_inp;
+    mat = K - omega(jj)^2*M;
+    matinv = inv(mat);
+    uvec = matinv*f;
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+2,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+    M_eff(jj) = -i*f_inp/(v_sol*omega(jj));
+  end
+  
+function [R_mass] = calcMassTL(M0, m0, m1, n, omega)
+
+  mass = 2*M0;
+  for ii=1:n
+    mass = mass + m0 + m1(ii);
+  end
+  mass
+  for jj=1:length(omega)
+    z = i*omega(jj)*mass;
+
+    rho = 1.2;
+    c = 344;
+    R_mass(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs7.m",".m","4526","185","function Springs7
+
+  nval = 51;
+  n_layer = 7;
+  ratio = [0.1 0.5 0.75 1 1.5 2 10];
+  %Kfac = [1.0e-5 1.0e-3 1.0];
+  %for jj = 1:length(Kfac)
+  %  Springs7Plot(jj, 2*nval, n_layer, 0.1, Kfac(jj), 1.0, [0 0 1], 1);
+  %  Springs7Plot(jj, 2*nval, n_layer, 0.5, Kfac(jj), 1.0, [0.25 0.75 0.25], 1);
+  %  Springs7Plot(jj, 2*nval, n_layer, 0.75, Kfac(jj), 1.0, [0.75 0.25 0.1], 1);
+  %  Springs7Plot(jj, 2*nval, n_layer, 2.0, Kfac(jj), 1.0, [0.1 0.1 0.5], 1);
+  %  Springs7Plot(jj, 2*nval, n_layer, 10.0, Kfac(jj), 1.0, [0.9 0.1 0.3], 1);
+  %end
+  rhofac = [0.5 1 3];
+  for jj = 1:length(rhofac)
+    Springs7Plot(jj, 2*nval, n_layer, 0.1, 1.0, rhofac(jj), [0 0 1], 1);
+    Springs7Plot(jj, 2*nval, n_layer, 0.5, 1.0, rhofac(jj), [0.25 0.75 0.25], 1);
+    Springs7Plot(jj, 2*nval, n_layer, 0.75, 1.0, rhofac(jj), [0.75 0.25 0.1], 1);
+    Springs7Plot(jj, 2*nval, n_layer, 2.0, 1.0, rhofac(jj), [0.1 0.1 0.5], 1);
+    Springs7Plot(jj, 2*nval, n_layer, 10.0, 1.0, rhofac(jj), [0.9 0.1 0.3], 1);
+  end
+
+%
+% n = number of masses
+%
+function Springs7Plot(plotnum, n, n_layer, ratio_stiff_soft, Kfac, rhofac, color, damp)
+
+  L = 0.05;
+  R = 0.05;
+  A = pi*R^2;
+  l = L/(n+1);
+  E_stiff = 70e9*Kfac;
+  E_soft = 1e5;
+  rho_stiff = 3000*rhofac;
+  rho_soft = 1200;
+  K_stiff = E_stiff*A/l
+  K_soft = E_soft*A/l
+  if (damp == 1)
+    K_stiff = K_stiff*(1+0.1*i);
+    K_soft = K_soft*(1+0.1*i);
+  else
+    K_stiff = K_stiff;
+    K_soft = K_soft;
+  end
+  M_stiff = rho_stiff*(A*l);
+  M_soft = rho_soft*(A*l);
+
+  n_mass = n;
+  n_spring = n_mass + 1;
+
+  k = ones(n+1,1);
+  m = ones(n,1);
+
+  N = n_spring/n_layer;
+  n_stiff = ratio_stiff_soft/(1+ratio_stiff_soft)*N;
+  n_soft = n_spring - n_stiff;
+
+  count = 0;
+  for ii=1:n_spring-1
+    count = count + 1;
+    if (count < n_stiff)
+      k(ii) = K_stiff;
+      m(ii) = M_stiff;
+    else
+      k(ii) = K_soft;
+      m(ii) = M_soft;
+    end
+    if (count > N)
+      count = 0;
+    end
+  end
+  k(n_spring) = K_stiff;
+
+  % Harmonic mean k
+  H_k = 0;
+  for ii=1:length(k)
+    H_k = H_k + 1/k(ii);
+  end
+  K0 = 1/H_k
+
+  M0 = M_stiff;
+
+  f_inp = 1;
+
+  omega = 1:20:8000;
+  [TL_direct, M_eff_dir] = calcTLDirect(K0, M0, m, k, n, omega, f_inp);
+  [TL_mass] = calcMassTL(M0, m, n, omega);
+
+  figure(plotnum);
+  p0 = plot(omega/(2*pi), TL_mass, 'r--'); hold on;
+  p2 = plot(omega/(2*pi), TL_direct, 'm-');
+  set(p0, 'LineWidth', 3, 'Color', color); 
+  set(p2, 'LineWidth', 3, 'Color', color);
+  xlabel('Frequency (Hz)', 'FontSize', 16);
+  ylabel('Transmission loss (db)', 'FontSize', 16);
+  set(gca, 'LineWidth', 2, 'FontSize', 16);
+  axis square
+  grid on
+
+  filenum = num2str(n);
+  if (damp == 1)
+    filename = strcat('TLLayerRatioElasBarDampRho',filenum,'-',num2str(plotnum), '.eps');
+  else
+    filename = strcat('TLLayerRatioElasBarRho',filenum,'-',num2str(plotnum),'.eps');
+  end
+  print(gcf, '-depsc', filename);
+
+  %figure(2*plotnum);
+  %p4 = plot(omega, M_eff_dir/(2*M0)); hold on;
+  %set(p4, 'LineWidth', 3, 'Color', color); 
+  %xlabel('Frequency', 'FontSize', 16);
+  %ylabel('Effective mass (M_{eff}/M_0)', 'FontSize', 16);
+  %set(gca, 'LineWidth', 2, 'FontSize', 16);
+  %set(gca, 'YLim', [-5 5]);
+  %axis square
+  %grid on
+%
+  %if (damp == 1)
+  %  filename = strcat('EffMLayerElasBarDamp',filenum,'.eps');
+  %else
+  %  filename = strcat('EffMLayerElasBar',filenum,'.eps');
+  %end
+  %print(gcf, '-depsc', filename);
+
+  
+function [R_tl, M_eff] = calcTLDirect(K0, M0, m, k, n, omega, f_inp)
+
+  for jj=1:length(omega)
+
+    K = zeros(n+2,n+2);
+    M = zeros(n+2,n+2);
+    K(1,1) = (k(1) + K0);
+    K(1,2) = -k(1);
+    K(1,n+2) = -K0;
+    M(1,1) = M0;
+    for ii=1:n
+      alpha = k(ii)+k(ii+1);
+      beta =  m(ii);
+      K(ii+1,ii) = -k(ii);
+      K(ii+1,ii+1) = alpha;
+      K(ii+1,ii+2) = -k(ii+1);
+      M(ii+1,ii+1) = beta;
+    end
+    K(n+2,n+1) = -k(n+1);
+    K(n+2,n+2) = k(n+1) + K0;
+    K(n+2,1) = -K0;
+    M(n+2,n+2) = M0;
+  
+    %K
+    %M
+    [usol, omegasq] = eig(K,M);
+    %sum(diag(M))
+  
+    f = zeros(n+2,1);
+    f(1) = f_inp;
+    mat = K - omega(jj)^2*M;
+    matinv = inv(mat);
+    uvec = matinv*f;
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+2,1);
+    v_sol = i*omega(jj)*u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+    M_eff(jj) = -i*f_inp/(v_sol*omega(jj));
+  end
+  
+function [R_mass] = calcMassTL(M0, m, n, omega)
+
+  mass = 2*M0;
+  for ii=1:n
+    mass = mass + m(ii);
+  end
+  %mass
+  for jj=1:length(omega)
+    z = i*omega(jj)*mass;
+
+    rho = 1.2;
+    c = 344;
+    R_mass(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs6.m",".m","4795","213","function Springs6
+
+  nval = 10;
+  %Kfac = [1.0e-4 1.0e-2 1.0];
+  %for jj = 1:length(Kfac)
+  %  Springs6Plot(jj, 2*nval, 2, Kfac(jj), 1.0, [0 0 1], 1);
+  %  Springs6Plot(jj, 2*nval, 3, Kfac(jj), 1.0, [0.25 0.75 0.25], 1);
+  %  Springs6Plot(jj, 2*nval, 4, Kfac(jj), 1.0, [0.75 0.25 0.1], 1);
+  %  Springs6Plot(jj, 2*nval, 5, Kfac(jj), 1.0, [0.1 0.1 0.5], 1);
+  %  Springs6Plot(jj, 2*nval, 8, Kfac(jj), 1.0, [0.9 0.1 0.3], 1);
+  %end
+  rhofac = [0.5 1 3];
+  for jj = 1:length(rhofac)
+    Springs6Plot(jj, 2*nval, 2, 1.0, rhofac(jj), [0 0 1], 1);
+    Springs6Plot(jj, 2*nval, 3, 1.0, rhofac(jj), [0.25 0.75 0.25], 1);
+    Springs6Plot(jj, 2*nval, 4, 1.0, rhofac(jj), [0.75 0.25 0.1], 1);
+    Springs6Plot(jj, 2*nval, 5, 1.0, rhofac(jj), [0.1 0.1 0.5], 1);
+    Springs6Plot(jj, 2*nval, 8, 1.0, rhofac(jj), [0.9 0.1 0.3], 1);
+  end
+
+%
+% n = number of masses
+%
+function Springs6Plot(plotnum, n, period, Kfac, rhofac, color, damp)
+
+  L = 0.05;
+  R = 0.05;
+  A = pi*R^2;
+  l = L/(n+1);
+  E_stiff = 70e9*Kfac;
+  E_soft = 1e5;
+  rho_stiff = 3000*rhofac;
+  rho_soft = 1200;
+  K_stiff = E_stiff*A/l
+  K_soft = E_soft*A/l
+  if (damp == 1)
+    K_stiff = K_stiff*(1+0.1*i);
+    K_soft = K_soft*(1+0.1*i);
+  else
+    K_stiff = K_stiff;
+    K_soft = K_soft;
+  end
+  M_stiff = rho_stiff*(A*l);
+  M_soft = rho_soft*(A*l);
+
+  n_mass = n;
+  n_spring = n_mass + 1;
+
+  k = ones(n+1,1);
+  m = ones(n,1);
+  stiff = true;
+  count = 1;
+  for ii=1:n_spring-1
+    if (mod(count, period) == 0) 
+      if (stiff == true)
+        stiff = false;
+        count = 1;
+      else
+        stiff = true;
+        count = 1;
+      end
+    end
+    count = count + 1;
+    if (stiff == true)
+      k(ii) = K_stiff;
+      m(ii) = M_stiff;
+    else
+      k(ii) = K_soft;
+      m(ii) = M_soft;
+    end
+  end
+  k(n_spring) = K_stiff;
+
+  % Harmonic mean k
+  H_k = 0;
+  for ii=1:length(k)
+    H_k = H_k + 1/k(ii);
+  end
+  K0 = 1/H_k
+
+  M0 = M_stiff;
+
+  % k = ones(n+1,1);
+  % count = 0;
+  % for ii=1:n+1
+  %   count = count + 1;
+  %   if (count < 2)
+  %     k(ii) = K_stiff;
+  %   else
+  %     k(ii) = K_soft;
+  %   end
+  %   if (count > 10)
+  %     count = 0;
+  %   end
+  % end
+
+  % m = ones(n,1);
+  % count = 0;
+  % for ii=1:n
+  %   count = count + 1;
+  %   if (count < 2)
+  %     m(ii) = M_stiff;
+  %   else
+  %     m(ii) = M_soft;
+  %   end
+  %   if (count > 10)
+  %     count = 0;
+  %   end
+  % end
+
+  f_inp = 1;
+
+  omega = 1:20:8000;
+  [TL_direct, M_eff_dir] = calcTLDirect(K0, M0, m, k, n, omega, f_inp);
+  [TL_mass] = calcMassTL(M0, m, n, omega);
+
+  figure(plotnum);
+  p0 = plot(omega, TL_mass, 'r--'); hold on;
+  p2 = plot(omega, TL_direct, 'm-');
+  set(p0, 'LineWidth', 3, 'Color', color); 
+  set(p2, 'LineWidth', 3, 'Color', color);
+  xlabel('Frequency', 'FontSize', 16);
+  ylabel('Transmission loss (db)', 'FontSize', 16);
+  set(gca, 'LineWidth', 2, 'FontSize', 16);
+  axis square
+  grid on
+
+  filenum = num2str(n);
+  if (damp == 1)
+    filename = strcat('TLLayerElasBarDampRho',filenum,'-',num2str(plotnum), '.eps');
+  else
+    filename = strcat('TLLayerElasBarRho',filenum,'-',num2str(plotnum),'.eps');
+  end
+  print(gcf, '-depsc', filename);
+
+  %figure(2*plotnum);
+  %p4 = plot(omega, M_eff_dir/(2*M0)); hold on;
+  %set(p4, 'LineWidth', 3, 'Color', color); 
+  %xlabel('Frequency', 'FontSize', 16);
+  %ylabel('Effective mass (M_{eff}/M_0)', 'FontSize', 16);
+  %set(gca, 'LineWidth', 2, 'FontSize', 16);
+  %set(gca, 'YLim', [-5 5]);
+  %axis square
+  %grid on
+%
+  %if (damp == 1)
+  %  filename = strcat('EffMLayerElasBarDamp',filenum,'.eps');
+  %else
+  %  filename = strcat('EffMLayerElasBar',filenum,'.eps');
+  %end
+  %print(gcf, '-depsc', filename);
+
+  
+function [R_tl, M_eff] = calcTLDirect(K0, M0, m, k, n, omega, f_inp)
+
+  for jj=1:length(omega)
+
+    K = zeros(n+2,n+2);
+    M = zeros(n+2,n+2);
+    K(1,1) = (k(1) + K0);
+    K(1,2) = -k(1);
+    K(1,n+2) = -K0;
+    M(1,1) = M0;
+    for ii=1:n
+      alpha = k(ii)+k(ii+1);
+      beta =  m(ii);
+      K(ii+1,ii) = -k(ii);
+      K(ii+1,ii+1) = alpha;
+      K(ii+1,ii+2) = -k(ii+1);
+      M(ii+1,ii+1) = beta;
+    end
+    K(n+2,n+1) = -k(n+1);
+    K(n+2,n+2) = k(n+1) + K0;
+    K(n+2,1) = -K0;
+    M(n+2,n+2) = M0;
+  
+    %K
+    %M
+    [usol, omegasq] = eig(K,M);
+    sum(diag(M))
+  
+    f = zeros(n+2,1);
+    f(1) = f_inp;
+    mat = K - omega(jj)^2*M;
+    matinv = inv(mat);
+    uvec = matinv*f;
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+2,1);
+    v_sol = i*omega(jj)*u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+    M_eff(jj) = -i*f_inp/(v_sol*omega(jj));
+  end
+  
+function [R_mass] = calcMassTL(M0, m, n, omega)
+
+  mass = 2*M0;
+  for ii=1:n
+    mass = mass + m(ii);
+  end
+  mass
+  for jj=1:length(omega)
+    z = i*omega(jj)*mass;
+
+    rho = 1.2;
+    c = 344;
+    R_mass(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs2.m",".m","4362","183","function Springs
+
+  seed = rand('twister');
+  ii = 1;
+  nval = [2 5 7 10 20 100];
+%  for ii=1:length(nval)
+%    figure
+%    Springs2Plot(seed, ii, nval(ii), 10, 1, [0.6 0.2 0.2], 0, 0, 1);
+%    Springs2Plot(seed, ii, nval(ii), 1, 1, [0 0 1], 0, 0, 1);
+%    Springs2Plot(seed, ii, nval(ii), 0.1, 1, [.25 .75 .25], 0, 0, 1);
+%  end
+%  for ii=1:length(nval)
+%    figure
+%    Springs2Plot(seed, ii, nval(ii), 1, 10, [0.6 0.2 0.2], 0, 0, 1);
+%    Springs2Plot(seed, ii, nval(ii), 1, 1, [0 0 1], 0, 0, 1);
+%    Springs2Plot(seed, ii, nval(ii), 1, 0.1, [.25 .75 .25], 0, 0, 1);
+%  end
+%  for ii=1:length(nval)
+%    figure
+%    %Springs2Plot(seed, ii, nval(ii), 1, 1, [0.0 0.0 1.0], 1, 0, 1);
+%    %Springs2Plot(seed, ii, nval(ii), 1, 1, [0.25 0.75 0.25], 0, 0, 1);
+%    Springs2Plot(seed, ii, nval(ii), 1, 1, [0.0 0.0 1.0], 0, 1, 1);
+%    Springs2Plot(seed, ii, nval(ii), 1, 1, [0.25 0.75 0.25], 0, 0, 1);
+%  end
+  for ii=1:length(nval)
+    figure
+    Springs2Plot(seed, ii, nval(ii), 1, 1, [0.0 0.0 1.0], 0, 0, 0);
+  end
+%
+% n = number of masses
+%
+function Springs2Plot(seed, plotnum, n, Kfac, Mfac, color, varyK, varyM, scale)
+
+  %K = 10^5*(1-0.5*i)*Kfac;
+  %K = 10^5*(1-0.3*i)*Kfac;
+  K = 10^5*Kfac;
+  if (varyK == 1) 
+    K_plus = 2.00*K;
+    K_minus = 0.1*K;
+    %K_plus = 10*K;
+    %K_minus = 0.1*K;
+    rand('twister', seed);
+    k = K_minus + (K_plus - K_minus).*rand(n+1,1);
+  else
+    k = K*ones(n+1,1);
+  end
+
+  % Scale
+  if (scale ==1)
+    k = (n+1)/2*k;
+  end
+  
+  G = 0.1*K;
+  M0 = 2*Mfac;
+  m0 = 1*Mfac;
+  M = 1*Mfac;
+  if (varyM == 1)
+    %M_plus = 1.10*M;
+    %M_minus = 0.90*M;
+    M_plus = 3*M;
+    M_minus = 0.01*M;
+    rand('twister', seed);
+    m1 = M_minus + (M_plus - M_minus).*rand(n,1);
+  else
+    m1 = M*ones(n,1);
+  end
+
+  % Scale
+  if (scale ==1)
+    m0 = m0/n;
+    m1 = 1/n*m1;
+  end
+
+  u = 1.0;
+  f_inp = 1;
+  %omega = 1:0.01:100;
+  omega = 1:10:2000;
+  %omega = 1:10:3000;
+  %omega = 1:10:5000;
+  %omega = [1 100];
+
+  for ii=1:length(omega)
+    [TL(ii), u_sol(ii), m_u(ii)] = calcTL(M0, G, m0, m1, k, n, omega(ii), u, f_inp);
+    [TL_mass(ii)] = calcMassTL(M0, m0, m1, n, omega(ii));
+  end 
+  figure(plotnum)
+  p1 = plot(omega, TL); hold on;
+  p2 = plot(omega, TL_mass, 'r--');
+  set(p1, 'LineWidth', 3, 'Color', color);
+  set(p2, 'LineWidth', 3);
+  %set(p2, 'LineWidth', 3, 'Color', color);
+  xlabel('Frequency', 'FontSize', 16);
+  ylabel('Transmission loss (db)', 'FontSize', 16);
+  set(gca, 'LineWidth', 2, 'FontSize', 16);
+  %set(gca, 'YLim', [0 20]);
+  grid on
+  axis square
+  filenum = num2str(n);
+  filename = strcat('TLSpringMassNoScale',filenum,'.eps');
+  print(gcf, '-depsc', filename);
+  %print -depsc TLSpringMass.eps
+  %figure
+  %semilogy(omega, u_sol);
+  %figure
+  %p3 = plot(omega, m_u/M0);
+  %set(p3, 'LineWidth', 3, 'Color', color); hold on;
+  %xlabel('Frequency', 'FontSize', 16);
+  %ylabel('Effective mass (M_{eff}/M_0)', 'FontSize', 16);
+  %set(gca, 'LineWidth', 2, 'FontSize', 16);
+  %set(gca, 'YLim', [-5 5]);
+  %axis square
+  %grid on
+  %filenum = num2str(n);
+  %filename = strcat('EffMassStiff',filenum,'.eps');
+  %print(gcf, '-depsc', filename);
+  %print -depsc EffMass.eps
+  %plot(omega, log(abs(m_u)));
+
+function [R_tl, u_sol, m_u] = calcTL(M0, G, m0, m1, k, n, omega, u, f_inp)
+
+  for jj=1:n
+    omega0 = sqrt(2*G/m1(jj));
+    %m(jj) = m0 + (omega0^2/(omega0^2-omega^2))*m1(jj);
+    m(jj) = m0 + m1(jj);
+  end
+
+  K = zeros(n,n);
+  f = zeros(n,1);
+
+  alpha = k(1)+k(2) - m(1)*omega^2;
+  if (n == 1)
+    K(1,1) = alpha;
+    f(1) = (k(1)+k(n+1))*u;
+  else
+    K(1,1) = alpha;
+    K(1,2) = -k(2);
+    for ii=2:n-1
+      alpha = k(ii)+k(ii+1) - m(ii)*omega^2;
+      K(ii,ii-1) = -k(ii);
+      K(ii,ii) = alpha;
+      K(ii,ii+1) = -k(ii+1);
+    end
+    %k(n+1) = k(n+1)/2;
+    alpha = k(n)+k(n+1) - m(n)*omega^2;
+    K(n,n-1) = -k(n);
+    K(n,n) = alpha;
+    f(1) = k(1)*u;
+    f(n) = k(n+1)*u;
+    %f(n) = k(n+1)*(-u);
+    %f(n) = 0;
+  end
+  
+  Kinv = inv(K);
+  
+  uvec = Kinv*f;
+  
+  m_u = M0;
+  for ii=1:n
+    m_u = m_u + m(ii)*uvec(ii)/u;
+  end
+  
+  u_sol = -f_inp/(m_u*omega^2);
+  
+  v_sol = -i*omega*u_sol;
+  
+  z = f_inp/v_sol;
+  
+  rho = 1.2;
+  c = 344;
+  R_tl = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  
+function [R_mass] = calcMassTL(M0, m0, m1, n, omega)
+
+  mass = M0;
+  for ii=1:n
+    mass = mass + m0 + m1(ii);
+  end
+  z = i*omega*mass;
+
+  rho = 1.2;
+  c = 344;
+  R_mass = 20*log10(abs(1 + 0.5*z/(rho*c)));
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","SpringModel/Springs4.m",".m","3174","168","function Springs4
+
+  ii=4;
+  for ii=2:4:20
+    Springs4Plot(ii);
+  end
+%
+% n = number of masses
+%
+function Springs4Plot(n)
+
+  K0 = 10^16;
+  %K = 10^5*(1+0.01*i);
+  K = 10^5;
+  %K_plus = 1.10*K;
+  %K_minus = 0.90*K;
+  %K_plus = 10*K;
+  %K_minus = 0.1*K;
+  %k = K_minus + (K_plus - K_minus).*rand(n+1,1);
+  k = K*ones(n+1,1);
+  
+  G = 10;
+  M0 = 1;
+  %m0 = 1;
+  m0 = 0;
+  M = 1;
+  %M_plus = 1.10*M;
+  %M_minus = 0.90*M;
+  %M_plus = 10*M;
+  %M_minus = 0.1*M;
+  %m1 = M_minus + (M_plus - M_minus).*rand(n,1);
+  m1 = M*ones(n,1);
+
+  u = 1.0e-4;
+  f_inp = 1;
+
+  omega = 1:10:2000;
+  [TL] = calcTL(K0, M0, G, m0, m1, k, n, omega, u, f_inp);
+  [TL_direct] = calcTLDirect(K0, M0, G, m0, m1, k, n, omega, u, f_inp);
+  [TL_mass] = calcMassTL(M0, m0, m1, n, omega);
+
+  figure
+  plot(omega, TL); hold on;
+  plot(omega, TL_mass, 'r-');
+  plot(omega, TL_direct, 'm-');
+  %figure
+  %semilogy(omega, u_sol);
+  %figure
+  %plot(omega, m_u);
+  %plot(omega, log(abs(m_u)));
+
+function [R_tl] = calcTL(K0, M0, G, m0, m1, k, n, omega, u, f_inp)
+
+  for jj=1:n
+    omega0 = sqrt(2*G/m1(jj));
+    m(jj) = m0 + m1(jj);
+  end
+
+  K = zeros(n+1,n+1);
+  M = zeros(n+1,n+1);
+  K(1,1) = k(1) + K0;
+  K(1,2) = -k(1);
+  K(1,n+1) = -K0;
+  M(1,1) = M0;
+  %M(1,1) = 0;
+  for ii=1:n-1
+    alpha = k(ii)+k(ii+1);
+    beta =  m(ii);
+    K(ii+1,ii) = -k(ii);
+    K(ii+1,ii+1) = alpha;
+    K(ii+1,ii+2) = -k(ii+1);
+    M(ii+1,ii+1) = beta;
+  end
+  K(n+1,n) = -k(n);
+  K(n+1,n+1) = k(n) + K0;
+  K(n+1,1) = -K0;
+  M(n+1,n+1) = m(n);
+  %M(n+1,n+1) = 0;
+  
+  K
+  M
+  [usol, omegasq] = eig(K,M);
+  sum(diag(M))
+  
+  f = zeros(n+1,1);
+  f(1) = f_inp;
+  for jj=1:length(omega)
+    uvec = zeros(n+1,1);
+    for ii=1:length(omegasq)
+      y = usol(:,ii)'*f/(omegasq(ii,ii)-omega(jj)^2);
+      uvec = uvec + usol(:,ii)*y;
+    end
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+1,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+  
+function [R_tl] = calcTLDirect(K0, M0, G, m0, m1, k, n, omega, u, f_inp)
+
+  for jj=1:n
+    omega0 = sqrt(2*G/m1(jj));
+    m(jj) = m0 + m1(jj);
+  end
+
+  K = zeros(n+1,n+1);
+  M = zeros(n+1,n+1);
+  K(1,1) = k(1) + K0;
+  K(1,2) = -k(1);
+  K(1,n+1) = -K0;
+  M(1,1) = M0;
+  for ii=1:n-1
+    alpha = k(ii)+k(ii+1);
+    beta =  m(ii);
+    K(ii+1,ii) = -k(ii);
+    K(ii+1,ii+1) = alpha;
+    K(ii+1,ii+2) = -k(ii+1);
+    M(ii+1,ii+1) = beta;
+  end
+  K(n+1,n) = -k(n);
+  K(n+1,n+1) = k(n) + K0;
+  K(n+1,1) = -K0;
+  M(n+1,n+1) = m(n);
+  
+  K
+  M
+  [usol, omegasq] = eig(K,M);
+  sum(diag(M))
+  
+  f = zeros(n+1,1);
+  f(1) = f_inp;
+  for jj=1:length(omega)
+    mat = K - omega(jj)^2*M;
+    matinv = inv(mat);
+    uvec = matinv*f;
+  
+    u_1 = uvec(1,1);
+    u_sol = uvec(n+1,1);
+    v_sol = i*omega(jj)*u_sol;
+    %z = u_1/u_sol;
+    z = f_inp/v_sol;
+  
+    rho = 1.2;
+    c = 344;
+    R_tl(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+  
+function [R_mass] = calcMassTL(M0, m0, m1, n, omega)
+
+  mass = M0;
+  for ii=1:n
+    mass = mass + m0 + m1(ii);
+  end
+  mass
+  for jj=1:length(omega)
+    z = i*omega(jj)*mass;
+
+    rho = 1.2;
+    c = 344;
+    R_mass(jj) = 20*log10(abs(1 + 0.5*z/(rho*c)));
+  end
+","MATLAB"
+"Metamaterial","bbanerjee/MetamaterialSim","PanelModel/plot_Panel3.m",".m","5538","244","function plot_Panel3
+
+  %
+  % Panel with balls + silicone
+  %
+  mass = 0.10896;
+  nnode = 415;
+  rad = 0.5*8.5e-2;
+  fnode = 1.0e-2;
+  F = nnode*fnode;
+  %A = 0.25*pi*rad^2;
+  panel_len = 12.7e-2;
+  A = 0.25*(panel_len^2);
+  %
+  load ModalPanel3_qtr.uz72085
+  freq = ModalPanel3_qtr(:,1);
+  uz_cen = ModalPanel3_qtr(:,2);
+  load ModalPanel3_qtr.uz72535
+  uz_mid = ModalPanel3_qtr(:,2);
+  load ModalPanel3_qtr.uz72543
+  uz_cor = ModalPanel3_qtr(:,2);
+  %
+  figure
+  mode = 1;
+  plotAcc(freq, uz_cen, uz_mid, uz_cor, mode);
+  plotMassLawAccl(freq, mass, F, mode);
+  title('Balls + Silicone');
+  figure
+  plotTL(freq, uz_cen, uz_mid, uz_cor, F, A);
+  plotMassLawTL(freq, mass, A);
+  title('6 mm Balls + Silicone');
+  clear freq
+  clear uz_cen
+  clear uz_mid
+  clear uz_cor
+
+  %
+  % Panel with balls + silicone + damping
+  %
+  load ModalPanel3_qtr_damp.uz72085
+  freq = ModalPanel3_qtr_damp(:,1);
+  uz_cen = ModalPanel3_qtr_damp(:,2);
+  load ModalPanel3_qtr_damp.uz72535
+  uz_mid = ModalPanel3_qtr_damp(:,2);
+  load ModalPanel3_qtr_damp.uz72543
+  uz_cor = ModalPanel3_qtr_damp(:,2);
+  %
+  figure
+  plot(freq, uz_cen)
+  figure
+  plotAcc(freq, uz_cen, uz_mid, uz_cor, mode);
+  plotMassLawAccl(freq, mass, F, mode);
+  title('Balls + Silicone + Damping');
+  figure
+  plotTL(freq, uz_cen, uz_mid, uz_cor, F, A);
+  plotMassLawTL(freq, mass, A);
+  title('6 mm Balls + Silicone + Damping');
+  clear freq
+  clear uz_cen
+  clear uz_mid
+  clear uz_cor
+
+  %
+  % Panel with steel balls + no silicone
+  %
+  mass = 0.10559;
+  nnode = 415;
+  rad = 0.5*8.5e-2;
+  fnode = 1.0e-2;
+  F = nnode*fnode;
+  A = 0.25*pi*rad^2;
+  %
+  load ModalPanel4_qtr.uz72085
+  freq = ModalPanel4_qtr(:,1);
+  uz_cen = ModalPanel4_qtr(:,2);
+  load ModalPanel4_qtr.uz72535
+  uz_mid = ModalPanel4_qtr(:,2);
+  load ModalPanel4_qtr.uz72543
+  uz_cor = ModalPanel4_qtr(:,2);
+  %
+  % figure
+  % plotAcc(freq, uz_cen, uz_mid, uz_cor, mode);
+  % title('Steel Balls + No Silicone');
+  % figure
+  % plotTL(freq, uz_cen, uz_mid, uz_cor, F, A);
+  % plotMassLawTL(freq, mass, A);
+  % title('6 mm Steel Balls + No Silicone');
+
+  %
+  % Panel with lead balls + no silicone
+  %
+  mass = 0.11244;
+  nnode = 415;
+  rad = 0.5*8.5e-2;
+  fnode = 1.0e-2;
+  F = nnode*fnode;
+  A = 0.25*pi*rad^2;
+  %
+  load ModalPanel5_qtr.uz72085
+  freq = ModalPanel5_qtr(:,1);
+  uz_cen = ModalPanel5_qtr(:,2);
+  load ModalPanel5_qtr.uz72535
+  uz_mid = ModalPanel5_qtr(:,2);
+  load ModalPanel5_qtr.uz72543
+  uz_cor = ModalPanel5_qtr(:,2);
+  %
+  % figure
+  % plotAcc(freq, uz_cen, uz_mid, uz_cor, mode);
+  % title('6 mm Lead Balls + No Silicone');
+  % figure
+  % plotTL(freq, uz_cen, uz_mid, uz_cor, F, A);
+  % plotMassLawTL(freq, mass, A);
+  % title('Lead Balls + No Silicone');
+
+%
+% Plot acceleration
+%
+function [accl] = plotAcc(freq, u_center, u_middle, u_corner, mode)
+
+  omega = 2*pi*freq;
+  %
+  uz = u_center;
+  accl = abs(omega.^2.*uz);
+  if (mode == 1)
+    p1 = semilogy(freq, accl, 'r-', 'LineWidth', 3); hold on;
+  else
+    p1 = plot(freq, accl, 'r-', 'LineWidth', 3); hold on;
+  end
+  %
+  uz = u_middle;
+  accl = abs(omega.^2.*uz);
+  if (mode == 1)
+    p2 = semilogy(freq, accl, 'g-', 'LineWidth', 3); hold on;
+  else
+    p2 = plot(freq, accl, 'g-', 'LineWidth', 3); hold on;
+  end
+  %
+  uz = u_corner;
+  accl = abs(omega.^2.*uz);
+  if (mode == 1)
+    p3 = semilogy(freq, accl, 'b-', 'LineWidth', 3); hold on;
+  else
+    p3 = plot(freq, accl, 'b-', 'LineWidth', 3); hold on;
+  end
+  %
+  xlabel('Frequency (cycles/s)');
+  ylabel('Acceleration (m/s^2)');
+  %
+  legend([p3 p2 p1], 'Center', 'Middle', 'Corner');
+  set(gca, 'LineWidth', 3, 'FontSize', 18, 'FontName', 'times');
+  grid on;
+
+%
+% Plot mass law acceleration
+%
+function plotMassLawAccl(freq, mass, F, mode)
+
+  accl = MassLawAccl(mass, F);
+  for ii=1:length(freq)
+    aa(ii) = accl;
+  end
+  if (mode == 1)
+    p = semilogy(freq, aa, 'k--', 'LineWidth', 3); hold on;
+  else
+    p = plot(freq, aa, 'k--', 'LineWidth', 3); hold on;
+  end
+  xlabel('Frequency (cycles/s)');
+  ylabel('Accleration (m/s)');
+  set(gca, 'LineWidth', 3, 'FontSize', 18, 'FontName', 'times');
+
+%
+% Plot transmission loss
+%
+function plotTL(freq, u_center, u_middle, u_corner, F, A)
+
+  omega = 2*pi*freq;
+  %
+  uz = u_center;
+  tl = TransLoss(omega, uz, F, A);
+  p1 = plot(freq, tl, 'r-', 'LineWidth', 3); hold on;
+  %
+  uz = u_middle;
+  tl = TransLoss(omega, uz, F, A);
+  p2 = plot(freq, tl, 'g-', 'LineWidth', 3); hold on;
+  %
+  uz = u_corner;
+  tl = TransLoss(omega, uz, F, A);
+  p3 = plot(freq, tl, 'b-', 'LineWidth', 3); hold on;
+  %
+  xlabel('Frequency (cycles/s)');
+  ylabel('TL (dB)');
+  %
+  legend([p3 p2 p1], 'Center', 'Middle', 'Corner');
+  set(gca, 'LineWidth', 3, 'FontSize', 18, 'FontName', 'times');
+  grid on;
+
+%
+% Plot mass law transmission loss
+%
+function plotMassLawTL(freq, mass, A)
+
+  omega = 2*pi*freq;
+  tl_m = MassLawTL(omega, mass, A);
+  p = plot(freq, tl_m, 'k--', 'LineWidth', 3); hold on;
+  xlabel('Frequency (cycles/s)');
+  ylabel('TL (dB)');
+  set(gca, 'LineWidth', 3, 'FontSize', 18, 'FontName', 'times');
+
+%
+% Calculate transmission loss
+%   omega - frequency vector (radians/sec)
+%   a - acceleration 
+%   P - applied pressure
+%
+function [tl] = TransLoss(omega, uz, F, A )
+
+  P = F/A;
+  rho = 1.2;
+  c = 344;
+
+  v = -j*omega.*uz;
+
+  z = P./v;
+
+  tl = 20*log10(abs(1+z/(2*rho*c)));
+
+%
+% Calculate mass law transmission loss
+%
+function [tl_m] = MassLawTL(omega, mass, A)
+
+  rho = 1.2;
+  c = 344;
+  z = j*omega*mass/A;
+  tl_m = 20*log10(abs(1+z/(2*rho*c)) );
+
+%
+% Calculate mass law acceleration
+%
+function [accl] = MassLawAccl(mass, F)
+
+  accl = F/mass;
+
+","MATLAB"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","FEM simulation/FEM_simulation.m",".m","8049","142","function out = FEM_simulation
+%
+% Model.m
+%
+% Model exported on Jul 4 2020, 11:16 by COMSOL 5.5.0.359.
+
+import com.comsol.model.*
+import com.comsol.model.util.*
+
+load('G:\structure\unit_cell_data.mat','unit_cell_data')
+
+for i=1:length(unit_cell_data)
+
+unit_cell = find(flipud(unit_cell_data{1,i})==0);
+    
+model = ModelUtil.create('Model');
+
+model.modelPath('G:\2020\Test');
+
+model.param.set('l', '0.001');
+model.param.set('n', '50');
+model.param.set('a', 'n*l');
+model.param.set('k1', '0');
+model.param.set('kx', 'k1*pi/a');
+model.param.set('k2', '0');
+model.param.set('ky', 'k2*pi/a');
+model.param.set('ela', '1.7e9');
+model.param.set('poi', '0.4');
+model.param.set('den', '1150');
+model.param.set('ct', 'sqrt(ela/2/(1+poi)/den)');
+
+model.component.create('comp1', true);
+
+model.component('comp1').geom.create('geom1', 2);
+
+model.component('comp1').mesh.create('mesh1');
+
+model.component('comp1').geom('geom1').useConstrDim(false);
+model.component('comp1').geom('geom1').create('sq1', 'Square');
+model.component('comp1').geom('geom1').feature('sq1').set('size', 'l');
+model.component('comp1').geom('geom1').create('arr1', 'Array');
+model.component('comp1').geom('geom1').feature('arr1').set('fullsize', {'n' 'n'});
+model.component('comp1').geom('geom1').feature('arr1').set('displ', {'l' 'l'});
+model.component('comp1').geom('geom1').feature('arr1').selection('input').set({'sq1'});
+model.component('comp1').geom('geom1').run;
+model.component('comp1').geom('geom1').run('fin');
+
+model.component('comp1').common.create('mpf1', 'ParticipationFactors');
+
+model.component('comp1').physics.create('solid', 'SolidMechanics', 'geom1');
+model.component('comp1').physics('solid').selection.set(unit_cell);
+model.component('comp1').physics('solid').create('pc1', 'PeriodicCondition', 1);
+model.component('comp1').physics('solid').feature('pc1').selection.set([1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 5051 5052 5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097 5098 5099 5100]);
+model.component('comp1').physics('solid').create('pc2', 'PeriodicCondition', 1);
+model.component('comp1').physics('solid').feature('pc2').selection.set([2 101 103 202 204 303 305 404 406 505 507 606 608 707 709 808 810 909 911 1010 1012 1111 1113 1212 1214 1313 1315 1414 1416 1515 1517 1616 1618 1717 1719 1818 1820 1919 1921 2020 2022 2121 2123 2222 2224 2323 2325 2424 2426 2525 2527 2626 2628 2727 2729 2828 2830 2929 2931 3030 3032 3131 3133 3232 3234 3333 3335 3434 3436 3535 3537 3636 3638 3737 3739 3838 3840 3939 3941 4040 4042 4141 4143 4242 4244 4343 4345 4444 4446 4545 4547 4646 4648 4747 4749 4848 4850 4949 4951 5050]);
+
+model.component('comp1').mesh('mesh1').create('fq1', 'FreeQuad');
+
+model.component('comp1').view('view1').axis.set('xmin', -0.03089308924973011);
+model.component('comp1').view('view1').axis.set('xmax', 0.08089308440685272);
+model.component('comp1').view('view1').axis.set('ymin', -0.004877591505646706);
+model.component('comp1').view('view1').axis.set('ymax', 0.054877594113349915);
+
+model.component('comp1').physics('solid').feature('lemm1').set('E_mat', 'userdef');
+model.component('comp1').physics('solid').feature('lemm1').set('E', 'ela');
+model.component('comp1').physics('solid').feature('lemm1').set('nu_mat', 'userdef');
+model.component('comp1').physics('solid').feature('lemm1').set('nu', 'poi');
+model.component('comp1').physics('solid').feature('lemm1').set('rho_mat', 'userdef');
+model.component('comp1').physics('solid').feature('lemm1').set('rho', 'den');
+model.component('comp1').physics('solid').feature('pc1').set('PeriodicType', 'Floquet');
+model.component('comp1').physics('solid').feature('pc1').set('kFloquet', {'kx'; 'ky'; '0'});
+model.component('comp1').physics('solid').feature('pc2').set('PeriodicType', 'Floquet');
+model.component('comp1').physics('solid').feature('pc2').set('kFloquet', {'kx'; 'ky'; '0'});
+
+model.component('comp1').mesh('mesh1').run;
+
+model.study.create('std1');
+model.study('std1').create('param', 'Parametric');
+model.study('std1').create('eig', 'Eigenfrequency');
+
+model.sol.create('sol1');
+model.sol('sol1').study('std1');
+model.sol('sol1').attach('std1');
+model.sol('sol1').create('st1', 'StudyStep');
+model.sol('sol1').create('v1', 'Variables');
+model.sol('sol1').create('e1', 'Eigenvalue');
+model.sol.create('sol2');
+model.sol('sol2').study('std1');
+model.sol('sol2').label('Parametric Solutions 1');
+
+model.batch.create('p1', 'Parametric');
+model.batch('p1').create('so1', 'Solutionseq');
+model.batch('p1').study('std1');
+
+model.result.create('pg2', 'PlotGroup1D');
+model.result('pg2').set('data', 'dset2');
+model.result('pg2').create('glob1', 'Global');
+model.result.export.create('plot1', 'Plot');
+
+model.study('std1').feature('param').set('pname', {'k1' 'k2'});
+model.study('std1').feature('param').set('plistarr', {'1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 1 1 1 1 1 1 1 1 1' '1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0 0 0 0 0 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1'});
+model.study('std1').feature('param').set('punit', {'' ''});
+model.study('std1').feature('eig').set('neigs', 10);
+model.study('std1').feature('eig').set('neigsactive', true);
+model.study('std1').feature('eig').set('ngen', 5);
+
+model.sol('sol1').attach('std1');
+model.sol('sol1').feature('e1').set('transform', 'eigenfrequency');
+model.sol('sol1').feature('e1').set('neigs', 10);
+model.sol('sol1').feature('e1').set('shift', '1[Hz]');
+model.sol('sol1').feature('e1').set('eigvfunscale', 'maximum');
+model.sol('sol1').feature('e1').set('eigvfunscaleparam', 7.069999999999999E-8);
+model.sol('sol1').feature('e1').feature('aDef').set('cachepattern', true);
+model.sol('sol1').runAll;
+
+model.batch('p1').set('control', 'param');
+model.batch('p1').set('pname', {'k1' 'k2'});
+model.batch('p1').set('plistarr', {'1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 1 1 1 1 1 1 1 1 1' '1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0 0 0 0 0 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1'});
+model.batch('p1').set('punit', {'' ''});
+model.batch('p1').set('err', true);
+model.batch('p1').feature('so1').set('seq', 'sol1');
+model.batch('p1').feature('so1').set('psol', 'sol2');
+model.batch('p1').feature('so1').set('param', {'""k1"",""1"",""k2"",""1""' '""k1"",""0.9"",""k2"",""0.9""' '""k1"",""0.8"",""k2"",""0.8""' '""k1"",""0.7"",""k2"",""0.7""' '""k1"",""0.6"",""k2"",""0.6""' '""k1"",""0.5"",""k2"",""0.5""' '""k1"",""0.4"",""k2"",""0.4""' '""k1"",""0.3"",""k2"",""0.3""' '""k1"",""0.2"",""k2"",""0.2""' '""k1"",""0.1"",""k2"",""0.1""'  ...
+'""k1"",""0"",""k2"",""0""' '""k1"",""0.1"",""k2"",""0""' '""k1"",""0.2"",""k2"",""0""' '""k1"",""0.3"",""k2"",""0""' '""k1"",""0.4"",""k2"",""0""' '""k1"",""0.5"",""k2"",""0""' '""k1"",""0.6"",""k2"",""0""' '""k1"",""0.7"",""k2"",""0""' '""k1"",""0.8"",""k2"",""0""' '""k1"",""0.9"",""k2"",""0""'  ...
+'""k1"",""1"",""k2"",""0""' '""k1"",""1"",""k2"",""0.1""' '""k1"",""1"",""k2"",""0.2""' '""k1"",""1"",""k2"",""0.3""' '""k1"",""1"",""k2"",""0.4""' '""k1"",""1"",""k2"",""0.5""' '""k1"",""1"",""k2"",""0.6""' '""k1"",""1"",""k2"",""0.7""' '""k1"",""1"",""k2"",""0.8""' '""k1"",""1"",""k2"",""0.9""'  ...
+'""k1"",""1"",""k2"",""1""'});
+model.batch('p1').attach('std1');
+model.batch('p1').run;
+
+model.result('pg2').set('xlabel', 'Solution number');
+model.result('pg2').set('xlabelactive', false);
+model.result('pg2').feature('glob1').set('expr', {'freq*a/ct'});
+model.result('pg2').feature('glob1').set('unit', {'Hz'});
+model.result('pg2').feature('glob1').set('descr', {''});
+model.result('pg2').feature('glob1').set('const', {'solid.refpntx' '0' 'Reference point for moment computation, x coordinate'; 'solid.refpnty' '0' 'Reference point for moment computation, y coordinate'; 'solid.refpntz' '0' 'Reference point for moment computation, z coordinate'});
+model.result('pg2').feature('glob1').set('xdatasolnumtype', 'outer');
+model.result.export('plot1').set('filename', ['G:\dispersion relation\model' num2str(i) '.txt']);
+model.result.export('plot1').run;
+
+end
+out = model;
+","MATLAB"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/train.py",".py","2838","88","import torch
+from utils import save_checkpoint, load_checkpoint, save_some_examples
+import torch.nn as nn
+import torch.optim as optim
+import config
+from dataset import train_loader, test_loader
+from generator_model import Generator
+from discriminator_model import Discriminator
+from tqdm import tqdm
+
+torch.backends.cudnn.benchmark = True
+
+
+def train_fn(disc, gen, loader, opt_disc, opt_gen, l1_loss, bce, g_scaler, d_scaler,):
+
+    loop = tqdm(loader, leave=True)
+
+    for idx, (x, y) in enumerate(loop):
+        x = x.to(config.DEVICE)
+        y = y.to(config.DEVICE)
+
+        # Train Discriminator
+        with torch.cuda.amp.autocast():
+            y_fake = gen(x)   # N*10*1*31--->N*1*50*50
+
+
+            D_real = disc(y, y)
+            D_real_loss = bce(D_real, torch.ones_like(D_real))
+            D_fake = disc(y, y_fake.detach())
+            D_fake_loss = bce(D_fake, torch.zeros_like(D_fake))
+            D_loss = (D_real_loss + D_fake_loss) / 2
+
+        disc.zero_grad()
+        d_scaler.scale(D_loss).backward()
+        d_scaler.step(opt_disc)
+        d_scaler.update()
+
+        # Train generator
+        with torch.cuda.amp.autocast():
+            D_fake = disc(y, y_fake)
+            G_fake_loss = bce(D_fake, torch.ones_like(D_fake))
+            L1 = l1_loss(y_fake, y) * config.L1_LAMBDA
+
+
+            G_loss = G_fake_loss + L1
+
+        opt_gen.zero_grad()
+        g_scaler.scale(G_loss).backward()
+        g_scaler.step(opt_gen)
+        g_scaler.update()
+
+        if idx % 10 == 0:
+            loop.set_postfix(D_real=torch.sigmoid(D_real).mean().item(),
+                             D_fake=torch.sigmoid(D_fake).mean().item(),)
+
+
+def main():
+    disc = Discriminator(in_channels=1).to(config.DEVICE)
+    gen = Generator(in_channels=1, features=32).to(config.DEVICE)
+    opt_disc = optim.Adam(disc.parameters(), lr=config.LEARNING_RATE, betas=(0.5, 0.999),)
+    opt_gen = optim.Adam(gen.parameters(), lr=config.LEARNING_RATE, betas=(0.5, 0.999))
+    BCE = nn.BCEWithLogitsLoss()
+    L1_LOSS = nn.L1Loss()
+
+    if config.LOAD_MODEL:
+        load_checkpoint(
+            config.CHECKPOINT_GEN, gen, opt_gen, config.LEARNING_RATE,
+        )
+        load_checkpoint(
+            config.CHECKPOINT_DISC, disc, opt_disc, config.LEARNING_RATE,
+        )
+
+    g_scaler = torch.cuda.amp.GradScaler()
+    d_scaler = torch.cuda.amp.GradScaler()
+
+    for epoch in range(config.NUM_EPOCHS):
+        train_fn(disc, gen, train_loader, opt_disc, opt_gen, L1_LOSS, BCE, g_scaler, d_scaler,)
+
+        if config.SAVE_MODEL and epoch % 1 == 0:
+            save_checkpoint(gen, opt_gen, filename=f'checkpoints/gen_{epoch+1}.pth.tar')
+            save_checkpoint(disc, opt_disc, filename=f'checkpoints/disc_{epoch+1}.pth.tar')
+
+        save_some_examples(gen, test_loader, epoch+1, folder=""evaluation"")
+
+
+if __name__ == ""__main__"":
+    main()
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/generator_model.py",".py","4899","131","import torch
+import torch.nn as nn
+
+
+class Block(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Block_down(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block_down, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 6, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 6, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Block_up(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block_up, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 6, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 6, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Generator(nn.Module):
+    def __init__(self, in_channels=1, features=64):
+        super().__init__()
+
+        self.initial_down_1 = nn.Sequential(
+            nn.Conv2d(10, 50, 1, 1, 0, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )   # N*50*1*31
+        self.initial_down_2 = nn.Sequential(
+            nn.Conv2d(31, 50, 1, 1, 0, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )   # N*50*1*50
+        self.initial_down = nn.Sequential(
+            nn.Conv2d(1, features, 3, 1, 1, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )
+
+        self.down1 = Block_down(features, features * 2, down=True, act=""leaky"", use_dropout=False)
+        self.down2 = Block(
+            features * 2, features * 4, down=True, act=""leaky"", use_dropout=False
+        )
+        self.down3 = Block(
+            features * 4, features * 8, down=True, act=""leaky"", use_dropout=False
+        )
+        self.down4 = Block(
+            features * 8, features * 8, down=True, act=""leaky"", use_dropout=False
+        )
+
+        self.bottleneck = nn.Sequential(
+            nn.Conv2d(features * 8, features * 8, 3, 1, 1), nn.ReLU()
+        )
+
+        self.up1 = Block(features * 8, features * 8, down=False, act=""relu"", use_dropout=True)
+        self.up2 = Block(
+            features * 8 * 2, features * 4, down=False, act=""relu"", use_dropout=True
+        )
+        self.up3 = Block(
+            features * 4 * 2, features * 2, down=False, act=""relu"", use_dropout=True
+        )
+        self.up4 = Block_up(
+            features * 2 * 2, features * 1, down=False, act=""relu"", use_dropout=False
+        )
+
+        self.final_up = nn.Sequential(
+            nn.ConvTranspose2d(features * 2, in_channels, kernel_size=3, stride=1, padding=1),
+            nn.Tanh(),
+        )
+
+    def forward(self, x):
+        d1 = self.initial_down_1(x)   # N*10*1*31--->N*50*1*31
+        d1 = self.initial_down_2(d1.transpose(3, 1))   # N*50*1*31--->N*50*1*50
+        d1 = d1.reshape(-1, 1, 50, 50)
+
+        d1 = self.initial_down(d1)   # N*64*50*50
+
+        d2 = self.down1(d1)  # N*128*24*24
+        d3 = self.down2(d2)  # N*256*12*12
+        d4 = self.down3(d3)  # N*512*6*6
+        d5 = self.down4(d4)  # N*512*3*3
+        bottleneck = self.bottleneck(d5)  # N*512*3*3
+        up1 = self.up1(bottleneck)  # N*512*6*6
+        up2 = self.up2(torch.cat([up1, d4], 1))  # N*256*12*12
+        up3 = self.up3(torch.cat([up2, d3], 1))  # N*128*24*24
+        up4 = self.up4(torch.cat([up3, d2], 1))  # N*64*50*50
+
+        output = self.final_up(torch.cat([up4, d1], 1))
+        return output
+
+
+def test():
+    x = torch.randn((100, 10, 1, 31))
+    model = Generator(in_channels=1, features=64)
+    preds = model(x)
+    print(preds.shape)   # 1*1*50*50
+
+
+if __name__ == ""__main__"":
+    test()
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/dataset.py",".py","1106","34","import scipy.io as sio
+import numpy as np
+import torch.utils.data as Data
+import torch
+
+BATCH_SIZE = 512
+SPLIT_RATIO = 0.8
+
+dataset = sio.loadmat(f'unit_cell_with_bandgap.mat')['train']
+
+all_data = np.concatenate([[i.T for i in dataset[4]]], axis=0).astype('float32')   # N*10*31
+all_label = np.concatenate([[i for i in dataset[6]]], axis=0).astype('float32')   # N*50*50
+
+all_data = all_data.reshape(-1, 10, 1, 31)
+all_label = all_label.reshape(-1, 1, 50, 50)
+
+
+split_dataset = int(all_data.shape[0]*SPLIT_RATIO)
+Xtrain = torch.tensor(all_data[:split_dataset])
+Labeltrain = torch.tensor(all_label[:split_dataset])
+Xtest = torch.tensor(all_data[split_dataset:])
+Labeltest = torch.tensor(all_label[split_dataset:])
+
+
+train_data = Data.TensorDataset(Xtrain, Labeltrain)
+train_loader = Data.DataLoader(dataset=train_data,
+                               batch_size=BATCH_SIZE,
+                               shuffle=True)
+
+test_data = Data.TensorDataset(Xtest, Labeltest)
+test_loader = Data.DataLoader(dataset=test_data,
+                              batch_size=1,
+                              shuffle=False)
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/inference.py",".py","2762","92","import torch
+from dataset import test_loader
+from generator_model import Generator
+from tqdm import tqdm
+import config
+from torchvision.utils import save_image
+import numpy as np
+import cv2 as cv
+from skimage.measure._label import label
+
+def cv_imread(filePath):
+
+    cv_img = cv.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)
+    cv_img = cv.cvtColor(cv_img, cv.COLOR_RGB2BGR)
+
+    return cv_img
+
+def threshold_image(image):
+
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    ret, binary = cv.threshold(gray, 255, 255, cv.THRESH_BINARY|cv.THRESH_OTSU)
+
+    return binary
+
+def save_some_examples(gen, x, y, epoch, folder):
+
+    x, y = x.to(config.DEVICE), y.to(config.DEVICE)
+    gen.eval()
+    with torch.no_grad():
+        y_fake = gen(x)
+
+        y_fake += torch.flip(y_fake, (2,))   # row
+        y_fake += torch.flip(y_fake, (3,))   # column
+        y_fake += y_fake.transpose(-1, -2)   # diagonal
+
+        save_image(y_fake, folder + f""/y_gen_{epoch}.png"")
+        save_image(y, folder + f""/input_{epoch}.png"")
+
+        y_fake_name = folder + f""/y_gen_{epoch}.png""
+        y_fake_image = cv_imread(y_fake_name)
+        y_fake_image = cv.medianBlur(y_fake_image, ksize=3)
+        y_binary = threshold_image(image=y_fake_image)
+
+        labels, num = label(y_binary, return_num=True, background=255)
+
+        max_area = 0
+        for i in range(1, num + 1):
+            value_area = (np.ones_like(labels) * i == labels).sum()
+
+            if value_area > max_area:
+                max_area = value_area
+                value = i
+
+        y_binary = 1 - np.array(np.ones_like(labels) * value == labels).astype('uint8')
+
+        y_fake = torch.from_numpy(y_binary)[None, None, ...]
+        y_fake = torch.clamp(y_fake.float(), min=0., max=1.)
+
+        save_image(y_fake, folder + f""/y_gen_{epoch}_binary.png"")
+
+        return y, y_fake
+
+
+gen = Generator(in_channels=1, features=32).to(config.DEVICE)
+
+gen.load_state_dict(torch.load('checkpoints/gen_500.pth.tar')['state_dict'], strict=False)
+
+
+loop = tqdm(test_loader, leave=True)
+
+output_50_50 = []
+label_50_50 = []
+label_10_31 = []
+for idx, (x, y) in enumerate(loop):
+
+    if idx>14120:
+
+        y, y_fake = save_some_examples(gen, x, y, idx+1, folder=""inference"")
+
+        label_50_50.append(y.squeeze(1).cpu().numpy())
+        output_50_50.append(y_fake.squeeze(1).cpu().numpy())
+        label_10_31.append(x.reshape(-1, 10, 31).cpu().numpy())
+
+label_10_31 = np.concatenate(label_10_31, axis=0)
+label_50_50 = np.concatenate(label_50_50, axis=0)
+output_50_50 = np.concatenate(output_50_50, axis=0)
+
+import scipy.io as sio
+
+sio.savemat('inference.mat', {'label_10_31': label_10_31,
+                              'label_50_50': label_50_50,
+                              'output_50_50': output_50_50})","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/discriminator_model.py",".py","1748","69","import torch
+import torch.nn as nn
+
+
+class CNNBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride):
+        super(CNNBlock, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                in_channels, out_channels, 4, stride, 1, bias=False, padding_mode=""reflect""
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.LeakyReLU(0.2),
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Discriminator(nn.Module):
+    def __init__(self, in_channels=1, features=[64, 128, 256, 512]):
+        super().__init__()
+        self.initial = nn.Sequential(
+            nn.Conv2d(
+                in_channels * 2,
+                features[0],
+                kernel_size=4,
+                stride=2,
+                padding=1,
+                padding_mode=""reflect"",
+            ),
+            nn.LeakyReLU(0.2),
+        )
+
+        layers = []
+        in_channels = features[0]
+        for feature in features[1:]:
+            layers.append(
+                CNNBlock(in_channels, feature, stride=1 if feature == features[-1] else 2),
+            )
+            in_channels = feature
+
+        layers.append(
+            nn.Conv2d(
+                in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode=""reflect""
+            ),
+        )
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x, y):
+        x = torch.cat([x, y], dim=1)
+        x = self.initial(x)
+        x = self.model(x)
+        return x
+
+
+def test():
+    x = torch.randn((1, 1, 50, 50))
+    y = torch.randn((1, 1, 50, 50))
+    model = Discriminator(in_channels=1)
+    preds = model(x, y)
+    print(model)
+    print(preds.shape)   # 1*1*4*4
+
+
+if __name__ == ""__main__"":
+    test()
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/utils.py",".py","1165","38","import torch
+import config
+from torchvision.utils import save_image
+
+def save_some_examples(gen, val_loader, epoch, folder):
+    x, y = next(iter(val_loader))
+    x, y = x.to(config.DEVICE), y.to(config.DEVICE)
+    gen.eval()
+    with torch.no_grad():
+        y_fake = gen(x)
+        y_fake = y_fake  # remove normalization#
+        save_image(y_fake, folder + f""/y_gen_{epoch}.png"")
+        save_image(y, folder + f""/input_{epoch}.png"")
+        if epoch == 1:
+            save_image(y, folder + f""/label_{epoch}.png"")
+    gen.train()
+
+
+def save_checkpoint(model, optimizer, filename=""my_checkpoint.pth.tar""):
+    print(""=> Saving checkpoint"")
+    checkpoint = {
+        ""state_dict"": model.state_dict(),
+        ""optimizer"": optimizer.state_dict(),
+    }
+    torch.save(checkpoint, filename)
+
+
+def load_checkpoint(checkpoint_file, model, optimizer, lr):
+    print(""=> Loading checkpoint"")
+    checkpoint = torch.load(checkpoint_file, map_location=config.DEVICE)
+    model.load_state_dict(checkpoint[""state_dict""], strict=False)
+    optimizer.load_state_dict(checkpoint[""optimizer""])
+
+    for param_group in optimizer.param_groups:
+        param_group[""lr""] = lr
+
+
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/config.py",".py","292","15","import torch
+
+
+DEVICE = ""cuda"" if torch.cuda.is_available() else ""cpu""
+LEARNING_RATE = 2e-4
+BATCH_SIZE = 512
+NUM_WORKERS = 4
+L1_LAMBDA = 100
+NUM_EPOCHS = 500
+LOAD_MODEL = False
+SAVE_MODEL = True
+CHECKPOINT_DISC = ""checkpoints/disc_500.pth.tar""
+CHECKPOINT_GEN = ""checkpoints/gen_500.pth.tar""
+
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/cGAN/shuffle_data.py",".py","296","16","import scipy.io as sio
+import random
+
+
+MAT_NAME = 'unit_cell_with_bandgap.mat'
+
+dataset = sio.loadmat(MAT_NAME)['exist']
+
+shuffle_idx = list(range(dataset.shape[-1]))
+random.shuffle(shuffle_idx)
+
+all_data = dataset[:, shuffle_idx]
+
+sio.savemat('unit_cell_with_bandgap.mat', {'train': all_data})
+
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/CNN/train.py",".py","4319","137","import torch
+import torch.nn as nn
+from torch.utils.tensorboard import SummaryWriter
+import os
+import numpy as np
+from dataset import loader
+from model import CNN
+
+
+def train_once_data(num):
+    Iter = 1234
+
+    torch.manual_seed(Iter)
+    EPOCH = 230
+    LR = 0.1
+
+    model = CNN(num_classes=31)
+
+    if torch.cuda.device_count() >= 1:
+      print(""Let's use"", torch.cuda.device_count(), ""GPUs!"")
+      model = nn.DataParallel(model)
+
+    model.cuda()
+
+    opt = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9, weight_decay=1e-5)
+
+    save_dir = os.path.join(os.getcwd(), f'checkpoint{num}')
+
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+    log = open(os.path.join(save_dir, 'log.txt'), 'a')
+
+    def print_log(print_string, log):
+        print(""{}"".format(print_string))
+        log.write('{}\n'.format(print_string))
+        log.flush()
+
+    def save_checkpoint(state, is_best, epoch):
+        """"""
+        Save the training model
+        """"""
+        if is_best:
+            max_file_num = 0
+            filelist = os.listdir(save_dir)
+            filelist.sort()
+            file_num = 0
+            to_move = []
+            for file in filelist:
+                if 'model' in file:
+                    file_num = file_num + 1
+                    to_move.append(os.path.join(save_dir, file))
+            if file_num > max_file_num:
+                to_move.sort()
+                os.remove(to_move[0])
+                to_move.pop(0)
+
+            torch.save(state, save_dir + (f'/model_best_{epoch}.pth.tar'))
+
+    def adjust_learning_rate(optimizer, epoch, start_lr):
+        lr = start_lr * (0.1 ** (epoch // 100))
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+
+    BEST_test_MAE_PER_AVG = np.inf
+    writer = SummaryWriter(f'{save_dir}/logs/CNN')
+    START_EPOCH = 0
+
+    for epoch in range(START_EPOCH, EPOCH):
+
+        adjust_learning_rate(opt, epoch, LR)
+
+        model.train()
+        train_MAE = 0.
+        train_MAE_PER = 0.
+        for batch_idx, (b_x, b_y) in enumerate(loader(num)[1]):
+
+            output = model(b_x.unsqueeze(1).cuda())
+
+            MAE = torch.mean(torch.abs(output-b_y.cuda()))
+
+            opt.zero_grad()
+            MAE.backward()
+            opt.step()
+
+            train_MAE += MAE.item()
+
+            b_y = torch.cat([b_y[:, :10], b_y[:, 11:]], dim=-1)
+            output = torch.cat([output[:, :10], output[:, 11:]], dim=-1)
+            train_MAE_PER += torch.mean(torch.abs(output-b_y.cuda())/b_y.cuda()).item()
+
+
+        train_MAE_AVG = train_MAE/(batch_idx+1)
+        train_MAE_PER_AVG = train_MAE_PER/(batch_idx+1)
+
+        model.eval()
+        test_MAE = 0.
+        test_MAE_PER = 0.
+        with torch.no_grad():
+            for batch_idx, (b_x, b_y) in enumerate(loader(num)[0]):
+                test_output = model(b_x.unsqueeze(1).cuda())
+
+                test_MAE += torch.mean(torch.abs(test_output-b_y.cuda())).item()
+
+                b_y = torch.cat([b_y[:, :10], b_y[:, 11:]], dim=-1)
+                test_output = torch.cat([test_output[:, :10], test_output[:, 11:]], dim=-1)
+                test_MAE_PER += torch.mean(torch.abs(test_output-b_y.cuda())/b_y.cuda()).item()
+
+            test_MAE_AVG = test_MAE/(batch_idx+1)
+            test_MAE_PER_AVG = test_MAE_PER/(batch_idx+1)
+
+            print_log(f'Epoch: {epoch}\t'
+                      f'|train_MAE_AVG: {train_MAE_AVG:.8f}\t|train_MAE_PER_AVG: {train_MAE_PER_AVG:.8f}\t'
+                      f'|test_MAE_AVG: {test_MAE_AVG:.8f}\t|test_MAE_PER_AVG: {test_MAE_PER_AVG:.8f}', log)
+
+            is_best = test_MAE_PER_AVG < BEST_test_MAE_PER_AVG
+
+            if is_best:
+                BEST_test_MAE_PER_AVG = min(test_MAE_PER_AVG, BEST_test_MAE_PER_AVG)
+                print('BEST_test_MAE_PER_AVG: ', BEST_test_MAE_PER_AVG)
+
+                save_checkpoint({
+                    'epoch': epoch + 1,
+                    'state_dict': model.state_dict(),
+                    'BEST_test_MAE_PER_AVG': BEST_test_MAE_PER_AVG,
+                    'optimizer': opt.state_dict()
+                }, is_best, epoch + 1)
+
+        writer.add_scalar('train_MAE_PER_AVG', train_MAE_PER_AVG, epoch)
+        writer.add_scalar('test_MAE_PER_AVG', test_MAE_PER_AVG, epoch)
+
+    writer.close()
+
+
+for num in range(0, 10):
+    train_once_data(num)
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/CNN/model.py",".py","1658","59","import torch
+import torch.nn as nn
+
+
+__all__ = ['CNN']
+
+
+class CNN(nn.Module):
+
+    def __init__(self, num_classes=31):
+        super(CNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(1, 128, kernel_size=5, stride=1, padding=2),   # N*50*50
+            # nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # N*25*25
+
+
+            nn.Conv2d(128, 512, kernel_size=3, padding=1),           # N*25*25
+            # nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),                  # N*12*12
+
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),          # N*12*12
+            # nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(512, 1024, kernel_size=3, padding=1),          # N*12*12
+            # nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # N*6*6
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((3, 3))
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(1024 * 3 * 3, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 2048),
+            nn.ReLU(inplace=True),
+            nn.Linear(2048, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x
+
+def test():
+    x = torch.randn(1, 1, 50, 50)
+    model = CNN(num_classes=31)
+    print(model(x).shape)
+
+
+if __name__ == ""__main__"":
+    test()
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/CNN/dataset.py",".py","1177","35","import scipy.io as sio
+import numpy as np
+import torch.utils.data as Data
+import torch
+
+def loader(num):
+
+    SPLIT_RATIO = 0.8
+    BATCH_SIZE = 128
+
+    MAT_NAME = 'unit_cell_data.mat'
+
+    dataset = sio.loadmat(MAT_NAME)['train']
+
+    all_data = np.concatenate([i[None, ...] for i in dataset[6]], axis=0).astype('float32')   #N*50*50
+    all_label = np.concatenate([[i.T[num] for i in dataset[4]]], axis=0).astype('float32')   #N*31
+
+    split_dataset = int(all_data.shape[0]*SPLIT_RATIO)
+    Xtrain = torch.tensor(all_data[:split_dataset])
+    Labeltrain = torch.tensor(all_label[:split_dataset])
+    Xtest = torch.tensor(all_data[split_dataset:])
+    Labeltest = torch.tensor(all_label[split_dataset:])
+
+    train_data = Data.TensorDataset(Xtrain, Labeltrain)
+    train_loader = Data.DataLoader(dataset=train_data,
+                                   batch_size=BATCH_SIZE,
+                                   shuffle=True)
+
+    test_data = Data.TensorDataset(Xtest, Labeltest)
+    test_loader = Data.DataLoader(dataset=test_data,
+                                  batch_size=BATCH_SIZE,
+                                  shuffle=False)
+
+    return test_loader, train_loader
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/CNN/inference.py",".py","2051","79","import torch
+import torch.nn as nn
+import os
+import numpy as np
+import scipy.io as sio
+from dataset import loader
+from model import CNN
+
+def predict_once_data(num):
+
+    DATA_DIR = os.getcwd()
+    weights = os.listdir(os.path.join(DATA_DIR, 'checkpoints'))
+    weights.sort()
+
+    weight = weights[num]
+
+    DIR = os.path.join(DATA_DIR, 'checkpoints/'+weight)
+    checkpoint = torch.load(DIR)
+
+    model = CNN(num_classes=31)
+
+    if torch.cuda.device_count() >= 1:
+      print(""Let's use"", torch.cuda.device_count(), ""GPUs!"")
+      model = nn.DataParallel(model)
+
+    model.cuda()
+
+    model.load_state_dict(checkpoint['state_dict'])
+
+    model.eval()
+    with torch.no_grad():
+
+        output_1_13 = []
+        label_1_13 = []
+
+        for batch_idx, (b_x, b_y) in enumerate(loader(num)[0]):
+            batch_test_output = model(b_x.unsqueeze(1).cuda())
+            output_1_13.append(batch_test_output.cpu().numpy()[:, None, :])
+            label_1_13.append(b_y.numpy()[:, None, :])
+
+        output_1_13 = np.concatenate(output_1_13, 0)
+        label_1_13 = np.concatenate(label_1_13, 0)
+
+    return output_1_13, label_1_13
+
+
+
+output_10_13 = []
+label_10_13 = []
+for num in range(0, 10):
+    output_1_13, label_1_13 = predict_once_data(num)
+
+
+    output_10_13.append(output_1_13)
+    label_10_13.append(label_1_13)
+
+output_10_13 = np.concatenate(output_10_13, 1)
+label_10_13 = np.concatenate(label_10_13, 1)
+
+save_dir = 'results'
+
+if not os.path.exists(save_dir):
+    os.makedirs(save_dir)
+
+sio.savemat(save_dir+'/results.mat', {'output_10_13': output_10_13,
+                                      'label_10_13': label_10_13})
+
+
+
+
+output_10_13 = sio.loadmat('results/results.mat')['output_10_13']
+label_10_13 = sio.loadmat('results/results.mat')['label_10_13']
+
+output_10_13 = np.concatenate([output_10_13[:, :, :10], output_10_13[:, :, 11:]], axis=-1)
+label_10_13 = np.concatenate([label_10_13[:, :, :10], label_10_13[:, :, 11:]], axis=-1)
+
+MAE_PER_AVG = 1-np.mean(np.abs(output_10_13-label_10_13)/label_10_13)
+
+print(f'MAE_PER_AVG:{MAE_PER_AVG}')","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/inverse_design/inference.py",".py","7467","249","import torch
+import torch.nn as nn
+from tqdm import tqdm
+from torchvision.utils import save_image
+import os
+import scipy.io as sio
+import numpy as np
+import torch.utils.data as Data
+from skimage.measure._label import label
+import cv2 as cv
+
+import cGAN.config as config
+from cGAN.dataset import test_loader
+from cGAN.generator_model import Generator
+from CNN.model import CNN
+
+
+BATCH_SIZE = 512
+
+def cv_imread(filePath):
+
+    cv_img = cv.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)
+    cv_img = cv.cvtColor(cv_img, cv.COLOR_RGB2BGR)
+
+    return cv_img
+
+def threshold_image(image):
+
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    ret, binary = cv.threshold(gray, 255, 255, cv.THRESH_BINARY|cv.THRESH_OTSU)
+
+    return binary
+
+def inference_some_examples(gen, x, y, idx, epoch, folder):
+
+    gen.eval()
+    with torch.no_grad():
+        y_fake = gen(x)
+
+        save_image(y_fake, folder + f""/y_gen_0_{idx}_{epoch}.png"")
+
+        y_fake += torch.flip(y_fake, (2,))   # row
+        y_fake += torch.flip(y_fake, (3,))   # column
+        y_fake += y_fake.transpose(-1, -2)   # diagonal
+
+        save_image(y_fake, folder + f""/y_gen_1_{idx}_{epoch}.png"")
+
+        if epoch==1:
+            save_image(y, folder + f""/input_{idx}_{epoch}.png"")
+
+
+        y_fake_name = folder + f""/y_gen_1_{idx}_{epoch}.png""
+        y_fake_image = cv_imread(y_fake_name)
+        y_fake_image = cv.medianBlur(y_fake_image, ksize=3)
+        y_binary = threshold_image(image=y_fake_image)
+
+        labels, num = label(y_binary, return_num=True, background=255)
+
+        max_area = 0
+        value = 0
+        for i in range(1, num + 1):
+            value_area = (np.ones_like(labels) * i == labels).sum()
+
+            if value_area > max_area:
+                max_area = value_area
+                value = i
+
+        y_binary = 1 - np.array(np.ones_like(labels) * value == labels).astype('uint8')
+
+        y_fake = torch.from_numpy(y_binary)[None, None, ...]
+        y_fake = torch.clamp(y_fake.float(), min=0., max=1.)
+
+        save_image(y_fake, folder + f""/y_gen_1_{idx}_{epoch}_binary.png"")
+
+        return y, y_fake
+
+def save_some_examples(gt, test, epoch, folder):
+
+    save_image(gt, folder + f""/input_{epoch}.png"")
+    save_image(test, folder + f""/y_gen_{epoch}.png"")
+
+
+
+def loader(pre_50_50, label_10_31):
+
+    pre_data = Data.TensorDataset(pre_50_50, label_10_31)
+    pre_loader = Data.DataLoader(dataset=pre_data,
+                                  batch_size=BATCH_SIZE,
+                                  shuffle=False)
+
+    return pre_loader
+
+def predict_once_data(num, pre_loader):
+
+    DATA_DIR = os.getcwd()
+    weights = os.listdir(os.path.join(DATA_DIR, 'CNN/checkpoints'))
+    weights.sort()
+
+    weight = weights[num]
+
+    DIR = os.path.join(DATA_DIR, 'CNN/checkpoints/'+weight)
+    checkpoint = torch.load(DIR)
+
+    model = CNN(num_classes=31)
+
+    if torch.cuda.device_count() >= 1:
+      print(""Let's use"", torch.cuda.device_count(), ""GPUs!"")
+      model = nn.DataParallel(model)
+
+    model.cuda()
+
+    model.load_state_dict(checkpoint['state_dict'])
+
+    model.eval()
+    with torch.no_grad():
+
+        pre_1_13 = []
+
+        for batch_idx, (b_x, b_y) in enumerate(pre_loader):
+            batch_test_output = model(b_x.unsqueeze(1).cuda())
+            pre_1_13.append(batch_test_output.cpu().numpy()[:, None, :])
+
+        pre_1_13 = np.concatenate(pre_1_13, 0)
+
+    return pre_1_13
+
+
+
+gen = Generator(in_channels=1, features=32).to(config.DEVICE)
+gen.load_state_dict(torch.load('cGAN/checkpoints/gen_500.pth.tar')['state_dict'], strict=False)
+
+loop = tqdm(test_loader, leave=True)
+
+pre_50_50 = []
+label_50_50 = []
+label_10_31 = []
+noise_label_10_31 = []
+noise_10_31 = []
+num_choice_idx = 1
+num_noise = 2000
+
+# choice_idx = np.random.choice(len(test_loader), num_choice_idx, replace=False)
+# choice_idx = list(range(len(test_loader)))[-num_choice_idx:]
+choice_idx = list(range(len(test_loader)))[:]
+
+
+log = open(os.path.join('inference/log.txt'), 'a')
+
+def print_log(print_string, log):
+    print(""{}"".format(print_string))
+    log.write('{}\n'.format(print_string))
+    log.flush()
+
+print_log(f'choice_idx:\n{choice_idx}', log)
+
+for idx, (x0, y) in enumerate(loop):
+
+    if idx in choice_idx:
+        for num_iter in range(num_noise):
+            if num_iter!=0:
+
+                # noise = torch.randn([*x0.shape])
+                # x = x0 + (noise.normal_(mean=0, std=1).clamp_(min=-1, max=1) / 25)
+
+                # x = x0 + (torch.randn([*x0.shape])/5)*(torch.softmax(x0.max(-1).values, 1)[..., None])
+
+                noise = torch.randn([*x0.shape])/50
+                x = x0 + noise
+
+            else:
+                noise = torch.zeros([*x0.shape]) / 50
+                x = x0
+
+            y, y_fake = inference_some_examples(gen, x.to(config.DEVICE), y.to(config.DEVICE), idx, num_iter+1, folder='inference')   # N*1*50*50
+
+            label_50_50.append(y.squeeze(1))   # N*50*50
+            pre_50_50.append(y_fake.squeeze(1))
+            label_10_31.append(x0.reshape(-1, 10, 31).to(config.DEVICE))
+            noise_label_10_31.append(x.reshape(-1, 10, 31).to(config.DEVICE))
+            noise_10_31.append(noise.reshape(-1, 10, 31).to(config.DEVICE))
+
+
+label_50_50 = torch.cat(label_50_50, dim=0)
+pre_50_50 = torch.cat(pre_50_50, dim=0)
+label_10_31 = torch.cat(label_10_31, dim=0)
+noise_label_10_31 = torch.cat(noise_label_10_31, dim=0)
+noise_10_31 = torch.cat(noise_10_31, dim=0)
+
+
+pre_10_31 = []
+pre_loader = loader(pre_50_50, label_10_31)
+
+for num in range(0, 10):
+
+    pre_1_13 = predict_once_data(num, pre_loader)
+
+    pre_10_31.append(pre_1_13)
+
+pre_10_31 = np.concatenate(pre_10_31, 1)
+label_10_31 = label_10_31.cpu().numpy()
+noise_label_10_31 = noise_label_10_31.cpu().numpy()
+noise_10_31 = noise_10_31.cpu().numpy()
+
+# pre_10_31_MAE = np.concatenate([pre_10_31[:, 0, :10], pre_10_31[:, 0, 11:]], axis=-1)
+# label_10_31_MAE = np.concatenate([label_10_31[:, 0, :10], label_10_31[:, 0, 11:]], axis=-1)
+# pre_10_31_MAE = pre_10_31[:, 0, :10]
+# label_10_31_MAE = label_10_31[:, 0, :10]
+pre_10_31_MAE = pre_10_31[:, 8, :]
+label_10_31_MAE = label_10_31[:, 8, :]
+
+MAE_PER_AVG = np.mean(np.abs(pre_10_31_MAE-label_10_31_MAE)/label_10_31_MAE, axis=(-1))
+
+MAE_PER_AVG = MAE_PER_AVG.reshape(num_choice_idx, num_noise)
+
+
+num_save = 5
+top5_idx = np.argpartition(MAE_PER_AVG, num_save)[:, :num_save]
+top5_idx = top5_idx + (np.array(list(range(num_choice_idx)))*num_noise).reshape(-1, 1)
+top5_idx = top5_idx.flatten()
+
+
+label_50_50 = label_50_50[top5_idx]
+pre_50_50 = pre_50_50[top5_idx]
+label_10_31 = label_10_31[top5_idx]
+pre_10_31 = pre_10_31[top5_idx]
+noise_label_10_31 = noise_label_10_31[top5_idx]
+noise_10_31 = noise_10_31[top5_idx]
+
+
+symmetry_idx = pre_50_50[:, :, -1].sum(-1)!=1*50
+
+label_50_50 = label_50_50[symmetry_idx]
+pre_50_50 = pre_50_50[symmetry_idx]
+label_10_31 = label_10_31[symmetry_idx]
+pre_10_31 = pre_10_31[symmetry_idx]
+noise_label_10_31 = noise_label_10_31[symmetry_idx]
+noise_10_31 = noise_10_31[symmetry_idx]
+
+
+for i in range(len(label_50_50)):
+    save_some_examples(gt=label_50_50[i], test=pre_50_50[i], epoch=i+1, folder='save')
+
+
+sio.savemat('inference_flat_band.mat', {'label_10_31': label_10_31,
+                              'label_50_50': label_50_50.cpu().numpy(),
+                              'pre_50_50': pre_50_50.cpu().numpy(),
+                              'pre_10_31': pre_10_31,
+                              'noise_10_31': noise_10_31,
+                              'noise_label_10_31': noise_label_10_31})","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/inverse_design/cGAN/generator_model.py",".py","4899","133","import torch
+import torch.nn as nn
+
+
+class Block(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Block_down(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block_down, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 6, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 6, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Block_up(nn.Module):
+    def __init__(self, in_channels, out_channels, down=True, act=""relu"", use_dropout=False):
+        super(Block_up, self).__init__()
+        self.conv = nn.Sequential(nn.Conv2d(in_channels, out_channels, 6, 2, 1, bias=False, padding_mode=""reflect"") if down else nn.ConvTranspose2d(in_channels, out_channels, 6, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU() if act == ""relu"" else nn.LeakyReLU(0.2),
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(0.5)
+        self.down = down
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.dropout(x) if self.use_dropout else x
+
+class Generator(nn.Module):
+    def __init__(self, in_channels=1, features=64):
+        super().__init__()
+
+        self.initial_down_1 = nn.Sequential(
+            nn.Conv2d(10, 50, 1, 1, 0, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )   # N*50*1*31
+        self.initial_down_2 = nn.Sequential(
+            nn.Conv2d(31, 50, 1, 1, 0, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )   # N*50*1*50
+        self.initial_down = nn.Sequential(
+            nn.Conv2d(1, features, 3, 1, 1, padding_mode=""reflect""),
+            nn.LeakyReLU(0.2),
+        )
+
+        self.down1 = Block_down(features, features * 2, down=True, act=""leaky"", use_dropout=False)
+        self.down2 = Block(
+            features * 2, features * 4, down=True, act=""leaky"", use_dropout=False
+        )
+        self.down3 = Block(
+            features * 4, features * 8, down=True, act=""leaky"", use_dropout=False
+        )
+        self.down4 = Block(
+            features * 8, features * 8, down=True, act=""leaky"", use_dropout=False
+        )
+
+        self.bottleneck = nn.Sequential(
+            nn.Conv2d(features * 8, features * 8, 3, 1, 1), nn.ReLU()
+        )
+
+        self.up1 = Block(features * 8, features * 8, down=False, act=""relu"", use_dropout=True)
+        self.up2 = Block(
+            features * 8 * 2, features * 4, down=False, act=""relu"", use_dropout=True
+        )
+        self.up3 = Block(
+            features * 4 * 2, features * 2, down=False, act=""relu"", use_dropout=True
+        )
+        self.up4 = Block_up(
+            features * 2 * 2, features * 1, down=False, act=""relu"", use_dropout=False
+        )
+
+        self.final_up = nn.Sequential(
+            nn.ConvTranspose2d(features * 2, in_channels, kernel_size=3, stride=1, padding=1),
+            nn.Tanh(),
+        )
+
+    def forward(self, x):
+        d1 = self.initial_down_1(x)   # N*10*1*31--->N*50*1*31
+        d1 = self.initial_down_2(d1.transpose(3, 1))   # N*50*1*31--->N*50*1*50
+        d1 = d1.reshape(-1, 1, 50, 50)
+
+        d1 = self.initial_down(d1)   # N*64*50*50
+
+        d2 = self.down1(d1)  # N*128*24*24
+        d3 = self.down2(d2)  # N*256*12*12
+        d4 = self.down3(d3)  # N*512*6*6
+        d5 = self.down4(d4)  # N*512*3*3
+
+        bottleneck = self.bottleneck(d5)  # N*512*3*3
+
+        up1 = self.up1(bottleneck)  # N*512*6*6
+        up2 = self.up2(torch.cat([up1, d4], 1))  # N*256*12*12
+        up3 = self.up3(torch.cat([up2, d3], 1))  # N*128*24*24
+        up4 = self.up4(torch.cat([up3, d2], 1))  # N*64*50*50
+
+        output = self.final_up(torch.cat([up4, d1], 1))
+        return output
+
+
+def test():
+    x = torch.randn((2, 10, 1, 31))
+    model = Generator(in_channels=1, features=32)
+    preds = model(x)
+    print(preds.shape)   # 1*1*50*50
+
+
+if __name__ == ""__main__"":
+    test()
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/inverse_design/cGAN/dataset.py",".py","739","26","import scipy.io as sio
+import numpy as np
+import torch.utils.data as Data
+import torch
+
+# dataset = sio.loadmat(f'../flat_band.mat')['flatband']
+dataset = sio.loadmat(f'flat_band.mat')['flatband']
+# all_data = np.concatenate([[i.T for i in dataset[2]]], axis=0).astype('float32')   # N*10*31
+all_data = dataset[2][0].T.astype('float32')[None, ...]
+
+
+all_label = np.random.random((1, 50, 50))
+
+all_data = all_data.reshape(-1, 10, 1, 31)
+all_label = all_label.reshape(-1, 1, 50, 50)
+
+Xtest = torch.tensor(all_data)
+Labeltest = torch.tensor(all_label)
+
+test_data = Data.TensorDataset(Xtest, Labeltest)
+test_loader = Data.DataLoader(dataset=test_data,
+                              batch_size=1,
+                              shuffle=False)
+
+
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/inverse_design/cGAN/config.py",".py","196","13","import torch
+
+
+DEVICE = ""cuda"" if torch.cuda.is_available() else ""cpu""
+LEARNING_RATE = 2e-4
+BATCH_SIZE = 512
+NUM_WORKERS = 4
+L1_LAMBDA = 100
+NUM_EPOCHS = 500
+LOAD_MODEL = False
+SAVE_MODEL = True
+
+","Python"
+"Metamaterial","Clarkxielf/Dispersion-relation-prediction-and-structure-inverse-design-of-elastic-metamaterials","Code_PyTorch/inverse_design/CNN/model.py",".py","1655","58","import torch
+import torch.nn as nn
+
+
+__all__ = ['CNN']
+
+
+class CNN(nn.Module):
+
+    def __init__(self, num_classes=31):
+        super(CNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(1, 128, kernel_size=5, stride=1, padding=2),   # N*50*50
+            # nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # N*25*25
+
+
+            nn.Conv2d(128, 512, kernel_size=3, padding=1),           # N*25*25
+            # nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),                  # N*12*12
+
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),          # N*12*12
+            # nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+
+
+            nn.Conv2d(512, 1024, kernel_size=3, padding=1),          # N*12*12
+            # nn.BatchNorm2d(512),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # N*6*6
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((3, 3))
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(1024 * 3 * 3, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 2048),
+            nn.ReLU(inplace=True),
+            nn.Linear(2048, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x
+
+def test():
+    x = torch.randn(1, 1, 50, 50)
+    model = CNN(num_classes=31)
+    print(model(x).shape)
+
+if __name__=='__main__':
+    test()","Python"
+"Metamaterial","deveringham/metalens_optimization","farfield_optimization_tensorflow.ipynb",".ipynb","152126","492","{
+ ""cells"": [
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""db98c09c"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Metalens Optimization for the Far Field Design Case, using TensorFlow\n"",
+    ""\n"",
+    ""This notebook demonstrates the use of the metalens optmization library for the COPILOT far field design case.\n"",
+    ""TensorFlow is used as the backend framework for automatic differentiation.""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""464d06b1"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Compute Devices""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""4f5bf556"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 18:42:53.530348: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"",
+      ""2023-03-17 18:42:53.666396: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:42:53.666414: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"",
+      ""2023-03-17 18:42:54.262255: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:42:54.262300: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:42:54.262305: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Choose which device to use.\n"",
+    ""use_GPU = False\n"",
+    ""tfDevice = '/job:localhost/replica:0/task:0/device:GPU:1' if use_GPU else '/CPU:0'\n"",
+    ""\n"",
+    ""# Import device utils.\n"",
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_tf/src/')\n"",
+    ""import utils\n"",
+    ""\n"",
+    ""if use_GPU: \n"",
+    ""    # Configure GPUs.\n"",
+    ""    utils.config_gpu_memory_usage()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""3fbbb1a3"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Dependencies""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""c42677c1"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 18:42:56.124806: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:42:56.124825: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n"",
+      ""2023-03-17 18:42:56.124840: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dylan-XPS-13-7390): /proc/driver/nvidia/version does not exist\n"",
+      ""2023-03-17 18:42:56.125046: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""import tensorflow as tf\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import time\n"",
+    ""import solver\n"",
+    ""import solver_metasurface""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""bc053a93"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Optimization Parameters\n"",
+    ""\n"",
+    ""Change the parameters here in order to configure the design case being optimized for, choose algorithm hyperparameters, or set logging behavior.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""0d501ddf"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""# Initialize parameters dictionary.\n"",
+    ""user_params = {}\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Algorithm hyperparameters.\n"",
+    ""# These values subject to a hyperparameter grid search. These default values are used if the grid search is disabled.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Maximum number of optimization iterations.\n"",
+    ""user_params['N'] = 10\n"",
+    ""\n"",
+    ""# Coefficient used for differentiable thresholding annealing.\n"",
+    ""# At each optimization step, the coefficient of the sigmoid function used to force admissable solutions\n"",
+    ""# is increased by the increment N / sigmoid_update.\n"",
+    ""user_params['sigmoid_update'] = 10.0\n"",
+    ""\n"",
+    ""# Learning rate provided to Keras optimizer.\n"",
+    ""user_params['learning_rate'] = 8E-1\n"",
+    ""\n"",
+    ""# Initial height of each of the device pixels.\n"",
+    ""# Should be in the range [0, Nlay-1], where Nlay is the number of device layers\n"",
+    ""# (specified as the length of the parameter L below).\n"",
+    ""user_params['initial_height'] = 0\n"",
+    ""\n"",
+    ""# Flag to enable hyperparameter grid search.\n"",
+    ""user_params['enable_hyperparameter_gridsearch'] = False\n"",
+    ""\n"",
+    ""# Values to use in hyperparameter grid search.\n"",
+    ""# Stored as a dict. Each dict key is the key in user_params corresponding to a tunable hyperparameter, i.e. 'N',\n"",
+    ""# and its value is a list of values to try for that hyperparameter.\n"",
+    ""param_grid = {'N': [10],\n"",
+    ""              'sigmoid_update': [10.0, 20.0],\n"",
+    ""              'learning_rate': [8E-1],\n"",
+    ""              'initial_height': [0]}\n"",
+    ""user_params['param_grid'] = param_grid\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Source parameters.\n"",
+    ""# These values specify input light sources. Each is a list, and all should be the same length.\n"",
+    ""# The length of these lists is the number of souces over which the device should be optimized.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Wavelength (um).\n"",
+    ""user_params['wavelengths'] = [158.0]\n"",
+    ""\n"",
+    ""# Orientation (radians).\n"",
+    ""user_params['thetas'] = [0.0]\n"",
+    ""user_params['phis'] = [0.0]\n"",
+    ""\n"",
+    ""# Source polarization.\n"",
+    ""user_params['pte'] = [1.0]\n"",
+    ""user_params['ptm'] = [0.0]\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Device parmeters.\n"",
+    ""# These values specify device design parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of device 'pixels', i.e. square regions of constant height, in each direction.\n"",
+    ""user_params['pixelsX'] = 18\n"",
+    ""user_params['pixelsY'] = user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Relative permittivity of the non-vacuum, constituent material of the device layers.\n"",
+    ""user_params['erd'] = 11.9\n"",
+    ""\n"",
+    ""# Relative permittivity of the substrate layer.\n"",
+    ""user_params['ers'] = user_params['erd']\n"",
+    ""\n"",
+    ""# Thickness of each layer (um). L[0] corresponds to layer closest to the source, and L[-1] to the substrate layer.\n"",
+    ""# The length of this list is used to specify the number of device layers.\n"",
+    ""user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 250.0]\n"",
+    ""\n"",
+    ""# Length of each pixel in the x direction (um).\n"",
+    ""user_params['Lx'] = 5000.0 / user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Length of each pixel in the y direction (um).\n"",
+    ""user_params['Ly'] = user_params['Lx']\n"",
+    ""\n"",
+    ""# Focal distance (nm).\n"",
+    ""user_params['f'] = 30000000.0\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Solver parameters.\n"",
+    ""# These values specify the behavior of RCWA.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of spatial harmonics used by RCWA in each transverse direction.\n"",
+    ""user_params['PQ'] = [3,3]\n"",
+    ""\n"",
+    ""# Upsampling rate (per pixel) used when simulating scattering from the device.\n"",
+    ""user_params['upsample'] = 11\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Logging parameters.\n"",
+    ""# These values are used to configure logging behavior.\n"",
+    ""##########\n"",
+    ""user_params['enable_logging'] = False\n"",
+    ""\n"",
+    ""# Logfile name.\n"",
+    ""user_params['parameter_string'] = 'N' + str(user_params['N']) \\\n"",
+    ""    + '-sigmoid_update' + str(user_params['sigmoid_update']) \\\n"",
+    ""    + '-learning_rate' + str(user_params['learning_rate']) \\\n"",
+    ""    + '-initial_height' + str(user_params['initial_height'])\n"",
+    ""user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'\n"",
+    ""user_params['log_filename_extension'] = '.txt'\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Misc. parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Initial value of the sigmoid coefficient.\n"",
+    ""user_params['sigmoid_coeff'] = 1.0\n"",
+    ""\n"",
+    ""# Radius of the focal spot used in the loss function, where 16 = width of one pixel.\n"",
+    ""user_params['focal_spot_radius'] = 10\n"",
+    ""\n"",
+    ""# Flag to enable random initial guess for the metasurface shape.\n"",
+    ""user_params['enable_random_init'] = False\n"",
+    ""\n"",
+    ""# Flags to enable some debug behaviors.\n"",
+    ""user_params['enable_debug'] = False\n"",
+    ""user_params['enable_print'] = True""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""280c2f49"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Loss Function Definition\n"",
+    ""\n"",
+    ""This loss function incentivises intensity focused at the center of the focal plane. Change in order to optimize for a different objective.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""id"": ""4e7180e0"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""    l1 = tf.math.reduce_sum(tf.abs(focal_plane[0, index-r:index+r, index-r:index+r]))\n"",
+    ""\n"",
+    ""    # Final loss: (negative) field intensity at focal point.\n"",
+    ""    return -1.0*l1\n"",
+    ""\n"",
+    ""# Set loss function.\n"",
+    ""user_params['loss_function'] = loss_function""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""fc0a4418"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Perform Experiments\n"",
+    ""\n"",
+    ""### Single Optimization Run""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""b30660aa"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizing... 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, Done.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# This context gurantees that Tensorflow executes all operations on the specified device.\n"",
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    if not user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""        # Optimize.\n"",
+    ""        h, loss, params, focal_plane = solver_metasurface.optimize_device(user_params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""b896b9bd"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Hyperparameter Grid Search""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""2c823798"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    if user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""        # Perform the hyperparameter grid search.\n"",
+    ""        results = solver_metasurface.hyperparameter_gridsearch(user_params)\n"",
+    ""\n"",
+    ""        # Get list of evaluation scores.\n"",
+    ""        scores = [r['eval_score'] for r in results]\n"",
+    ""\n"",
+    ""        # Select hyperparameters and results corresponding to best evaluation score.\n"",
+    ""        result = results[np.argmax(scores)]\n"",
+    ""        h = result['h']\n"",
+    ""        loss = result['loss']\n"",
+    ""        focal_plane = result['focal_plane']\n"",
+    ""        eval_score = result['eval_score']\n"",
+    ""        params = result['params']\n"",
+    ""\n"",
+    ""        print('Best hyperparameters: ' + str(result['hyperparameters']))\n"",
+    ""        print('With evaluation score: ' + f'{eval_score:.2f}')""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""27c33419"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Visualize Results""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""459ded9c"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""a582734e"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 2 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    plt.imshow(tf.abs(focal_plane[0, :, :]) ** 2)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""ed999792"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Learning Curve""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""id"": ""83337ea7"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    plt.plot(loss)\n"",
+    ""    plt.xlabel('Iterations')\n"",
+    ""    plt.ylabel('Loss')\n"",
+    ""    plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""f70afeb4"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""id"": ""a30387ed"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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"",
+      ""text/plain"": [
+       ""<Figure size 1200x1200 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    ER_t, UR_t = solver_metasurface.generate_layered_metasurface(h, params)\n"",
+    ""    solver_metasurface.display_layered_metasurface(ER_t, params)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""id"": ""37c34603"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": []
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","read_results_farfield.ipynb",".ipynb","83952","288","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""406cecc9"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_pt/src/')\n"",
+    ""import utils\n"",
+    ""import torch\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import matplotlib.colors as colors\n"",
+    ""import time\n"",
+    ""import solver_pt\n"",
+    ""import solver_metasurface_pt""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""4264826c"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Results for the Far Field Design Case\n"",
+    ""\n"",
+    ""This notebook can be used to read in and visualize results from metalens optimization runs.\n"",
+    ""Results produced by the other notebooks in this directory, for example \""farfield_optimization_pytorch.ipynb\"", are stored in log files in the ./results directory.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""fcef7207"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""./results/farfield_040822/farfield-180x180-N50-sigmoid_update40.0-learning_rate0.8-initial_height0.txt\n"",
+      ""{'batchSize': 1, 'pixelsX': 180, 'pixelsY': 180, 'Nlay': 6, 'Nx': 16, 'Ny': 16, 'ers': 11.9, 'urd': 1.0, 'eps_min': 1.0, 'eps_max': 11.9, 'sigmoid_coeff': 41.0}\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Set this flag if you want to find the best result out of all those stored in ./results.\n"",
+    ""find_best = False\n"",
+    ""\n"",
+    ""# Get list of files.\n"",
+    ""dirs = ['./results/farfield_040822/', './results/farfield_120822/', './results/farfield_170822/']\n"",
+    ""files = [[os.path.join(d, f) for f in os.listdir(d) if os.path.isfile(os.path.join(d, f))] for d in dirs]\n"",
+    ""files = [f for d in files for f in d]\n"",
+    ""\n"",
+    ""# Find the best result.\n"",
+    ""if find_best:\n"",
+    ""    eval_scores = [solver_metasurface_pt.load_result(f)['eval_score'] for f in files]\n"",
+    ""    idx_best = np.argmin(eval_scores)\n"",
+    ""    filename = files[idx_best]\n"",
+    ""\n"",
+    ""# Otherwise, read a specific file.\n"",
+    ""filename = './results/farfield_040822/farfield-180x180-N50-sigmoid_update40.0-learning_rate0.8-initial_height0.txt'\n"",
+    ""print(filename)\n"",
+    ""\n"",
+    ""result = solver_metasurface_pt.load_result(filename)\n"",
+    ""\n"",
+    ""loss = result['loss']\n"",
+    ""focal_plane = result['focal_plane']\n"",
+    ""h = result['h']\n"",
+    ""\n"",
+    ""batchSize = 1\n"",
+    ""pixelsX = h.shape[0]\n"",
+    ""pixelsY = h.shape[1]\n"",
+    ""Nlay = 6\n"",
+    ""Nx = 16\n"",
+    ""Ny = 16\n"",
+    ""ers = 11.9\n"",
+    ""urd = 1.0\n"",
+    ""eps_min = 1.0\n"",
+    ""eps_max = 11.9\n"",
+    ""sigmoid_coeff = result['hyperparameters'][1] + 1\n"",
+    ""\n"",
+    ""params = {'batchSize' : batchSize,\n"",
+    ""         'pixelsX' : pixelsX,\n"",
+    ""         'pixelsY' : pixelsY,\n"",
+    ""         'Nlay' : Nlay,\n"",
+    ""         'Nx' : Nx,\n"",
+    ""         'Ny' : Ny,\n"",
+    ""         'ers' : ers,\n"",
+    ""         'urd' : urd,\n"",
+    ""         'eps_min' : eps_min,\n"",
+    ""         'eps_max' : eps_max,\n"",
+    ""         'sigmoid_coeff' : sigmoid_coeff}\n"",
+    ""print(params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""d97ae59c"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Plot Loss""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""c7601666"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.plot(loss)\n"",
+    ""plt.xlabel('Optimization Iterations')\n"",
+    ""plt.ylabel('Loss')\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""7a1c6ab7"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""9f67b513"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""ename"": ""NameError"",
+     ""evalue"": ""name 'plt' is not defined"",
+     ""output_type"": ""error"",
+     ""traceback"": [
+      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
+      ""\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)"",
+      ""Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mimshow(torch\u001b[38;5;241m.\u001b[39mabs(focal_plane[\u001b[38;5;241m0\u001b[39m, :, :]) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m      2\u001b[0m fig\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_xaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m fig\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_yaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n"",
+      ""\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined""
+     ]
+    }
+   ],
+   ""source"": [
+    ""fig = plt.imshow(torch.abs(focal_plane[0, :, :]) ** 2)\n"",
+    ""fig.axes.get_xaxis().set_visible(False)\n"",
+    ""fig.axes.get_yaxis().set_visible(False)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""f1d3e25b"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Focal Plane Cross Sections""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""bd2a7bb5"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""index = (pixelsY * 11) // 2\n"",
+    ""x = np.linspace(0, 5, focal_plane.shape[2])\n"",
+    ""plt.plot(x, torch.abs(focal_plane[0, index, :]) ** 2)\n"",
+    ""plt.xlabel('x (mm)')\n"",
+    ""plt.ylabel('Electric Field Intensity\\n on Focal Plane (N/C)')\n"",
+    ""plt.xlim(2.435,2.565)\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""0ae81a6a"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""index = (pixelsX * 11) // 2\n"",
+    ""x = np.linspace(0, 5, focal_plane.shape[2])\n"",
+    ""plt.plot(x, torch.abs(focal_plane[0, :, index]) ** 2)\n"",
+    ""plt.xlabel('y (mm)')\n"",
+    ""plt.ylabel('Electric Field Intensity\\n on Focal Plane (N/C)')\n"",
+    ""plt.xlim(2.435,2.565)\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""7e154d2d"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""5725dab2"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 864x864 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""solver_metasurface_pt.display_layered_metasurface(ER_t, params)""
+   ]
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","nearfield_optimization_pytorch.ipynb",".ipynb","156266","472","{
+ ""cells"": [
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""db98c09c"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Metalens Optimization for the Near Field Design Case, using PyTorch\n"",
+    ""\n"",
+    ""This notebook demonstrates the use of the metalens optmization library for the COPILOT near field design case.\n"",
+    ""PyTorch is used as the backend framework for automatic differentiation.""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""464d06b1"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Compute Devices""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""4f5bf556"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 18:50:19.854045: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"",
+      ""2023-03-17 18:50:19.975973: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:50:19.975990: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"",
+      ""2023-03-17 18:50:20.594173: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:50:20.594240: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:50:20.594250: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Choose which device to use.\n"",
+    ""use_GPU = False\n"",
+    ""\n"",
+    ""# Import device utils.\n"",
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_pt/src/')\n"",
+    ""import utils\n"",
+    ""\n"",
+    ""if use_GPU: \n"",
+    ""    # Configure GPUs.\n"",
+    ""    utils.config_gpu_memory_usage()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""3fbbb1a3"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Dependencies""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""c42677c1"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""import torch\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import time\n"",
+    ""import solver_pt\n"",
+    ""import solver_metasurface_pt""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""bc053a93"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Optimization Parameters\n"",
+    ""\n"",
+    ""Change the parameters here in order to configure the design case being optimized for, choose algorithm hyperparameters, or set logging behavior.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""0d501ddf"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""# Initialize parameters dictionary.\n"",
+    ""user_params = {}\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Algorithm hyperparameters.\n"",
+    ""# These values subject to a hyperparameter grid search. These default values are used if the grid search is disabled.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Maximum number of optimization iterations.\n"",
+    ""user_params['N'] = 10\n"",
+    ""\n"",
+    ""# Coefficient used for differentiable thresholding annealing.\n"",
+    ""# At each optimization step, the coefficient of the sigmoid function used to force admissable solutions\n"",
+    ""# is increased by the increment N / sigmoid_update.\n"",
+    ""user_params['sigmoid_update'] = 10.0\n"",
+    ""\n"",
+    ""# Learning rate provided to Keras optimizer.\n"",
+    ""user_params['learning_rate'] = 8E-1\n"",
+    ""\n"",
+    ""# Initial height of each of the device pixels.\n"",
+    ""# Should be in the range [0, Nlay-1], where Nlay is the number of device layers\n"",
+    ""# (specified as the length of the parameter L below).\n"",
+    ""user_params['initial_height'] = 0\n"",
+    ""\n"",
+    ""# Flag to enable hyperparameter grid search.\n"",
+    ""user_params['enable_hyperparameter_gridsearch'] = False\n"",
+    ""\n"",
+    ""# Values to use in hyperparameter grid search.\n"",
+    ""# Stored as a dict. Each dict key is the key in user_params corresponding to a tunable hyperparameter, i.e. 'N',\n"",
+    ""# and its value is a list of values to try for that hyperparameter.\n"",
+    ""param_grid = {'N': [10],\n"",
+    ""              'sigmoid_update': [10.0, 20.0],\n"",
+    ""              'learning_rate': [8E-1],\n"",
+    ""              'initial_height': [0]}\n"",
+    ""user_params['param_grid'] = param_grid\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Source parameters.\n"",
+    ""# These values specify input light sources. Each is a list, and all should be the same length.\n"",
+    ""# The length of these lists is the number of souces over which the device should be optimized.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Wavelength (um).\n"",
+    ""user_params['wavelengths'] = [120.0]\n"",
+    ""\n"",
+    ""# Orientation (radians).\n"",
+    ""user_params['thetas'] = [0.0]\n"",
+    ""user_params['phis'] = [0.0]\n"",
+    ""\n"",
+    ""# Source polarization.\n"",
+    ""user_params['pte'] = [1.0]\n"",
+    ""user_params['ptm'] = [0.0]\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Device parmeters.\n"",
+    ""# These values specify device design parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of device 'pixels', i.e. square regions of constant height, in each direction.\n"",
+    ""user_params['pixelsX'] = 18\n"",
+    ""user_params['pixelsY'] = user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Relative permittivity of the non-vacuum, constituent material of the device layers.\n"",
+    ""user_params['erd'] = 11.9\n"",
+    ""\n"",
+    ""# Relative permittivity of the substrate layer.\n"",
+    ""user_params['ers'] = user_params['erd']\n"",
+    ""\n"",
+    ""# Thickness of each layer (um). L[0] corresponds to layer closest to the source, and L[-1] to the substrate layer.\n"",
+    ""# The length of this list is used to specify the number of device layers.\n"",
+    ""user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 950.0]\n"",
+    ""\n"",
+    ""# Length of each pixel in the x direction (um).\n"",
+    ""user_params['Lx'] = 5000.0 / user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Length of each pixel in the y direction (um).\n"",
+    ""user_params['Ly'] = user_params['Lx']\n"",
+    ""\n"",
+    ""# Focal distance (nm).\n"",
+    ""user_params['f'] = 0.0\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Solver parameters.\n"",
+    ""# These values specify the behavior of RCWA.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of spatial harmonics used by RCWA in each transverse direction.\n"",
+    ""user_params['PQ'] = [3,3]\n"",
+    ""\n"",
+    ""# Upsampling rate (per pixel) used when simulating scattering from the device.\n"",
+    ""user_params['upsample'] = 11\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Logging parameters.\n"",
+    ""# These values are used to configure logging behavior.\n"",
+    ""##########\n"",
+    ""user_params['enable_logging'] = False\n"",
+    ""\n"",
+    ""# Logfile name.\n"",
+    ""user_params['parameter_string'] = 'N' + str(user_params['N']) \\\n"",
+    ""    + '-sigmoid_update' + str(user_params['sigmoid_update']) \\\n"",
+    ""    + '-learning_rate' + str(user_params['learning_rate']) \\\n"",
+    ""    + '-initial_height' + str(user_params['initial_height'])\n"",
+    ""user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'\n"",
+    ""user_params['log_filename_extension'] = '.txt'\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Misc. parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Initial value of the sigmoid coefficient.\n"",
+    ""user_params['sigmoid_coeff'] = 1.0\n"",
+    ""\n"",
+    ""# Radius of the focal spot used in the loss function, where 16 = width of one pixel.\n"",
+    ""user_params['focal_spot_radius'] = 10\n"",
+    ""\n"",
+    ""# Flag to enable random initial guess for the metasurface shape.\n"",
+    ""user_params['enable_random_init'] = False\n"",
+    ""\n"",
+    ""# Flags to enable some debug behaviors.\n"",
+    ""user_params['enable_debug'] = False\n"",
+    ""user_params['enable_print'] = True""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""280c2f49"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Loss Function Definition\n"",
+    ""\n"",
+    ""This loss function incentivises intensity focused at the center of the focal plane. Change in order to optimize for a different objective.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""id"": ""4e7180e0"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver_pt.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""    l1 = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]))\n"",
+    ""\n"",
+    ""    # Final loss: (negative) field intensity at focal point.\n"",
+    ""    return -1.0*l1\n"",
+    ""\n"",
+    ""# Set loss function.\n"",
+    ""user_params['loss_function'] = loss_function""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""fc0a4418"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Perform Experiments\n"",
+    ""\n"",
+    ""### Single Optimization Run""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""b30660aa"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizing... 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, Done.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""if not user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""    \n"",
+    ""    # Optimize.\n"",
+    ""    h, loss, params, focal_plane = solver_metasurface_pt.optimize_device(user_params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""b896b9bd"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Hyperparameter Grid Search""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""2c823798"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""if user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""    \n"",
+    ""    # Perform the hyperparameter grid search.\n"",
+    ""    results = solver_metasurface_pt.hyperparameter_gridsearch(user_params)\n"",
+    ""\n"",
+    ""    # Get list of evaluation scores.\n"",
+    ""    scores = [r['eval_score'] for r in results]\n"",
+    ""\n"",
+    ""    # Select hyperparameters and results corresponding to best evaluation score.\n"",
+    ""    result = results[np.argmax(scores)]\n"",
+    ""    h = result['h']\n"",
+    ""    loss = result['loss']\n"",
+    ""    focal_plane = result['focal_plane']\n"",
+    ""    eval_score = result['eval_score']\n"",
+    ""    params = result['params']\n"",
+    ""    \n"",
+    ""    print('Best hyperparameters: ' + str(result['hyperparameters']))\n"",
+    ""    print('With evaluation score: ' + f'{eval_score:.2f}')""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""27c33419"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Visualize Results""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""459ded9c"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""a582734e"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""<matplotlib.image.AxesImage at 0x7f82bda26b60>""
+      ]
+     },
+     ""execution_count"": 7,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    },
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""intensity = torch.abs(focal_plane[0, :, :]) ** 2\n"",
+    ""intensity = intensity.detach().cpu().numpy()\n"",
+    ""plt.imshow(intensity)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""ed999792"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Learning Curve""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""id"": ""83337ea7"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.plot(loss)\n"",
+    ""plt.xlabel('Iterations')\n"",
+    ""plt.ylabel('Loss')\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""f70afeb4"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""id"": ""a30387ed"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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"",
+      ""text/plain"": [
+       ""<Figure size 1200x1200 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""solver_metasurface_pt.display_layered_metasurface(ER_t, params)""
+   ]
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","read_results_nearfield.ipynb",".ipynb","83959","288","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""406cecc9"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_pt/src/')\n"",
+    ""import utils\n"",
+    ""import torch\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import matplotlib.colors as colors\n"",
+    ""import time\n"",
+    ""import solver_pt\n"",
+    ""import solver_metasurface_pt""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""bbc506d8"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Results for the Near Field Design Case\n"",
+    ""\n"",
+    ""This notebook can be used to read in and visualize results from metalens optimization runs.\n"",
+    ""Results produced by the other notebooks in this directory, for example \""nearfield_optimization_pytorch.ipynb\"", are stored in log files in the ./results directory.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""fcef7207"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""./results/farfield_040822/farfield-180x180-N50-sigmoid_update40.0-learning_rate0.8-initial_height0.txt\n"",
+      ""{'batchSize': 1, 'pixelsX': 180, 'pixelsY': 180, 'Nlay': 6, 'Nx': 16, 'Ny': 16, 'ers': 11.9, 'urd': 1.0, 'eps_min': 1.0, 'eps_max': 11.9, 'sigmoid_coeff': 41.0}\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Set this flag if you want to find the best result out of all those stored in ./results.\n"",
+    ""find_best = False\n"",
+    ""\n"",
+    ""# Get list of files.\n"",
+    ""dirs = ['./results/nearfield_040822/', './results/nearfield_120822/', './results/nearfield_170822/']\n"",
+    ""files = [[os.path.join(d, f) for f in os.listdir(d) if os.path.isfile(os.path.join(d, f))] for d in dirs]\n"",
+    ""files = [f for d in files for f in d]\n"",
+    ""\n"",
+    ""# Find the best result.\n"",
+    ""if find_best:\n"",
+    ""    eval_scores = [solver_metasurface_pt.load_result(f)['eval_score'] for f in files]\n"",
+    ""    idx_best = np.argmin(eval_scores)\n"",
+    ""    filename = files[idx_best]\n"",
+    ""\n"",
+    ""# Otherwise, read a specific file.\n"",
+    ""filename = './results/nearfield_040822/nearfield-180x180-N50-sigmoid_update40.0-learning_rate0.8-initial_height0.txt'\n"",
+    ""print(filename)\n"",
+    ""\n"",
+    ""result = solver_metasurface_pt.load_result(filename)\n"",
+    ""\n"",
+    ""loss = result['loss']\n"",
+    ""focal_plane = result['focal_plane']\n"",
+    ""h = result['h']\n"",
+    ""\n"",
+    ""batchSize = 1\n"",
+    ""pixelsX = h.shape[0]\n"",
+    ""pixelsY = h.shape[1]\n"",
+    ""Nlay = 6\n"",
+    ""Nx = 16\n"",
+    ""Ny = 16\n"",
+    ""ers = 11.9\n"",
+    ""urd = 1.0\n"",
+    ""eps_min = 1.0\n"",
+    ""eps_max = 11.9\n"",
+    ""sigmoid_coeff = result['hyperparameters'][1] + 1\n"",
+    ""\n"",
+    ""params = {'batchSize' : batchSize,\n"",
+    ""         'pixelsX' : pixelsX,\n"",
+    ""         'pixelsY' : pixelsY,\n"",
+    ""         'Nlay' : Nlay,\n"",
+    ""         'Nx' : Nx,\n"",
+    ""         'Ny' : Ny,\n"",
+    ""         'ers' : ers,\n"",
+    ""         'urd' : urd,\n"",
+    ""         'eps_min' : eps_min,\n"",
+    ""         'eps_max' : eps_max,\n"",
+    ""         'sigmoid_coeff' : sigmoid_coeff}\n"",
+    ""print(params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""9f4e7d6e"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Plot Loss""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""c7601666"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.plot(loss)\n"",
+    ""plt.xlabel('Optimization Iterations')\n"",
+    ""plt.ylabel('Loss')\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""2a08e016"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""9f67b513"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""ename"": ""NameError"",
+     ""evalue"": ""name 'plt' is not defined"",
+     ""output_type"": ""error"",
+     ""traceback"": [
+      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
+      ""\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)"",
+      ""Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mimshow(torch\u001b[38;5;241m.\u001b[39mabs(focal_plane[\u001b[38;5;241m0\u001b[39m, :, :]) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m      2\u001b[0m fig\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_xaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m fig\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_yaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n"",
+      ""\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined""
+     ]
+    }
+   ],
+   ""source"": [
+    ""fig = plt.imshow(torch.abs(focal_plane[0, :, :]) ** 2)\n"",
+    ""fig.axes.get_xaxis().set_visible(False)\n"",
+    ""fig.axes.get_yaxis().set_visible(False)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""89604264"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Focal Plane Cross Sections""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""bd2a7bb5"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""index = (pixelsY * 11) // 2\n"",
+    ""x = np.linspace(0, 5, focal_plane.shape[2])\n"",
+    ""plt.plot(x, torch.abs(focal_plane[0, index, :]) ** 2)\n"",
+    ""plt.xlabel('x (mm)')\n"",
+    ""plt.ylabel('Electric Field Intensity\\n on Focal Plane (N/C)')\n"",
+    ""plt.xlim(2.435,2.565)\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""0ae81a6a"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 432x288 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""index = (pixelsX * 11) // 2\n"",
+    ""x = np.linspace(0, 5, focal_plane.shape[2])\n"",
+    ""plt.plot(x, torch.abs(focal_plane[0, :, index]) ** 2)\n"",
+    ""plt.xlabel('y (mm)')\n"",
+    ""plt.ylabel('Electric Field Intensity\\n on Focal Plane (N/C)')\n"",
+    ""plt.xlim(2.435,2.565)\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""85727e84"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""5725dab2"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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\n"",
+      ""text/plain"": [
+       ""<Figure size 864x864 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {
+      ""needs_background"": ""light""
+     },
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""solver_metasurface_pt.display_layered_metasurface(ER_t, params)""
+   ]
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","farfield_optimization_pytorch.ipynb",".ipynb","194068","566","{
+ ""cells"": [
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""db98c09c"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Metalens Optimization for the Far Field Design Case, using PyTorch\n"",
+    ""\n"",
+    ""This notebook demonstrates the use of the metalens optmization library for the COPILOT far field design case.\n"",
+    ""PyTorch is used as the backend framework for automatic differentiation.""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""464d06b1"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Compute Devices""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""4f5bf556"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-21 11:24:25.069268: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"",
+      ""2023-03-21 11:24:25.196529: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-21 11:24:25.196551: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"",
+      ""2023-03-21 11:24:25.996109: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-21 11:24:25.996162: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-21 11:24:25.996169: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Choose which device to use.\n"",
+    ""use_GPU = False\n"",
+    ""tfDevice = '/job:localhost/replica:0/task:0/device:GPU:1' if use_GPU else '/CPU:0'\n"",
+    ""\n"",
+    ""# Import device utils.\n"",
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_pt/src/')\n"",
+    ""import utils\n"",
+    ""\n"",
+    ""if use_GPU: \n"",
+    ""    # Configure GPUs.\n"",
+    ""    utils.config_gpu_memory_usage()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""3fbbb1a3"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Dependencies""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""c42677c1"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""import torch\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import time\n"",
+    ""import solver_pt\n"",
+    ""import solver_metasurface_pt""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""bc053a93"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Optimization Parameters\n"",
+    ""\n"",
+    ""Change the parameters here in order to configure the design case being optimized for, choose algorithm hyperparameters, or set logging behavior.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""0d501ddf"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""# Initialize parameters dictionary.\n"",
+    ""user_params = {}\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Algorithm hyperparameters.\n"",
+    ""# These values subject to a hyperparameter grid search. These default values are used if the grid search is disabled.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Maximum number of optimization iterations.\n"",
+    ""user_params['N'] = 10\n"",
+    ""\n"",
+    ""# Coefficient used for differentiable thresholding annealing.\n"",
+    ""# At each optimization step, the coefficient of the sigmoid function used to force admissable solutions\n"",
+    ""# is increased by the increment N / sigmoid_update.\n"",
+    ""user_params['sigmoid_update'] = 10.0\n"",
+    ""\n"",
+    ""# Learning rate provided to Keras optimizer.\n"",
+    ""user_params['learning_rate'] = 8E-1\n"",
+    ""\n"",
+    ""# Initial height of each of the device pixels.\n"",
+    ""# Should be in the range [0, Nlay-1], where Nlay is the number of device layers\n"",
+    ""# (specified as the length of the parameter L below).\n"",
+    ""user_params['initial_height'] = 0\n"",
+    ""\n"",
+    ""# Flag to enable hyperparameter grid search.\n"",
+    ""user_params['enable_hyperparameter_gridsearch'] = False\n"",
+    ""\n"",
+    ""# Values to use in hyperparameter grid search.\n"",
+    ""# Stored as a dict. Each dict key is the key in user_params corresponding to a tunable hyperparameter, i.e. 'N',\n"",
+    ""# and its value is a list of values to try for that hyperparameter.\n"",
+    ""param_grid = {'N': [10],\n"",
+    ""              'sigmoid_update': [10.0, 20.0],\n"",
+    ""              'learning_rate': [8E-1],\n"",
+    ""              'initial_height': [0]}\n"",
+    ""user_params['param_grid'] = param_grid\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Source parameters.\n"",
+    ""# These values specify input light sources. Each is a list, and all should be the same length.\n"",
+    ""# The length of these lists is the number of souces over which the device should be optimized.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Wavelength (um).\n"",
+    ""user_params['wavelengths'] = [158.0]\n"",
+    ""\n"",
+    ""# Orientation (radians).\n"",
+    ""user_params['thetas'] = [0.0]\n"",
+    ""user_params['phis'] = [0.0]\n"",
+    ""\n"",
+    ""# Source polarization.\n"",
+    ""user_params['pte'] = [1.0]\n"",
+    ""user_params['ptm'] = [0.0]\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Device parmeters.\n"",
+    ""# These values specify device design parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of device 'pixels', i.e. square regions of constant height, in each direction.\n"",
+    ""user_params['pixelsX'] = 18\n"",
+    ""user_params['pixelsY'] = user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Relative permittivity of the non-vacuum, constituent material of the device layers.\n"",
+    ""user_params['erd'] = 11.9\n"",
+    ""\n"",
+    ""# Relative permittivity of the substrate layer.\n"",
+    ""user_params['ers'] = user_params['erd']\n"",
+    ""\n"",
+    ""# Thickness of each layer (um). L[0] corresponds to layer closest to the source, and L[-1] to the substrate layer.\n"",
+    ""# The length of this list is used to specify the number of device layers.\n"",
+    ""user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 250.0]\n"",
+    ""\n"",
+    ""# Length of each pixel in the x direction (um).\n"",
+    ""user_params['Lx'] = 5000.0 / user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Length of each pixel in the y direction (um).\n"",
+    ""user_params['Ly'] = user_params['Lx']\n"",
+    ""\n"",
+    ""# Focal distance (nm).\n"",
+    ""user_params['f'] = 30000000.0\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Solver parameters.\n"",
+    ""# These values specify the behavior of RCWA.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of spatial harmonics used by RCWA in each transverse direction.\n"",
+    ""user_params['PQ'] = [3,3]\n"",
+    ""\n"",
+    ""# Upsampling rate (per pixel) used when simulating scattering from the device.\n"",
+    ""user_params['upsample'] = 11\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Logging parameters.\n"",
+    ""# These values are used to configure logging behavior.\n"",
+    ""##########\n"",
+    ""user_params['enable_logging'] = False\n"",
+    ""\n"",
+    ""# Logfile name.\n"",
+    ""user_params['parameter_string'] = 'N' + str(user_params['N']) \\\n"",
+    ""    + '-sigmoid_update' + str(user_params['sigmoid_update']) \\\n"",
+    ""    + '-learning_rate' + str(user_params['learning_rate']) \\\n"",
+    ""    + '-initial_height' + str(user_params['initial_height'])\n"",
+    ""user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'\n"",
+    ""user_params['log_filename_extension'] = '.txt'\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Misc. parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Initial value of the sigmoid coefficient.\n"",
+    ""user_params['sigmoid_coeff'] = 1.0\n"",
+    ""\n"",
+    ""# Radius of the focal spot used in the loss function, where 16 = width of one pixel.\n"",
+    ""user_params['focal_spot_radius'] = 10\n"",
+    ""\n"",
+    ""# Flag to enable random initial guess for the metasurface shape.\n"",
+    ""user_params['enable_random_init'] = False\n"",
+    ""\n"",
+    ""# Flags to enable some debug behaviors.\n"",
+    ""user_params['enable_debug'] = False\n"",
+    ""user_params['enable_print'] = True""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""280c2f49"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Loss Function Definition\n"",
+    ""\n"",
+    ""Here I give several possible loss functions which each incentivize a different type of lens.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""id"": ""4e7180e0"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function_centered(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver_pt.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # Calculate sum of electric field magnitude within some radius of the center of the focal plane.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""    l1 = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]))\n"",
+    ""    \n"",
+    ""    return -1.0*l1""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""6b3c693d"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function_noncentered(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver_pt.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # Calculate sum of electric field magnitude within some radius of the desired focal point,\n"",
+    ""    # in the upper right quadrant of the focal plane.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index = (params['pixelsX'] * params['upsample']) // 4\n"",
+    ""    l1 = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]))\n"",
+    ""\n"",
+    ""    return -1.0*l1""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""fea6493e"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function_doublefocus(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver_pt.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # Calculate sum of electric field magnitude within some radius of the first desired focal point,\n"",
+    ""    # in the upper right quadrant of the focal plane.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index1 = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""    index2 = (params['pixelsX'] * params['upsample']) // 4\n"",
+    ""    l1 = torch.sum(torch.abs(focal_plane[0, index1-r:index1+r, index1-index2-r:index1-index2+r]))\n"",
+    ""    \n"",
+    ""    # Then do the same for the seconf focal point.\n"",
+    ""    l2 = torch.sum(torch.abs(focal_plane[0, index1-r:index1+r, index1+index2-r:index1+index2+r]))\n"",
+    ""\n"",
+    ""    # Return the sum of these terms as a final loss.\n"",
+    ""    return -1.0*l1 - l2""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""fd3ed409"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""# Set loss function.\n"",
+    ""user_params['loss_function'] = loss_function_doublefocus""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""fc0a4418"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Perform Experiments\n"",
+    ""\n"",
+    ""### Single Optimization Run""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""id"": ""b30660aa"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizing... 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, Done.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""if not user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""    # Optimize.\n"",
+    ""    h, loss, params, focal_plane, h_list = solver_metasurface_pt.optimize_device(user_params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""b896b9bd"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Hyperparameter Grid Search""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""id"": ""2c823798"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""if user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""    # Perform the hyperparameter grid search.\n"",
+    ""    results = solver_metasurface_pt.hyperparameter_gridsearch(user_params)\n"",
+    ""\n"",
+    ""    # Get list of evaluation scores.\n"",
+    ""    scores = [r['eval_score'] for r in results]\n"",
+    ""\n"",
+    ""    # Select hyperparameters and results corresponding to best evaluation score.\n"",
+    ""    result = results[np.argmax(scores)]\n"",
+    ""    h = result['h']\n"",
+    ""    loss = result['loss']\n"",
+    ""    focal_plane = result['focal_plane']\n"",
+    ""    eval_score = result['eval_score']\n"",
+    ""    params = result['params']\n"",
+    ""\n"",
+    ""    print('Best hyperparameters: ' + str(result['hyperparameters']))\n"",
+    ""    print('With evaluation score: ' + f'{eval_score:.2f}')""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""27c33419"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Visualize Results""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""459ded9c"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 10,
+   ""id"": ""a582734e"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""text/plain"": [
+       ""<matplotlib.image.AxesImage at 0x7f23f29b1a20>""
+      ]
+     },
+     ""execution_count"": 10,
+     ""metadata"": {},
+     ""output_type"": ""execute_result""
+    },
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""intensity = torch.abs(focal_plane[0, :, :]) ** 2\n"",
+    ""intensity = intensity.detach().cpu().numpy()\n"",
+    ""plt.imshow(intensity)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""ed999792"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Learning Curve""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 11,
+   ""id"": ""83337ea7"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""plt.plot(loss)\n"",
+    ""plt.xlabel('Iterations')\n"",
+    ""plt.ylabel('Loss')\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""f70afeb4"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 12,
+   ""id"": ""a30387ed"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": ""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"",
+      ""text/plain"": [
+       ""<Figure size 1200x1200 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""solver_metasurface_pt.display_layered_metasurface(ER_t, params)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 13,
+   ""id"": ""37c34603"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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5vWv9HDJ6BhcXzi+f/lz+/rXbtrRdfOXJd85e8sH6x8Nv2b7723OLjLn7ksZHq6fzcc4rrz/7U4/WLVVW7tP7gV0eqJyLiAw/dXbv293/2+uK5a51v9T/esBkY92egWx++v3bthosuG7muktZC/Y931draRJ5zkGnUNIlr37T+jxh/Bv7koXtq195y8RUjVsWwsq//sDMwVMg889b4QuyJhZaQ2Xj/62ehpvzJxKD+n5/fWz5/vfDnNGPM08KeffVrO2xOJ/VaFubq7001X37cVt3zNqz/IwbPQOk6R0QcbBf+nGaM+1ucvcL9md9b328Rg1/PyI9bqrcQMmt7cQjFaz/odZqBiBj/Z6BJ9X9Jq1UIdFO6b9OoaSLXvmH9H7EzZ4CnpF//IWegGX9MDgAAwLYQMgEAAEgjZAIAAJBGyAQAACDNUB/8A5z+BM1+H5TR2zfgU0of/tpE6jnw8Qdq11Z/pv4TO0+eO53/yX7v4/Wfslt6Lb0xnnPt0fpPCZ172SXFcxcuWKwp6GREZ4yidrCFpcW+M/Chv/zT4nlvuOCVE6nnm3/zvNq1xZvr5+7Znyr/fvXxV4/2Uf3P/tT3iuut736/dq1z04W1az/+wMMj1RMR8cbCtf/o0Y8Uz33TT/58/4XeyYjRPxh717m9c09x/drFKybyvPMHD9aura+sTOQ5x1H6BNlJvZbStZ/WfduNBl/Ll29PIfT1hgvqr/8k58A7mQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABp7JMJQ1o//5xoze/bcrz6/BenUE1Er9utXTvr/kdr1xaeKO85Vq2vj1RPa36+uD53qLAPWuG1TErvvgeL69UrXtr3+Pr6icbuk/mBT/zXONje+rvJ6wt7cE3Sj//OZ2rX3vW1+2vX/t1Lyvt2Hrz3xGgF7dv69WGjd33pc7Vrv3XhX4/2nGMo7Z0WEXHb0Q/3Pb7S7cV5F0+iotl268P39+3/ae2nWNo/srT33bTqnbWaBj1nXb0r3V6cc1F+PTvBrM1AySzugzqLNY0yB8POgHcyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGlsYQJDqu7+clStPdMuYyjr331825+zWlsrrk+jpnHUbU1TVavbXMnsuOniy2Nhh8zAb114WWF1xC1KBuidKD9uuabZc/0FV/Y9vlatRsSj21rLLLjhost2TP/P4pYSs1hTSV29p/v/kW2tZVaYgfHMYk2D9Kt52BnwTiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQJqFaRcAO0XryhdHa37fluPV5784hWrK5p91bu1a74mV4rnV+vpIz9many+uzx06WLu2/t3HR3rOSWq94qX9j6+fiLj7T7e5mtlw80P3xsH21t9NXn/BlVOopuyff+3+2rV/95JXFs/tnTgx0nPO7dv69WGjf/alz9Wu/daFl430nJN029G7+x5f6fbivIu3uZgZcOvD9/ft/2sXr9j+Yga4vXNP7dq06p3Fmkrq6l3p9uKci7a3llmxW2YgYjo1z2JNg/SredgZ8E4mAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII0tTGBI89/6XszP7d1yvPeyS4rn9e57cCL1zLXbtWunLnte7drJc/dMoJrB9j6+Wru25wv1a71udxLlxNyA+zb3zf7bqlS9k5MoZ0d46+v+diz0mYGPHi1v6fKGC14+kXr+56/+VO3ae3/6b9SfeHH9Fj8RESuXHhqpnmc+8ERx/b0/Xf+4//NXL6xd+/Hf+cxI9Qzy0aNfKK6/8Sd/vu/xtd7JiPiPE6hott1w0WWx0Nr69XNa2xLMH6zfFmoWt0Io1VR6Lesr5W23RjXqfVurViPikfR6doJZm4FZe85BZrGmUe7dsDPgnUwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANPbJhCGtdb4V0Wd/qPmzn1E8b+F5z6l/zEcfG7meY6+/tHZt/6131a+N/IyTc+yGV9WulV7LIKVrX52o35szImLtaKf/8ap83m62dqTTdwbedNXPFc/7cGEfzTde8MqR63n2f/9O7Vr1rGfWrn3ztecUH/eCW74xUj1Hb3xucX3xE73atdJrWR+pmtM+fPRztWtvuqr/PphnrB090v94g2egn0F735X2oRtn37zS/pGthfof76q1tZGfcxylmqaxF+Ys7lm4Uw26lqU9eSe1jzJPKV3/axcnd/29kwkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkGerTZauqioiItViNqCZaDzvAWpz+ZMEzfbHbDer/av1k8fxWr/6TGMf5lMa11RMTedxpmNhr6dXfm2q9/pM+IyLWa563af0fMcT3gMJ1johY6dZf63Hub3H2Wq3apfWT9f0WEbE24PWM/Lilegv9VNeLwyhe+wGvs+7eNG0Gxv0ZaFL9X9Iq3JuqmtKny06hpklc+6b1f8TOnAGekn39h52BVjXElBw5ciSWl5efdhHsbocPH46lpaVplzFx+p9+mtL/EWaA/poyA/qffprS/xFmgP4GzcBQIbPX60Wn04l2ux2twm+HaYaqqqLb7cbi4mLMze3+v7jW/2zUtP6PMANs1rQZ0P9s1LT+jzADbDbsDAwVMgEAAGAYzfgVDAAAANtCyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIsDPOPer1edDqdaLfb0Wq1Jl0TM66qquh2u7G4uBhzc7v/9xT6n42a1v8RZoDNmjYD+p+Nmtb/EWaAzYadgaFCZqfTieXl5bTi2B0OHz4cS0tL0y5j4vQ//TSl/yPMAP01ZQb0P/00pf8jzAD9DZqBoUJmu92OiIhr4vpYiD05lbFjrcVq3BG3/bAvdrszr/PCX/n1mN+7b8v6Xb/8ByM/9g0XXTbyubc+fH/t2i3HDtauXbf/O8XH3dsa6svCFierteL6x46fV7t244GV2rVJXaNBXvW7b+97fP3kifjaf/jXjen/iKdm4LXP/IVYaJ21Zf3mu/5i5Mee1v3dSaZ1jW561av7Hl+rTsWnvv/HjZmBQT8Dnfv/PbN4/n96bv18jHNvf/WeL9euvW5//dfj471Txcf90qn5kep5yVnrxfX9c1u/dpzxieP133d+54oXj1RPRLn//+E3+vf3GY//b9/ve7xpPwNFDJ6Bt919uHj+TQe+X7s2zgyU/Mxdx4rrf/n4C2rXjv3bxZGf98C/6NSu/eS5jxTP/eSrDoz8vCWlObj52DOL5/7RlVt/uTDsDAz10+SZt8YXYk8stITMxqtO/6cpfzJx5nXO793XN2QebI/+5zLjzFPpefe36n9IOLi/XO/e1miv52RVPm//fKGmA4U/t5jQNRqk373eqCn9H7Hhe0DrrL4hcxZnYDeZ1jXqd683asoMDPoZaM/Z5etUugfj3Nuz26Wv87365+yVe+LsU6P1zMGzquL6/sKf1Z1d+P4wqf4fdN9qn7dhPwNFDJ6B/QfKv5iY1AyU7DtQjjh7Ttbf/4WF8vf/4uMW+mpQTZO6FsWfF6N87/rWNOQMNOM7NAAAANtCyAQAACCNkAkAAEAaIRMAAIA0o32MJDTQXb/8ByN9iMa1i1fkFzPAWw48UVgtf9jBqPYP+JCQck2TUbr2t3fuKZ77xX/y/r7HV7q9OOe9YxS1g91811/smBngtHFm4LYv/Xnf4yvdXpxz0RhF7VAXf3ohzurzwR3//tn9r9MZk+r/v7V/tXbtl45cXbv2tvPuLD7uBfPlT+Ws8/lT5U/G/KPv1Nf0e0v1Nb1npGpOK/d/+b790y+8ou/xU8eq+ORrxihqBzvv44f6frDNW9v3FM+b1AwcveUltWvH1/+qeO4PXvvXtWtn7Rv955UfvPZEfU337i2e27n10tq1xRseGLmmcb4PfPQzl285tvrkqYjXD35e72QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApLFPJoxpWnsAjrPv0ayZxjUc9Jw77RpO026bgU/8YH6k8173jPWRn9MMzL5/e/5f9d0ndjb7v37fyT87vq/4uO98+c+OVM+vfK68/2ZpL8zZ7P/P9z2+0u3F702gnp3g/3rOHTM1Axfc+KXatd/olPeVvDauqF17w91HRy0pPnzps2rXfuPHyjV95vLJ7GNeMngOPrnl2Eq3FzcP8djeyQQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkaVVVVQ36RysrK3Ho0KH4mfj5WGjt2Y66mGFr1Wp8Mv40nnjiiTh48OC0y5k4/c9GTev/CDPAZk2bAf3PRk3r/wgzwGbDzoB3MgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0ixMuwDYKW59+P442N76e5lrF6/Y/mIGuL1zz7RLeFp20jVc6fbinIu2t5ZZ0ZQZ+MQP5kc673XPWB/5OXfSNWzqDOyW/v+z4/uK5/6HV1490nP+yufuLK7/nf0natd20jVsav9H7J4ZiCjX/MYHvjvy83740mfVro1T07T0q3nYGfBOJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNLUxgTNP6SOqdtk1JSem1uH6zb7fNwDhbkYzKDMy+f/Gtn4izju3Zcvz2zueL503j/v3SkfptSN52Xnmrkffd85GR6jm6fqC4Xqrp9k59TdPq/3/6zVf0PX7q2GpEPJJf0A7wfzx2Tew5+6wtx2/vfLJ43qTu4dFbXlK79pvfPjXy4370ygtGPjeifque3/z2pcUzO7fWry/e8MDIFZUMmoO//+jPbDm2+uSpGGYGvJMJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkMY+mTCkV/3u22N+774tx7/4T95fPG8a+9/9ybFDtWtv2P/t4rl7W6N9WThZrRXXP3r8x2rX3nLgiZGec5Bx9gF86W+/o+/x9ZMnIuLXRn7cneymV706Flpb90i77Ut/XjxvGjPAaePMwPUv+dm+x9eq4fZI220ees1aLLRaW47/vf/R/zqdcXunfj7G6f//dnzrnp1n/N5S/b6Tx3vl/QPvO/WMkep5xVnlx31NoabSaxlHqf//3tfL9+27P/29vsfXBnyv282+8/onYqG19V594KFziudN6nvABTd+qXZt/xdPFs99xqeWa9e6v7k0ck3t3zhSX9P8V4vnTmMvzA90y/fu2z/1/S3H1qrVoZ7XO5kAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAmqE+RrKqqoiIWIvViGqi9bADrMXpT5U60xe73ZnXefpTRbda6fZGfuxhP6Hr6T7v8WPr9eetl+vd2xrt9ZysyucdP16oqXDupK7RIHX3+8zxpvR/xIbvAVX/T4+cxRnYTaZ1jeru95njTZmBQT8DrT5Z/lTV0j0Y594+2R3t6/zxXrknnjy19RN0h7FyVvlx1+bq158sfH+YVP8Pum91z9u0n4EiBs9A6WeOiIiVmMwMlJw4Vv4U4NL9X1vr//1/GKXHPbG3XNOkrsWoPy9G9K9p2BloVUNMyZEjR2J5uf6jfmmmw4cPx9LS6B/zvFPof/ppSv9HmAH6a8oM6H/6aUr/R5gB+hs0A0OFzF6vF51OJ9rtdrT67BFFs1RVFd1uNxYXF2Nubvf/xbX+Z6Om9X+EGWCzps2A/mejpvV/hBlgs2FnYKiQCQAAAMNoxq9gAAAA2BZCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAIM3CMP+o1+tFp9OJdrsdrVZr0jUx46qqim63G4uLizE3t/t/T6H/2ahp/R9hBtisaTOg/9moaf0fYQbYbNgZGCpkdjqdWF5eTiuO3eHw4cOxtLQ07TImTv/TT1P6P8IM0F9TZkD/009T+j/CDNDfoBkYKmS22+2IiLj7cz8WBw4047c21Dt2rBdXvvLbP+yL3e7M6/zG3c+Lg8n9/3+vnF+79mdv+8niuTd/5EOptcyqm37uTcX1v/NHf1m79r8f/FZyNRErx3rx3CsfbUz/R0x2Bt788qtr13rHniyee+tD96XWMqtuuPhlxfW5A2fXrn3wC3dml9O4GTjzOl/0S78e82ft27J+5zv/YOTHvvHvvqV2rbrnweK5+v+01hWX1K7d8v/+ycjPe/X73t73+PqpE/GV3/vXjen/iKdm4OXX/los7Nk6Ax977x+O/Ng/+6/+YXH9mX/8udq1Dzz4hdq1va09I9c0LSer1dq1t17y8tq17//CK4uP++f/6j+NXNN17/rFLcfWVk/EF27/PwfOwFAh88xb4wcOzEW7LWRyWlP+ZOLM6zx4YC4OJvf/M3r1I7gwv7d4bnYts2rQdXjGgfprOMlr1JT+j5jsDCy0zqpd67VOFc9tzAwM+GFprnANzcD4zrzO+bP2xfzerT9gj3ONS1/fqgH3Xf+f1ipcw3GuUb97vel5G9L/EU+91oU9+/qGzLGuc59f3GxUuv+l593b2nnzcbIq/Olp4ToMuoZjfY3qc7/PGDQDO+8OAAAAMLOETAAAANIImQAAAKQRMgEAAEgz1Af/AKP72uqx2rUPXvo3Cmd+Nb+YHWj9wfJ1KF3D1z36cO3ahXsOjFwTT891z72qdm3uRc+uP/HLX5lANbtPa7n+Gpau/ce+8dlJlLNr3fnOPxjpAzSuvvfNtWvnfmeldm3taT9TM80XrmHp2t95+QeLj3vfu9/f9/hKtxfnvG+42nabj733D0eagZu+9vratWfd8sXiub3C2jifILtardeu7WnNT+VxR309g67hTW+vv/43X/jx4rmffv/vbzm20u3FOR8dXJd3MgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSTH2fzH/wnGtGPvc/P3ZHYiWzyzWabSer1eL6O5736ok87+Wf/YXatXuv+uOJPOeklF7L+fHgyI9buvYfPlLeI3Cc/bea5toLfqK4Pn9u/Z6k62PshVl63tuP/tXIjzsNg65hSekazp97zsjPudOu4bQMuo7XfOGR2rX7Hhv9ea+/5DW1a7c9+OnRH3gKSq8lols8d+2xI7Vr1xS2otb/eQZ//fruRJ63tP/moP0f56KVXc7Yj1t6PaVr2DtWvx97RET3NfXr18bk5sA7mQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA027KFyThbcDCe0rW3vcnTc6x3IuZ6W38v8+blq6dQTcT5N9Rv7XHhe3+5dm354+vFx93/9SdGquf48w8V1w+/fr527YXvumuk5xzHG5euKq5/8PCdfY8f6/UmUc6OcMPFL4uFEbZ2WX/8exOopmycLUF2k3Gufd01XKtWI6J+W47datT+v+/lEygmIta79Vt77Lz+L29TMqpxrr3+32rUGZiU7mvqt/UYtDXHbJrMVi/j6DcHw86AdzIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAabZlC5NJ2U3bc9jmZfb94lV/MxZaZ/VZmcxHr49jnC1Byhuc1Nv75fL6C28b8YGn5C2X/q2+x9eqUxHxX7a3mBnRWtgTrT4fX1+trU6hmrL5Qwdr19afWNnGSp4yizWVtBb6b1XQqiJi9m75xM1f9IKYn9+75fj6g1+dQjVlr733eO3apy7fv42VPGUWayqZv+SFfY9X6ycjHtrmYmbEN995Vczv3bfl+OJ7+m/5NU11X7/OmMb3rVmsaZDOu7du07d+8kTEb39o4LneyQQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABIk7JP5k7b43Fa9c7a3p2DrsOs1Ttt691jffcIZHda7/bf/3S9mr19rLZLtbYaVWvaVQxnFvednMWaSur2bKsaOgPrDz+yY74HzOK+k7NYU0nd/qdN/h7w7Pd9NhZ2yAzM4p6Ts1jTIP32QF2rVofaKtY7mQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0T2sLk3906dU75qOLZ3FblVmsqaSu3rVqNSI+tK21AAAAO4N3MgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAIM3C0/nHv//AndFub82l/+A516QVlOU/P3ZH7dq06p3Fmkrq6u12e3HRi7e5GAAAYEfwTiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgzdPawqROaWuOiNnbnmNQvU3hOjw98+0DMd86a8vx9W53CtUwafPtdt/jVXUqYmWbi5kRrYU90Wrt2XK8WludQjVl84cO1q6tPzGdGziLNZW0Frbe64iIVhURs3fLJ27+ohfE/PzeLcfXH/zqFKope+29x2vXPnX5/m2s5CmzWFPJ/CUv7Hu8Wj8Z8dA2FzMjvvnOq2J+774txxffc+cUqimr+/p1xjS+b81iTYN03n31lmPrJ09E/PaHBp7rnUwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANCn7ZE7LbtrnsfRaZm2f0ab6w8/+9zjY3vp7mTcvb91DaNq++t5X1a4tf3y9eO7+rz8x0nMef/6h4vrh18/Xrr3wXXeN9JyT9CcP/Le+x1e6vVi+ZJuLmRHV2mpUrWlXMZxZ3HdyFmsqqduzrapmby+37bD+8CN994mdRbO47+Qs1lRSt//pekP7PyLi2e/7bCzskBmYxT0nZ7GmQfrtgbpWrQ61Vax3MgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABptmULE9tzTM9u2uZl2g7M7YsDc1t/L/PhI58tnvfGpasmUs+3bq3fR+NrV/1u/Yl/dwLFjOny5/9C7dr5Nzw4keccdN/2tvb1Pd6b602inB3h1ofu67uNz7UX/ETxvPlzz6ldW3/8e2PX1c/tR/9qIo87KYOu4ajGufZ113Cl24tzLhqrrB1p1P5/2Rfq1+57+ej1zLfbtWu3Pfjp0R94Cq6/5DW1a+vd7siPO8611/9bjToDk9L+9LNq126+8OPFc9er+u/l863R34Mb53Fv+trra9e6r/nuyDWNo98cDDsD3skEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApNmWLUxKbLExmGs02/a29hTX3//oX9SuveN5rx75ee+96o9HPnfWlF7LtTH6R6OXrv3e1oGRH5fNBm0Xct1z67fxmX/xi2rX1r/8lbGedycpvZaBW8QUrmHvK4+O9JwMb9B1vPreN9eunfucU7Vra48dKT7uTtumpKT0Wgb1/8JzlmrX7vjrs2rX7jz6wcGFMZRBM1DamuPJ608Wz+0dO1a7NmibkuLjRlW7Nj/yo473uKXXU/pZaO5A+eeZs2/bO9Jzjss7mQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABppr5PJux2F+6p37/ozQ/8de3ah2786QGP3Iw97uYveWFx/U23/I/atdK1Z/t87BufrV372xfX79/FcKrD36xdK117np6r3/f2mN+7b8vx+979/uJ5d15evx/jdee9rf7Ex4YurdHWzztYu3bn5X808uO+7D3v6P98J09ExK+N/Lg72XXv+sVY2LN1Bj79/t8vnlfai/GqG/9x8dxz/p+/rF07Wa3Wrg3aw3xPa5zdMCfzuKXXU/LdG19aXP+vF/7HkR43IuI17/hHW46trZ6IiF8feK53MgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0Q326bFVVERFx7FhvosWwM5zpgzN9sdudeZ0rE+j/Hxxbq11bWz9ZPHel24x5HHQdStdwZS7/Gq00rP8jJjsDa9Wp2rXegE/aa8wMDLgOc4VrOIlr1LQZOPM610+d6Ls+zjUufX2r9H9EDO7/VuEajnONTn+KbJ/j/6sPmtL/EU+91tOfKrrVWNe5Zq7OKN3/0vPube28+ThZ1ddcug6DruFYX6P63PMzxwbNQKsaYkqOHDkSy8vLI5bHbnX48OFYWlqadhkTp//ppyn9H2EG6K8pM6D/6acp/R9hBuhv0AwMFTJ7vV50Op1ot9vRarVSC2Tnqaoqut1uLC4uxtzc7v+La/3PRk3r/wgzwGZNmwH9z0ZN6/8IM8Bmw87AUCETAAAAhtGMX8EAAACwLYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgzcIw/6jX60Wn04l2ux2tVmvSNTHjqqqKbrcbi4uLMTe3+39Pof/ZqGn9H2EG2KxpM6D/2ahp/R9hBths2BkYKmR2Op1YXl5OK47d4fDhw7G0tDTtMiZO/9NPU/o/wgzQX1NmQP/TT1P6P8IM0N+gGRgqZLbb7YiI+Mbdz4uDB5rxWxvqrRzrxXOvfPSHfbHb6X82alr/R5gBNmvaDOh/Nmpa/0eYATYbdgaGCpln3ho/eGAuDrY1F6c15U8m9D/9NKX/I8wA/TVlBvQ//TSl/yPMAP0NmgGdAgAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACCNkAkAAEAaIRMAAIA0QiYAAABphEwAAADSCJkAAACkETIBAABII2QCAACQRsgEAAAgjZAJAABAGiETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkEbIBAAAII2QCQAAQBohEwAAgDRCJgAAAGmETAAAANIImQAAAKQRMgEAAEgjZAIAAJBGyAQAACDNwjD/qKqqiIhYOdabaDHsDGf64Exf7Hb6n42a1v8RZoDNmjYD+p+Nmtb/EWaAzYadgaFCZrfbjYiI51756HhVsat0u904dOjQtMuYOP1PP03p/wgzQH9NmQH9Tz9N6f8IM0B/g2agVQ3xq5herxedTifa7Xa0Wq3UAtl5qqqKbrcbi4uLMTe3+//iWv+zUdP6P8IMsFnTZkD/s1HT+j/CDLDZsDMwVMgEAACAYTTjVzAAAABsCyETAACANEImAAAAaYRMAAAA0giZAAAApBEyAQAASCNkAgAAkOb/B17Eso7mgFVsAAAAAElFTkSuQmCC"",
+      ""text/plain"": [
+       ""<Figure size 1200x1200 with 30 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""solver_metasurface_pt.display_multiple_metasurfaces(h_list[1::len(h_list)//5],params)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""id"": ""be6d3ccd"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": []
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","nearfield_optimization_tensorflow.ipynb",".ipynb","156853","492","{
+ ""cells"": [
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""db98c09c"",
+   ""metadata"": {},
+   ""source"": [
+    ""# Metalens Optimization for the Near Field Design Case, using TensorFlow\n"",
+    ""\n"",
+    ""This notebook demonstrates the use of the metalens optmization library for the COPILOT near field design case.\n"",
+    ""TensorFlow is used as the backend framework for automatic differentiation.""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""464d06b1"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Compute Devices""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""id"": ""4f5bf556"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 18:54:11.658364: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"",
+      ""2023-03-17 18:54:11.781936: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:54:11.781956: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"",
+      ""2023-03-17 18:54:12.428074: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:54:12.428125: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:54:12.428131: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Choose which device to use.\n"",
+    ""use_GPU = False\n"",
+    ""tfDevice = '/job:localhost/replica:0/task:0/device:GPU:1' if use_GPU else '/CPU:0'\n"",
+    ""\n"",
+    ""# Import device utils.\n"",
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('./src/')\n"",
+    ""sys.path.append('./rcwa_tf/src/')\n"",
+    ""import utils\n"",
+    ""\n"",
+    ""if use_GPU: \n"",
+    ""    # Configure GPUs.\n"",
+    ""    utils.config_gpu_memory_usage()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""3fbbb1a3"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Dependencies""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""c42677c1"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 18:54:14.232430: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 18:54:14.232450: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n"",
+      ""2023-03-17 18:54:14.232466: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dylan-XPS-13-7390): /proc/driver/nvidia/version does not exist\n"",
+      ""2023-03-17 18:54:14.232678: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""import tensorflow as tf\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import time\n"",
+    ""import solver\n"",
+    ""import solver_metasurface""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""bc053a93"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Configure Optimization Parameters\n"",
+    ""\n"",
+    ""Change the parameters here in order to configure the design case being optimized for, choose algorithm hyperparameters, or set logging behavior.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""0d501ddf"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""# Initialize parameters dictionary.\n"",
+    ""user_params = {}\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Algorithm hyperparameters.\n"",
+    ""# These values subject to a hyperparameter grid search. These default values are used if the grid search is disabled.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Maximum number of optimization iterations.\n"",
+    ""user_params['N'] = 10\n"",
+    ""\n"",
+    ""# Coefficient used for differentiable thresholding annealing.\n"",
+    ""# At each optimization step, the coefficient of the sigmoid function used to force admissable solutions\n"",
+    ""# is increased by the increment N / sigmoid_update.\n"",
+    ""user_params['sigmoid_update'] = 10.0\n"",
+    ""\n"",
+    ""# Learning rate provided to Keras optimizer.\n"",
+    ""user_params['learning_rate'] = 8E-1\n"",
+    ""\n"",
+    ""# Initial height of each of the device pixels.\n"",
+    ""# Should be in the range [0, Nlay-1], where Nlay is the number of device layers\n"",
+    ""# (specified as the length of the parameter L below).\n"",
+    ""user_params['initial_height'] = 0\n"",
+    ""\n"",
+    ""# Flag to enable hyperparameter grid search.\n"",
+    ""user_params['enable_hyperparameter_gridsearch'] = False\n"",
+    ""\n"",
+    ""# Values to use in hyperparameter grid search.\n"",
+    ""# Stored as a dict. Each dict key is the key in user_params corresponding to a tunable hyperparameter, i.e. 'N',\n"",
+    ""# and its value is a list of values to try for that hyperparameter.\n"",
+    ""param_grid = {'N': [10],\n"",
+    ""              'sigmoid_update': [10.0, 20.0],\n"",
+    ""              'learning_rate': [8E-1],\n"",
+    ""              'initial_height': [0]}\n"",
+    ""user_params['param_grid'] = param_grid\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Source parameters.\n"",
+    ""# These values specify input light sources. Each is a list, and all should be the same length.\n"",
+    ""# The length of these lists is the number of souces over which the device should be optimized.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Wavelength (um).\n"",
+    ""user_params['wavelengths'] = [120.0]\n"",
+    ""\n"",
+    ""# Orientation (radians).\n"",
+    ""user_params['thetas'] = [0.0]\n"",
+    ""user_params['phis'] = [0.0]\n"",
+    ""\n"",
+    ""# Source polarization.\n"",
+    ""user_params['pte'] = [1.0]\n"",
+    ""user_params['ptm'] = [0.0]\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Device parmeters.\n"",
+    ""# These values specify device design parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of device 'pixels', i.e. square regions of constant height, in each direction.\n"",
+    ""user_params['pixelsX'] = 18\n"",
+    ""user_params['pixelsY'] = user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Relative permittivity of the non-vacuum, constituent material of the device layers.\n"",
+    ""user_params['erd'] = 11.9\n"",
+    ""\n"",
+    ""# Relative permittivity of the substrate layer.\n"",
+    ""user_params['ers'] = user_params['erd']\n"",
+    ""\n"",
+    ""# Thickness of each layer (um). L[0] corresponds to layer closest to the source, and L[-1] to the substrate layer.\n"",
+    ""# The length of this list is used to specify the number of device layers.\n"",
+    ""user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 950.0]\n"",
+    ""\n"",
+    ""# Length of each pixel in the x direction (um).\n"",
+    ""user_params['Lx'] = 5000.0 / user_params['pixelsX']\n"",
+    ""\n"",
+    ""# Length of each pixel in the y direction (um).\n"",
+    ""user_params['Ly'] = user_params['Lx']\n"",
+    ""\n"",
+    ""# Focal distance (nm).\n"",
+    ""user_params['f'] = 0.0\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Solver parameters.\n"",
+    ""# These values specify the behavior of RCWA.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Number of spatial harmonics used by RCWA in each transverse direction.\n"",
+    ""user_params['PQ'] = [3,3]\n"",
+    ""\n"",
+    ""# Upsampling rate (per pixel) used when simulating scattering from the device.\n"",
+    ""user_params['upsample'] = 11\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Logging parameters.\n"",
+    ""# These values are used to configure logging behavior.\n"",
+    ""##########\n"",
+    ""user_params['enable_logging'] = False\n"",
+    ""\n"",
+    ""# Logfile name.\n"",
+    ""user_params['parameter_string'] = 'N' + str(user_params['N']) \\\n"",
+    ""    + '-sigmoid_update' + str(user_params['sigmoid_update']) \\\n"",
+    ""    + '-learning_rate' + str(user_params['learning_rate']) \\\n"",
+    ""    + '-initial_height' + str(user_params['initial_height'])\n"",
+    ""user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'\n"",
+    ""user_params['log_filename_extension'] = '.txt'\n"",
+    ""\n"",
+    ""\n"",
+    ""##########\n"",
+    ""# Misc. parameters.\n"",
+    ""##########\n"",
+    ""\n"",
+    ""# Initial value of the sigmoid coefficient.\n"",
+    ""user_params['sigmoid_coeff'] = 1.0\n"",
+    ""\n"",
+    ""# Radius of the focal spot used in the loss function, where 16 = width of one pixel.\n"",
+    ""user_params['focal_spot_radius'] = 10\n"",
+    ""\n"",
+    ""# Flag to enable random initial guess for the metasurface shape.\n"",
+    ""user_params['enable_random_init'] = False\n"",
+    ""\n"",
+    ""# Flags to enable some debug behaviors.\n"",
+    ""user_params['enable_debug'] = False\n"",
+    ""user_params['enable_print'] = True""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""280c2f49"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Loss Function Definition\n"",
+    ""\n"",
+    ""This loss function incentivises intensity focused at the center of the focal plane. Change in order to optimize for a different objective.""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""id"": ""4e7180e0"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""def loss_function(h, params):\n"",
+    ""    \n"",
+    ""    # Generate permittivity and permeability distributions.\n"",
+    ""    ER_t, UR_t = solver_metasurface.generate_layered_metasurface(h, params)\n"",
+    ""\n"",
+    ""    # Simulate the system.\n"",
+    ""    outputs = solver.simulate(ER_t, UR_t, params)\n"",
+    ""    \n"",
+    ""    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.\n"",
+    ""    r = params['focal_spot_radius']\n"",
+    ""    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""    focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""    index = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""    l1 = tf.math.reduce_sum(tf.abs(focal_plane[0, index-r:index+r, index-r:index+r]))\n"",
+    ""\n"",
+    ""    # Final loss: (negative) field intensity at focal point.\n"",
+    ""    return -1.0*l1\n"",
+    ""\n"",
+    ""# Set loss function.\n"",
+    ""user_params['loss_function'] = loss_function""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""fc0a4418"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Perform Experiments\n"",
+    ""\n"",
+    ""### Single Optimization Run""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""b30660aa"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Optimizing... 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, Done.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# This context gurantees that Tensorflow executes all operations on the specified device.\n"",
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    if not user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""        # Optimize.\n"",
+    ""        h, loss, params, focal_plane = solver_metasurface.optimize_device(user_params)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""b896b9bd"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Hyperparameter Grid Search""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""2c823798"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    if user_params['enable_hyperparameter_gridsearch']:\n"",
+    ""\n"",
+    ""        # Perform the hyperparameter grid search.\n"",
+    ""        results = solver_metasurface.hyperparameter_gridsearch(user_params)\n"",
+    ""\n"",
+    ""        # Get list of evaluation scores.\n"",
+    ""        scores = [r['eval_score'] for r in results]\n"",
+    ""\n"",
+    ""        # Select hyperparameters and results corresponding to best evaluation score.\n"",
+    ""        result = results[np.argmax(scores)]\n"",
+    ""        h = result['h']\n"",
+    ""        loss = result['loss']\n"",
+    ""        focal_plane = result['focal_plane']\n"",
+    ""        eval_score = result['eval_score']\n"",
+    ""        params = result['params']\n"",
+    ""\n"",
+    ""        print('Best hyperparameters: ' + str(result['hyperparameters']))\n"",
+    ""        print('With evaluation score: ' + f'{eval_score:.2f}')""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""27c33419"",
+   ""metadata"": {},
+   ""source"": [
+    ""## Visualize Results""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""459ded9c"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Focal Plane""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 7,
+   ""id"": ""a582734e"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    plt.imshow(tf.abs(focal_plane[0, :, :]) ** 2)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""ed999792"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Learning Curve""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 8,
+   ""id"": ""83337ea7"",
+   ""metadata"": {
+    ""scrolled"": false
+   },
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    plt.plot(loss)\n"",
+    ""    plt.xlabel('Iterations')\n"",
+    ""    plt.ylabel('Loss')\n"",
+    ""    plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""id"": ""f70afeb4"",
+   ""metadata"": {},
+   ""source"": [
+    ""### Device Shape""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 9,
+   ""id"": ""a30387ed"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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2NXMSIixhxESMuYsRFjLiIWWsq4smDb5wVcUCcFcHOEhcx4iJGXMSIixhxESMuYtaa59oXXaeS3Nj0FKLU846VkYsYcREjLmLERYy4iBEXMXs7FdE3LZCaElj1vNvYp20ychEjLmLERYy4iBEXMeIiZtRTEWP7aN+3T2P7XYYwchEjLmLERYy4iBEXMeIiRlzEjHqeKzX/s2oVT5fb7M9Y57L6GLmIERcx4iJGXMSIixhxESMuYsRFjLiIERcx4iJGXMSIi5hRnxWRWjGzjTMUrP6BNYiLGHERIy5ixEWMuIgRFzGjnuca21VjdnGfkoxcxIiLGHERIy5ixEWMuIgZ9VREn02nKVZt28dtiP+ZkYsYcREjLmLERYy4iBEXMXs7FdFnW7chPjRGLmLERYy4iBEXMeIiRlzEiIuYtea5nr/6pU6mn/Zofmc/dZ1CdNks6ov7q7c3chEjLmLERYy4iBEXMeIiZq2piCcPvqnjyb3UvrBjuqaYrturqvp95fZGLmLERYy4iBEXMeIiRlzEOCuCTs6KYGeJixhxESMuYsRFjLiIERcxd3LKzW0uNHto98NJ2PQ13PR9c8oNWycuYsRFjLiIERcxgz4ttm1bVTefEj512Sx6t+/abtW2fdvxp01fw03ft5uv33TRZdKu+o6qevPmTZ2fn6/6Ng7MxcVFnZ2ddT4+KK7FYlHz+bym02lNJpM73UHGp23bapqmZrNZHR11H1kNigs24YCeGHERIy5ixEWMuIgRFzHiIkZcxIiLGHERIy5ixEWMuIgRFzHiIkZcxIiLGHERIy5ixEWMuIgRFzGDVlxbt8jHhq5bHBTXfD634ppPrFpxPSiu6XRaVVX/8+Pf6uQv/pIeust/LOqvj15/6KLLoLhu/hSe/OVo6e1ZOEyrDpGUQoy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmLERYy4iBEXMeIiRlzEiIsYcREjLmKOh3xT27ZVVXX5j0V0ZxiHmw5uuugyKK6maaqq6q+PXt9ur9grTdPU6elp5+OTdlV+VbVYLGo+n9d0Oq3JZHKnO8j4tG1bTdPUbDaro6PuI6tBccEmHNATIy5ixEWMuIgRFzHiIkZcxPwfvP+T0FW2sJgAAAAASUVORK5CYII="",
+      ""text/plain"": [
+       ""<Figure size 1200x1200 with 6 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""with tf.device(tfDevice):\n"",
+    ""    \n"",
+    ""    ER_t, UR_t = solver_metasurface.generate_layered_metasurface(h, params)\n"",
+    ""    solver_metasurface.display_layered_metasurface(ER_t, params)""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""id"": ""37c34603"",
+   ""metadata"": {},
+   ""outputs"": [],
+   ""source"": []
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_pt/src/rcwa_utils_pt.py",".py","10198","255","'''
+rcwa_utils_py.pt
+
+Contains some mathematical functions used in the PyTorch implementation
+of RCWA.
+
+This file is a modified version of the original by
+Copyright (c) 2020 Shane Colburn, University of Washington
+
+Modifications made by Dylan Everingham, 2022
+for performance optimization and conversion to use PyTorch.
+'''
+
+import torch
+import numpy as np
+
+    
+def convmat(A, P, Q):
+    '''
+    This function computes a convolution matrix for a real space matrix `A` that
+    represents either a relative permittivity or permeability distribution for a
+    set of pixels, layers, and batch.
+    Args:
+        A: A `torch.Tensor` of dtype `complex` and shape `(batchSize, pixelsX, 
+        pixelsY, Nlayers, Nx, Ny)` specifying real space values on a Cartesian
+        grid.
+
+        P: A positive and odd `int` specifying the number of spatial harmonics 
+        along `T1`.
+
+        Q: A positive and odd `int` specifying the number of spatial harmonics 
+        along `T2`.
+    Returns:
+        A `torch.Tensor` of dtype `complex` and shape `(batchSize, pixelsX, 
+        pixelsY, Nlayers, P * Q, P * Q)` representing a stack of convolution 
+        matrices based on `A`.   
+    '''
+    
+    # Determine the shape of A.
+    batchSize, pixelsX, pixelsY, Nlayers, Nx, Ny = A.shape
+
+    # Compute indices of spatial harmonics.
+    NH = P * Q # total number of harmonics.
+    p_max = int(np.floor(P / 2))
+    q_max = int(np.floor(P / 2))
+
+    # Indices along T1 and T2.
+    p = np.linspace(-p_max, p_max, P)
+    q = np.linspace(-q_max, q_max, Q)
+    
+    # Compute array indices of the center harmonic.
+    p0 = int(np.floor(Nx / 2))
+    q0 = int(np.floor(Ny / 2))
+    
+    # Fourier transform the real space distributions.
+    A = torch.fft.fftshift(torch.fft.fft2(A), dim = (4,5)) / (Nx * Ny)
+    
+    # Build the matrix.
+    firstCoeff = True
+    for qrow in range(Q):
+        for prow in range(P):
+            for qcol in range(Q):
+                for pcol in range(P):
+                    pfft = int(p[prow] - p[pcol])
+                    qfft = int(q[qrow] - q[qcol])
+
+                    # Sequentially concatenate Fourier coefficients.
+                    value = A[:, :, :, :, p0 + pfft, q0 + qfft]
+                    value = value[:, :, :, :, None, None]
+                    if firstCoeff:
+                        firstCoeff = False
+                        C = value
+                    else:
+                        C = torch.cat([C, value], dim = 5)
+    
+    # Reshape the coefficients tensor into a stack of convolution matrices.
+    convMatrixShape = (batchSize, pixelsX, pixelsY, Nlayers, P * Q, P * Q)      
+    matrixStack = torch.reshape(C, convMatrixShape)
+    
+    return matrixStack
+
+
+def redheffer_star_product(SA, SB):
+    '''
+    This function computes the redheffer star product of two block matrices, 
+    which is the result of combining the S-parameter of two systems.
+    Args:
+        SA: A `dict` of `torch.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of a system. `SA` needs to have the 
+        keys ('S11', 'S12', 'S21', 'S22'), where each key maps to a `torch.Tensor`
+        of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH)`, where NH is the 
+        total number of spatial harmonics.
+
+        SB: A `dict` of `torch.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of a second system. `SB` needs to have
+        the keys ('S11', 'S12', 'S21', 'S22'), where each key maps to a
+        `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH)`, where 
+        NH is the total number of spatial harmonics.
+    Returns:
+        A `dict` of `torch.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of the combined system. `SA` needs 
+        to have the keys ('S11', 'S12', 'S21', 'S22'), where each key maps to
+        a `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH),
+        where NH is the total number of spatial harmonics.  
+    '''
+    # Define the identity matrix.
+    batchSize, pixelsX, pixelsY, dim, _ = SA['S11'].shape
+    I = torch.eye(dim, dtype = torch.complex64)
+    I = I[None, None, None, :, :]
+    I = torch.tile(I, (batchSize, pixelsX, pixelsY, 1, 1))
+    
+    # Calculate S11.
+    S11 = torch.linalg.inv(I - torch.matmul(SB['S11'], SA['S22']))
+    S11 = torch.linalg.matmul(S11, SB['S11'])
+    S11 = torch.matmul(SA['S12'], S11)
+    S11 = SA['S11'] + torch.matmul(S11, SA['S21'])
+    
+    # Calculate S12.
+    S12 = torch.linalg.inv(I - torch.matmul(SB['S11'], SA['S22']))
+    S12 = torch.matmul(S12, SB['S12'])
+    S12 = torch.matmul(SA['S12'], S12)
+    
+    # Calculate S21.
+    S21 = torch.linalg.inv(I - torch.matmul(SA['S22'], SB['S11']))
+    S21 = torch.matmul(S21, SA['S21'])
+    S21 = torch.matmul(SB['S21'], S21)
+    
+    # Calculate S22.
+    S22 = torch.linalg.inv(I - torch.matmul(SA['S22'], SB['S11']))
+    S22 = torch.matmul(S22, SA['S22'])
+    S22 = torch.matmul(SB['S21'], S22)
+    S22 = SB['S22'] + torch.matmul(S22, SB['S12'])
+    
+    # Store S parameters in an output dictionary.
+    S = dict({})
+    S['S11'] = S11
+    S['S12'] = S12
+    S['S21'] = S21
+    S['S22'] = S22
+    
+    return S
+
+class EigCustomGrad(torch.autograd.Function):
+    '''
+    Computes the eigendecomposition of a batch of matrices, the same as 
+    `torch.linalg.eig()` but assumes the input shape also has extra dimensions for pixels
+    and layers. This function also provides the reverse mode gradient of the 
+    eigendecomposition as derived in 10.1109/ICASSP.2017.7952140. This applies 
+    for general, complex matrices that do not have to be self adjoint. This 
+    result gives the exact reverse mode gradient for nondegenerate eigenvalue 
+    problems. To extend to the case of degenerate eigenvalues common in RCWA, we
+    approximate the gradient by a Lorentzian broadening technique that 
+    introduces a small error but stabilizes the calculation. This is based on
+    10.1103/PhysRevX.9.031041.
+    Args:
+        A: A `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayers, Nx, 
+        Ny)` and dtype `torch.complex64` where the last two dimensions define 
+        matrices for which we will calculate the eigendecomposition of their 
+        reverse mode gradients.
+
+        eps: A `float` defining a regularization parameter used in the 
+        denominator of the Lorentzian broadening calculation to enable reverse
+        mode gradients for degenerate eigenvalues.
+
+    Returns:
+        A `Tuple(List[torch.Tensor, torch.Tensor], torch.Tensor)`, where the `List`
+        specifies the eigendecomposition as computed by `torch.linalg.eig()` and the 
+        second element of the `Tuple` gives the reverse mode gradient of the
+        eigendecompostion of the input argument `A`.
+  '''
+
+    @staticmethod
+    def forward(ctx, A, eps = 1E-6):
+        
+        # Perform the eigendecomposition.
+        eigenvalues, eigenvectors = torch.linalg.eig(A)
+        
+        # Sort the eigenvalues in non-descencing order, according to their real parts.
+        indices = torch.real(eigenvalues).argsort()
+        
+        # Apply the ordering to the (complex) eigenvalues and their eigenvectors.
+        eigenvalues = eigenvalues.gather(dim=-1, index=indices)
+        indices = indices[:,:,:,:,None,:]
+        indices = indices.repeat((1,1,1,1,indices.shape[-1],1))
+        eigenvectors = eigenvectors.gather(dim=-1, index=indices)
+        
+        # Save the decomposition for the backwards pass.
+        ctx.save_for_backward(A, eigenvalues, eigenvectors)
+        ctx.eps = eps
+        
+        return eigenvalues, eigenvectors
+
+    @staticmethod
+    def backward(ctx, grad_D, grad_U):
+        
+        # Get the pre-computed eigendecomposition.
+        A, D, U = ctx.saved_tensors
+        
+        # Referse mode gradient calculation.
+        # Convert eigenvalues gradient to a diagonal matrix.
+        grad_D = torch.diag_embed(grad_D, dim1 = -2, dim2 = -1)
+
+        # Extract the tensor dimensions for later use.
+        batchSize, pixelsX, pixelsY, Nlay, dim, _ = A.shape
+
+        # Calculate intermediate matrices.
+        I = torch.eye(dim, dtype = torch.complex64)
+        D = torch.reshape(D, (batchSize, pixelsX, pixelsY, Nlay, dim, 1))
+        shape_di = (batchSize, pixelsX, pixelsY, Nlay, dim, 1)
+        shape_dj = (batchSize, pixelsX, pixelsY, Nlay, 1, dim)
+        E = torch.ones(shape_di, dtype = torch.complex64) * torch.conj(torch.transpose(D, -2, -1))
+        E = E - D * torch.ones(shape_dj, dtype = torch.complex64)
+        E = torch.conj(torch.transpose(D, -2, -1)) - D
+
+        # Lorentzian broadening.
+        F = E / (E ** 2 + ctx.eps)
+        F = F - I * F
+
+        # Compute the reverse mode gradient of the eigendecomposition of A.
+        grad_A = torch.conj(F) *  torch.matmul(torch.conj(torch.transpose(U, -2, -1)), grad_U) ### Error here
+        grad_A = grad_D + grad_A
+        grad_A = torch.matmul(grad_A, torch.conj(torch.transpose(U, -2, -1)))
+        grad_A = torch.matmul(torch.linalg.inv(torch.conj(torch.transpose(U, -2, -1))), grad_A)
+        
+        return grad_A
+
+
+# Produces a differentiable function usable by PyTorch.
+eig_general = EigCustomGrad.apply
+
+
+def expand_and_tile_np(array, batchSize, pixelsX, pixelsY):
+  '''
+    Expands and tiles a numpy array for a given batchSize and number of pixels.
+    Args:
+        array: A `np.ndarray` of shape `(Nx, Ny)`.
+    Returns:
+        A `np.ndarray` of shape `(batchSize, pixelsX, pixelsY, Nx, Ny)` with
+        the values from `array` tiled over the new dimensions.
+  '''
+  array = array[np.newaxis, np.newaxis, np.newaxis, :, :]
+  return np.tile(array, reps = (batchSize, pixelsX, pixelsY, 1, 1))
+
+
+def expand_and_tile_pt(tensor, batchSize, pixelsX, pixelsY):
+  '''
+    Expands and tiles a `torch.Tensor` for a given batchSize and number of pixels.
+    Args:
+        tensor: A `torch.Tensor` of shape `(Nx, Ny)`.
+    Returns:
+        A `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nx, Ny)` with
+        the values from `tensor` tiled over the new dimensions.
+  '''
+  tensor = tensor[None, None, None, :, :]
+  return torch.tile(tensor, (batchSize, pixelsX, pixelsY, 1, 1))","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_pt/src/solver_pt.py",".py","28155","768","'''
+solver_pt.py
+
+Core implementation of differentiable RCWA, using PyTorch to take
+advantage of automatic differentiation.
+
+Contains all functions for configuration of input parameters,
+simulation of fields within device, and propagation of fields
+away from its transmission surface.
+
+This file is a modified version of the original by
+Copyright (c) 2020 Shane Colburn, University of Washington
+
+Modifications made by Dylan Everingham, 2022
+for performance optimization and conversion to use PyTorch.
+'''
+
+import torch
+import numpy as np
+import rcwa_utils_pt
+import json
+from torch.utils.checkpoint import checkpoint
+
+def initialize_params(wavelengths = [632.0],
+                      thetas = [0.0],
+                      phis = [0.0],
+                      pte = [1.0],
+                      ptm = [0.0],
+                      pixelsX = 1,
+                      pixelsY = 1,
+                      erd = 6.76,
+                      ers = 2.25,
+                      PQ = [11, 11],
+                      Lx = 0.7 * 632.0,
+                      Ly = 0.7 * 632.0,
+                      L = [632.0, 632.0],
+                      Nx = 512,
+                      eps_min = 1.0,
+                      eps_max = 12.11,
+                      blur_radius = 100.0):
+  '''
+    Initializes simulation parameters and hyperparameters.
+    Args:
+        wavelengths: A `list` of dtype `float` and length `batchSize` specifying
+        the set of wavelengths over which to optimize.
+
+        thetas: A `list` of dtype `float` and length `batchSize` specifying
+        the set of polar angles over which to optimize.
+
+        phis: A `list` of dtype `float` and length `batchSize` specifying the 
+        set of azimuthal angles over which to optimize.
+
+        pte: A `list` of dtype `float` and length `batchSize` specifying the set
+        of TE polarization component magnitudes over which to optimize. A 
+        magnitude of 0.0 means no TE component. Under normal incidence, the TE 
+        polarization is parallel to the y-axis.
+
+        ptm: A `list` of dtype `float` and length `batchSize` specifying the set
+        of TM polarization component magnitudes over which to optimize. A 
+        magnitude of 0.0 means no TM component. Under normal incidence, the TM 
+        polarization is parallel to the x-axis.
+
+        pixelsX: An `int` specifying the x dimension of the metasurface in
+        pixels that are of width `params['Lx']`.
+
+        pixelsY: An `int` specifying the y dimension of the metasurface in
+        pixels that are of width `params['Ly']`.
+
+        erd: A `float` specifying the relative permittivity of the non-vacuum,
+        constituent material of the device layer for shape optimizations.
+
+        ers: A `float` specifying the relative permittivity of the substrate
+        layer.
+
+        PQ: A `list` of dtype `int` and length 2 specifying the number of 
+        Fourier harmonics in the x and y directions. The numbers should be odd
+        values.
+
+        Lx: A `float` specifying the unit cell pitch in the x direction in
+        micrometers.
+
+        Ly: A `float` specifying the unit cell pitch in the y direction in
+        micrometers.
+
+        L: A `list` of dtype `float` specifying the layer thicknesses in 
+        micrometers.
+
+        Nx: An `int` specifying the number of sample points along the x 
+        direction in the unit cell.
+
+        eps_min: A `float` specifying the minimum allowed permittivity for 
+        topology optimizations.
+
+        eps_max: A `float` specifying the maximum allowed permittivity for 
+        topology optimizations.
+
+        blur_radius: A `float` specifying the radius of the blur function with 
+        which a topology optimized permittivity density should be convolved.
+        
+    Returns:
+        params: A `dict` containing simulation and optimization settings.
+  '''
+
+  # Define the `params` dictionary.
+  params = dict({})
+
+  # Units and tensor dimensions.
+  params['micrometers'] = 1E-6
+  params['degrees'] = np.pi / 180
+  params['batchSize'] = len(wavelengths)
+  params['pixelsX'] = pixelsX
+  params['pixelsY'] = pixelsY
+  params['Nlay'] = len(L)
+
+  # Simulation tensor shapes.
+  batchSize = params['batchSize']
+
+  # Batch parameters (wavelength, incidence angle, and polarization).
+  lam0 = params['micrometers'] * torch.tensor(wavelengths, dtype=torch.float32)
+  lam0 = lam0[:, None, None, None, None, None]
+  lam0 = torch.tile(lam0, (1, pixelsX, pixelsY, 1, 1, 1))
+  params['lam0'] = lam0
+
+  theta = params['degrees'] * torch.tensor(thetas, dtype=torch.float32)
+  theta = theta[:, None, None, None, None, None]
+  theta = torch.tile(theta, (1, pixelsX, pixelsY, 1, 1, 1))
+  params['theta'] = theta
+
+  phi = params['degrees'] * torch.tensor(phis, dtype=torch.float32)
+  phi = phi[:, None, None, None, None, None]
+  phi = torch.tile(phi, (1, pixelsX, pixelsY, 1, 1, 1))
+  params['phi'] = phi
+
+  pte = torch.tensor(pte, dtype=torch.complex64)
+  pte = pte[:, None, None, None]
+  pte = torch.tile(pte, (1, pixelsX, pixelsY, 1))
+  params['pte'] = pte
+
+  ptm = torch.tensor(ptm, dtype=torch.complex64)
+  ptm = ptm[:, None, None, None]
+  ptm = torch.tile(ptm, (1, pixelsX, pixelsY, 1))
+  params['ptm'] = ptm
+
+  # Device parameters.
+  params['ur1'] = 1.0 # permeability in reflection region
+  params['er1'] = 1.0 # permittivity in reflection region
+  params['ur2'] = 1.0 # permeability in transmission region
+  params['er2'] = 1.0 # permittivity in transmission region
+  params['urd'] = 1.0 # permeability of device
+  params['erd'] = erd # permittivity of device
+  params['urs'] = 1.0 # permeability of substrate
+  params['ers'] = ers # permittivity of substrate
+  params['Lx'] = Lx * params['micrometers'] # period along x
+  params['Ly'] = Ly * params['micrometers'] # period along y
+  L = torch.tensor(L, dtype=torch.complex64)
+  L = L[None, None, None, :, None, None]
+  params['L'] = L * params['micrometers']
+  params['length_min'] = 0.1
+  params['length_max'] = 2.0
+
+  # RCWA parameters.
+  params['PQ'] = PQ # number of spatial harmonics along x and y
+  params['Nx'] = Nx # number of point along x in real-space grid
+  if params['PQ'][1] == 1:
+    params['Ny'] = 1
+  else:
+    params['Ny'] = int(np.round(params['Nx'] * params['Ly'] / params['Lx'])) # number of point along y in real-space grid
+
+  # Coefficient for the argument of torch.sigmoid() when generating
+  # permittivity distributions with geometric parameters.
+  params['sigmoid_coeff'] = 1000.0
+
+  # Polynomial order for rectangular resonators definition.
+  params['rectangle_power'] = 200
+
+  # Allowed permittivity range.
+  params['eps_min'] = eps_min
+  params['eps_max'] = eps_max
+
+  # Upsampling for Fourier optics propagation.
+  params['upsample'] = 1
+
+  # Duty Cycle limits for gratings.
+  params['duty_min'] = 0.1
+  params['duty_max'] = 0.9
+
+  # Permittivity density blur radius.
+  params['blur_radius'] = blur_radius * params['micrometers']
+
+  return params
+
+
+def make_propagator(params, f):
+  '''
+    Pre-computes the band-limited angular spectrum propagator for modelling
+    free-space propagation for the distance and sampling as specified in `params`.
+
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+
+        f: A `float` specifying the focal length, or distance to propagate, in
+        meters.
+    Returns:
+        propagator: a `torch.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `torch.complex64` defining the 
+        reciprocal space, band-limited angular spectrum propagator.
+  '''
+
+  # Simulation tensor shape.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  upsample = params['upsample']
+
+  # Propagator definition.
+  k = 2 * np.pi / params['lam0'][:, 0, 0, 0, 0, 0]
+  k = k[:, None, None]
+  samp = params['upsample'] * pixelsX
+  k = torch.tile(k, (1, 2 * samp - 1, 2 * samp - 1))
+  k = k.type(torch.complex64)  
+  k_xlist_pos = 2 * np.pi * np.linspace(0, 1 / (2 *  params['Lx'] / params['upsample']), samp)  
+  front = k_xlist_pos[-(samp - 1):]
+  front = -front[::-1]
+  k_xlist = torch.tensor(np.hstack((front, k_xlist_pos)), dtype = torch.float32)
+  k_x = torch.kron(k_xlist, torch.ones((2 * samp - 1, 1)))
+  k_x = k_x[None, :, :]
+  k_y = torch.permute(k_x, (0, 2, 1))
+  k_x = k_x.type(torch.complex64)
+  k_x = torch.tile(k_x, (batchSize, 1, 1))
+  k_y = k_y.type(torch.complex64)
+  k_y = torch.tile(k_y, (batchSize, 1, 1))
+  k_z_arg = torch.square(k) - (torch.square(k_x) + torch.square(k_y))
+  k_z = torch.sqrt(k_z_arg)
+  
+  # Cast to double precision to accommodate long focal lengths.
+  propagator_arg = 1j * k_z * f
+  propagator = torch.exp(propagator_arg)
+
+  # Limit transfer function bandwidth to prevent aliasing.
+  kx_limit = 2 * np.pi * (((1 / (pixelsX * params['Lx'])) * f) ** 2 + 1) ** (-0.5) / params['lam0'][:, 0, 0, 0, 0, 0]
+  kx_limit = kx_limit.type(torch.complex64)
+  ky_limit = kx_limit
+  kx_limit = kx_limit[:, None, None]
+  ky_limit = ky_limit[:, None, None]
+
+  # Apply the antialiasing filter.
+  ellipse_kx = torch.real(torch.square(k_x / kx_limit) + torch.square(k_y / k)) <= 1
+  ellipse_ky = torch.real(torch.square(k_x / k) + torch.square(k_y / ky_limit)) <= 1   
+  propagator = propagator * ellipse_kx * ellipse_ky
+  
+  return propagator
+
+
+def propagate(field, propagator, upsample):
+  '''
+    Propagates a batch of input fields to a parallel output plane using the 
+    band-limited angular spectrum method.
+
+    Args:
+        field: A `torch.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `torch.complex64` specifying the 
+        input electric fields to be diffracted to the output plane.
+
+        propagator: a `torch.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `torch.complex64` defining the 
+        reciprocal space, band-limited angular spectrum propagator.
+
+        upsample: An odd-valued `int` specifying the factor by which the
+        transverse field data stored in `field` should be upsampled.
+        
+    Returns:
+        out: A `torch.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `torch.complex64` specifying the 
+        the electric fields at the output plane.
+  '''
+
+  batchSize, _, m = field.shape
+  n = upsample * m
+
+  field_real = torch.real(field)
+  field_imag = torch.imag(field)
+
+  # Add an extra channels dimension for torch.nn.interpolate, and remove afterwards.
+  field_real = field_real[None, :, :, :]
+  field_imag = field_imag[None, :, :, :]
+  field_real = torch.nn.functional.interpolate(field_real, size=[n,n], mode='nearest')
+  field_imag = torch.nn.functional.interpolate(field_imag, size=[n,n], mode='nearest')
+  field_real = field_real[0, :, :, :]
+  field_imag = field_imag[0, :, :, :]
+  field = field_real.type(torch.complex64) + 1j * field_imag.type(torch.complex64)
+
+  # To pad total image to have dimension 2n - 1, have to pad with (n-1)/2 on each side.
+  field = torch.nn.functional.pad(field, [(n-1) // 2, -((n-1) // -2), (n-1) // 2, -((n-1) // -2)])
+
+  # Apply the propagator in Fourier space.
+  field_freq = torch.fft.fftshift(torch.fft.fft2(field), dim = (1,2))
+  field_filtered = torch.fft.ifftshift(field_freq * propagator, dim = (1,2))
+  out = torch.fft.ifft2(field_filtered)
+    
+  # Crop back down to n x n matrices.
+  out = out[:, (n-1) // 2 : n-1-((n-1) // -2), (n-1) // 2 : n-1-((n-1) // -2)]
+
+  return out
+
+
+def define_input_fields(params):
+  '''
+    Given the batch of input conditions with different wavelengths and incidence
+    angles, this gives the input source fields incident on the metasurface.
+
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        A `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY)` and dtype 
+        `torch.complex64` specifying the source fields injected onto a metasurface
+        at the input.
+  '''
+
+  # Define the cartesian cross section.
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  dx = params['Lx'] # grid resolution along x
+  dy = params['Ly'] # grid resolution along y
+  xa = torch.linspace(0, pixelsX - 1, pixelsX) * dx # x axis array
+  xa = xa - torch.mean(xa) # center x axis at zero
+  ya = torch.linspace(0, pixelsY - 1, pixelsY) * dy # y axis vector
+  ya = ya - torch.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = torch.meshgrid(ya, xa, indexing='ij')
+  x_mesh = x_mesh[None, :, :]
+  y_mesh = y_mesh[None, :, :]
+
+  # Extract the batch of wavelengths and input thetas.
+  lam_phase_test = params['lam0'][:, 0, 0, 0, 0, 0]
+  lam_phase_test = lam_phase_test[:, None, None]
+  theta_phase_test = params['theta'][:, 0, 0, 0, 0, 0]
+  theta_phase_test = theta_phase_test[:, None, None]
+
+  # Apply a linear phase ramp based on the wavelength and thetas.
+  phase_def = 2 * np.pi * torch.sin(theta_phase_test) * x_mesh / lam_phase_test
+  phase_def = phase_def.type(torch.complex64)
+
+  return torch.exp(1j * phase_def)
+
+
+def simulate(ER_t, UR_t, params):
+  '''
+    Calculates the transmission/reflection coefficients for a unit cell with a
+    given permittivity/permeability distribution and the batch of input conditions 
+    (e.g., wavelengths, wavevectors, polarizations) for a fixed real space grid
+    and number of Fourier harmonics.
+
+    Args:
+        ER_t: A `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `torch.complex64` specifying the relative permittivity distribution
+        of the unit cell.
+
+        UR_t: A `torch.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `torch.complex64` specifying the relative permeability distribution
+        of the unit cell.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        outputs: A `dict` containing the keys {'rx', 'ry', 'rz', 'R', 'ref', 
+        'tx', 'ty', 'tz', 'T', 'TRN'} corresponding to the computed reflection/tranmission
+        coefficients and powers.
+  '''
+
+  # Extract parameters from the `params` dictionary.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  L = params['L']
+  Nlay = params['Nlay']
+  Lx = params['Lx']
+  Ly = params['Ly']
+  theta = params['theta']
+  phi = params['phi']
+  pte = params['pte']
+  ptm = params['ptm']
+  lam0 = params['lam0']
+  er1 = params['er1']
+  er2 = params['er2']
+  ur1 = params['ur1']
+  ur2 = params['ur2']
+  PQ = params['PQ']
+  
+  ### Step 1: Build convolution matrices for the permittivity and permeability ###
+  ERC = rcwa_utils_pt.convmat(ER_t, PQ[0], PQ[1])
+  URC = rcwa_utils_pt.convmat(UR_t, PQ[0], PQ[1])
+  
+  ### Step 2: Wave vector expansion ###
+  I = torch.eye(np.prod(PQ), dtype = torch.complex64)
+  I = I[None, None, None, None, :, :]
+  I = torch.tile(I, (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+  
+  Z = torch.zeros((np.prod(PQ), np.prod(PQ)), dtype = torch.complex64)
+  Z = Z[None, None, None, None, :, :]
+  Z = torch.tile(Z, (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+    
+  n1 = np.sqrt(er1)
+  n2 = np.sqrt(er2)
+
+  k0 = 2 * np.pi / lam0
+  k0 = k0.type(torch.complex64)
+  
+  kinc_x0 = n1 * torch.sin(theta) * torch.cos(phi)
+  kinc_x0 = kinc_x0.type(torch.complex64)
+  
+  kinc_y0 = n1 * torch.sin(theta) * torch.sin(phi)
+  kinc_y0 = kinc_y0.type(torch.complex64)
+  
+  kinc_z0 = n1 * torch.cos(theta)
+  kinc_z0 = kinc_z0.type(torch.complex64)
+  kinc_z0 = kinc_z0[:, :, :, 0, :, :]
+
+  # Unit vectors
+  T1 = np.transpose([2 * np.pi / Lx, 0])
+  T2 = np.transpose([0, 2 * np.pi / Ly])
+  p_max = np.floor(PQ[0] / 2.0)
+  q_max = np.floor(PQ[1] / 2.0)
+  p = torch.linspace(-p_max, p_max, PQ[0], dtype = torch.complex64) # indices along T1
+  p = p[None, None, None, None, :, None]
+  p = torch.tile(p, (1, pixelsX, pixelsY, Nlay, 1, 1))
+  q = torch.linspace(-q_max, q_max, PQ[1], dtype = torch.complex64) # indices along T2
+  q = q[None, None, None, None, None, :]
+  q = torch.tile(q, (1, pixelsX, pixelsY, Nlay, 1, 1))
+
+  # Build Kx and Ky matrices
+  kx_zeros = torch.zeros(PQ[1], dtype = torch.complex64)
+  kx_zeros = kx_zeros[None, None, None, None, None, :]
+  ky_zeros = torch.zeros(PQ[0], dtype = torch.complex64)
+  ky_zeros = ky_zeros[None, None, None, None, :, None]
+  kx = kinc_x0 - 2 * np.pi * p / (k0 * Lx) - kx_zeros
+  ky = kinc_y0 - 2 * np.pi * q / (k0 * Ly) - ky_zeros
+
+  kx_T = torch.transpose(kx, dim0 = 4, dim1 = 5)
+  KX = torch.reshape(kx_T, (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KX = torch.diag_embed(KX, dim1 = -2, dim2 = -1)
+
+  ky_T = torch.transpose(ky, dim0 = 4, dim1 = 5)
+  KY = torch.reshape(ky_T, (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KY = torch.diag_embed(KY, dim1 = -2, dim2 = -1)
+
+  KZref = torch.matmul(torch.conj(ur1 * I), torch.conj(er1 * I))
+  KZref = KZref - torch.matmul(KX, KX) - torch.matmul(KY, KY)
+  KZref = torch.sqrt(KZref)
+  KZref = -torch.conj(KZref)
+
+  KZtrn = torch.matmul(torch.conj(ur2 * I), torch.conj(er2 * I))
+  KZtrn = KZtrn - torch.matmul(KX, KX) - torch.matmul(KY, KY)
+  KZtrn = torch.sqrt(KZtrn)
+  KZtrn = torch.conj(KZtrn)
+    
+  ### Step 3: Free Space ###
+  KZ = I - torch.matmul(KX, KX) - torch.matmul(KY, KY)
+  KZ = torch.sqrt(KZ)
+  KZ = torch.conj(KZ)
+
+  Q_free_00 = torch.matmul(KX, KY)
+  Q_free_01 = I - torch.matmul(KX, KX)
+  Q_free_10 = torch.matmul(KY, KY) - I
+  Q_free_11 = -torch.matmul(KY, KX)
+  Q_free_row0 = torch.cat([Q_free_00, Q_free_01], dim = 5)
+  Q_free_row1 = torch.cat([Q_free_10, Q_free_11], dim = 5)
+  Q_free = torch.cat([Q_free_row0, Q_free_row1], dim = 4)
+
+  W0_row0 = torch.cat([I, Z], dim = 5)
+  W0_row1 = torch.cat([Z, I], dim = 5)
+  W0 = torch.cat([W0_row0, W0_row1], dim = 4)
+
+  LAM_free_row0 = torch.cat([1j * KZ, Z], dim = 5)
+  LAM_free_row1 = torch.cat([Z, 1j * KZ], dim = 5)
+  LAM_free = torch.cat([LAM_free_row0, LAM_free_row1], dim = 4)
+
+  V0 = torch.matmul(Q_free, torch.linalg.inv(LAM_free))
+  
+  ### Step 4: Initialize Global Scattering Matrix ###
+  SG = dict({})
+  SG_S11 = torch.zeros((2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = torch.complex64)
+  SG['S11'] = rcwa_utils_pt.expand_and_tile_pt(SG_S11, batchSize, pixelsX, pixelsY)
+
+  SG_S12 = torch.eye(2 * np.prod(PQ), dtype = torch.complex64)
+  SG['S12'] = rcwa_utils_pt.expand_and_tile_pt(SG_S12, batchSize, pixelsX, pixelsY)
+
+  SG_S21 = torch.eye(2 * np.prod(PQ), dtype = torch.complex64)
+  SG['S21'] = rcwa_utils_pt.expand_and_tile_pt(SG_S21, batchSize, pixelsX, pixelsY)
+
+  SG_S22 = torch.zeros((2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = torch.complex64)
+  SG['S22'] = rcwa_utils_pt.expand_and_tile_pt(SG_S22, batchSize, pixelsX, pixelsY)
+
+  ### Step 5: Calculate eigenmodes ###
+
+  # Build the eigenvalue problem.
+  P_00 = torch.matmul(KX, torch.linalg.inv(ERC))
+  P_00 = torch.matmul(P_00, KY)
+
+  P_01 = torch.matmul(KX, torch.linalg.inv(ERC))
+  P_01 = torch.matmul(P_01, KX)
+  P_01 = URC - P_01
+
+  P_10 = torch.matmul(KY, torch.linalg.inv(ERC))
+  P_10 = torch.matmul(P_10, KY) - URC
+
+  P_11 = torch.matmul(-KY, torch.linalg.inv(ERC))
+  P_11 = torch.matmul(P_11, KX)
+
+  P_row0 = torch.cat([P_00, P_01], dim = 5)
+  P_row1 = torch.cat([P_10, P_11], dim = 5)
+  P = torch.cat([P_row0, P_row1], dim = 4)
+
+  Q_00 = torch.matmul(KX, torch.linalg.inv(URC))
+  Q_00 = torch.matmul(Q_00, KY)
+
+  Q_01 = torch.matmul(KX, torch.linalg.inv(URC))
+  Q_01 = torch.matmul(Q_01, KX)
+  Q_01 = ERC - Q_01
+
+  Q_10 = torch.matmul(KY, torch.linalg.inv(URC))
+  Q_10 = torch.matmul(Q_10, KY) - ERC
+
+  Q_11 = torch.matmul(-KY, torch.linalg.inv(URC))
+  Q_11 = torch.matmul(Q_11, KX)
+
+  Q_row0 = torch.cat([Q_00, Q_01], dim = 5)
+  Q_row1 = torch.cat([Q_10, Q_11], dim = 5)
+  Q = torch.cat([Q_row0, Q_row1], dim = 4)
+
+  # Compute eignmodes for the layers in each pixel for the whole batch.
+  OMEGA_SQ = torch.matmul(P, Q)
+  #LAM, W = rcwa_utils_pt.eig_general(OMEGA_SQ)
+  LAM, W = checkpoint(rcwa_utils_pt.eig_general, OMEGA_SQ)
+  LAM = torch.sqrt(LAM)
+  LAM = torch.diag_embed(LAM, dim1 = -2, dim2 = -1)
+
+  V = torch.matmul(Q, W)
+  V = torch.matmul(V, torch.linalg.inv(LAM))
+
+  # Scattering matrices for the layers in each pixel for the whole batch.
+  W_inv = torch.linalg.inv(W)
+  V_inv = torch.linalg.inv(V)
+  A = torch.matmul(W_inv, W0) + torch.matmul(V_inv, V0)
+  B = torch.matmul(W_inv, W0) - torch.matmul(V_inv, V0)
+  X = torch.matrix_exp(-LAM * k0 * L)
+  #X = checkpoint(torch.matrix_exp, -LAM * k0 * L)
+
+  S = dict({})
+  A_inv = torch.linalg.inv(A)
+  S11_left = torch.matmul(X, B)
+  S11_left = torch.matmul(S11_left, A_inv)
+  S11_left = torch.matmul(S11_left, X)
+  S11_left = torch.matmul(S11_left, B)
+  S11_left = A - S11_left
+  S11_left = torch.linalg.inv(S11_left)
+
+  S11_right = torch.matmul(X, B)
+  S11_right = torch.matmul(S11_right, A_inv)
+  S11_right = torch.matmul(S11_right, X)
+  S11_right = torch.matmul(S11_right, A)
+  S11_right = S11_right - B
+  S['S11'] = torch.matmul(S11_left, S11_right)
+
+  S12_right = torch.matmul(B, A_inv)
+  S12_right = torch.matmul(S12_right, B)
+  S12_right = A - S12_right
+  S12_left = torch.matmul(S11_left, X)
+  S['S12'] = torch.matmul(S12_left, S12_right)
+
+  S['S21'] = S['S12']
+  S['S22'] = S['S11']
+
+  # Update the global scattering matrices.
+  for l in range(Nlay):
+    S_layer = dict({})
+    S_layer['S11'] = S['S11'][:, :, :, l, :, :]
+    S_layer['S12'] = S['S12'][:, :, :, l, :, :]
+    S_layer['S21'] = S['S21'][:, :, :, l, :, :]
+    S_layer['S22'] = S['S22'][:, :, :, l, :, :]
+    SG = rcwa_utils_pt.redheffer_star_product(SG, S_layer)
+
+  ### Step 6: Reflection side ###
+  # Eliminate layer dimension for tensors as they are unchanging on this dimension.
+  KX = KX[:, :, :, 0, :, :]
+  KY = KY[:, :, :, 0, :, :]
+  KZref = KZref[:, :, :, 0, :, :]
+  KZtrn = KZtrn[:, :, :, 0, :, :]
+  Z = Z[:, :, :, 0, :, :]
+  I = I[:, :, :, 0, :, :]
+  W0 = W0[:, :, :, 0, :, :]
+  V0 = V0[:, :, :, 0, :, :]
+
+  Q_ref_00 = torch.matmul(KX, KY)
+  Q_ref_01 = ur1 * er1 * I - torch.matmul(KX, KX)
+  Q_ref_10 = torch.matmul(KY, KY) - ur1 * er1 * I
+  Q_ref_11 = -torch.matmul(KY, KX)
+  Q_ref_row0 = torch.cat([Q_ref_00, Q_ref_01], dim = 4)
+  Q_ref_row1 = torch.cat([Q_ref_10, Q_ref_11], dim = 4)
+  Q_ref = torch.cat([Q_ref_row0, Q_ref_row1], dim = 3)
+
+  W_ref_row0 = torch.cat([I, Z], dim = 4)
+  W_ref_row1 = torch.cat([Z, I], dim = 4)
+  W_ref = torch.cat([W_ref_row0, W_ref_row1], dim = 3)
+
+  LAM_ref_row0 = torch.cat([-1j * KZref, Z], dim = 4)
+  LAM_ref_row1 = torch.cat([Z, -1j * KZref], dim = 4)
+  LAM_ref = torch.cat([LAM_ref_row0, LAM_ref_row1], dim = 3)
+
+  V_ref = torch.matmul(Q_ref, torch.linalg.inv(LAM_ref))
+
+  W0_inv = torch.linalg.inv(W0)
+  V0_inv = torch.linalg.inv(V0)
+  A_ref = torch.matmul(W0_inv, W_ref) + torch.matmul(V0_inv, V_ref)
+  A_ref_inv = torch.linalg.inv(A_ref)
+  B_ref = torch.matmul(W0_inv, W_ref) - torch.matmul(V0_inv, V_ref)
+
+  SR = dict({})
+  SR['S11'] = torch.matmul(-A_ref_inv, B_ref)
+  SR['S12'] = 2 * A_ref_inv
+  SR_S21 = torch.matmul(B_ref, A_ref_inv)
+  SR_S21 = torch.matmul(SR_S21, B_ref)
+  SR['S21'] = 0.5 * (A_ref - SR_S21)
+  SR['S22'] = torch.matmul(B_ref, A_ref_inv)
+
+  ### Step 7: Transmission side ###
+  Q_trn_00 = torch.matmul(KX, KY)
+  Q_trn_01 = ur2 * er2 * I - torch.matmul(KX, KX)
+  Q_trn_10 = torch.matmul(KY, KY) - ur2 * er2 * I
+  Q_trn_11 = -torch.matmul(KY, KX)
+  Q_trn_row0 = torch.cat([Q_trn_00, Q_trn_01], dim = 4)
+  Q_trn_row1 = torch.cat([Q_trn_10, Q_trn_11], dim = 4)
+  Q_trn = torch.cat([Q_trn_row0, Q_trn_row1], dim = 3)
+
+  W_trn_row0 = torch.cat([I, Z], dim = 4)
+  W_trn_row1 = torch.cat([Z, I], dim = 4)
+  W_trn = torch.cat([W_trn_row0, W_trn_row1], dim = 3)
+
+  LAM_trn_row0 = torch.cat([1j * KZtrn, Z], dim = 4)
+  LAM_trn_row1 = torch.cat([Z, 1j * KZtrn], dim = 4)
+  LAM_trn = torch.cat([LAM_trn_row0, LAM_trn_row1], dim = 3)
+
+  V_trn = torch.matmul(Q_trn, torch.linalg.inv(LAM_trn))
+
+  W0_inv = torch.linalg.inv(W0)
+  V0_inv = torch.linalg.inv(V0)
+  A_trn = torch.matmul(W0_inv, W_trn) + torch.matmul(V0_inv, V_trn)
+  A_trn_inv = torch.linalg.inv(A_trn)
+  B_trn = torch.matmul(W0_inv, W_trn) - torch.matmul(V0_inv, V_trn)
+
+  ST = dict({})
+  ST['S11'] = torch.matmul(B_trn, A_trn_inv)
+  ST_S12 = torch.matmul(B_trn, A_trn_inv)
+  ST_S12 = torch.matmul(ST_S12, B_trn)
+  ST['S12'] = 0.5 * (A_trn - ST_S12)
+  ST['S21'] = 2 * A_trn_inv
+  ST['S22'] = torch.matmul(-A_trn_inv, B_trn)
+
+  ### Step 8: Compute global scattering matrix ###
+  SG = rcwa_utils_pt.redheffer_star_product(SR, SG)
+  SG = rcwa_utils_pt.redheffer_star_product(SG, ST)
+  
+  ### Step 9: Compute source parameters ###
+  # Compute mode coefficients of the source.
+  delta = torch.zeros((batchSize, pixelsX, pixelsY, np.prod(PQ)), dtype = torch.float32)
+  delta[:, :, :, int(np.prod(PQ) / 2.0)] = 1
+
+  # Incident wavevector.
+  kinc_x0_pol = torch.real(kinc_x0[:, :, :, 0, 0])
+  kinc_y0_pol = torch.real(kinc_y0[:, :, :, 0, 0])
+  kinc_z0_pol = torch.real(kinc_z0[:, :, :, 0])
+  kinc_pol = torch.cat([kinc_x0_pol, kinc_y0_pol, kinc_z0_pol], dim = 3)
+
+  # Calculate TE and TM polarization unit vectors.
+  firstPol = True
+  for pol in range(batchSize):
+    if (kinc_pol[pol, 0, 0, 0] == 0.0 and kinc_pol[pol, 0, 0, 1] == 0.0):
+      ate_pol = np.zeros((1, pixelsX, pixelsY, 3))
+      ate_pol[:, :, :, 1] = 1
+      ate_pol = torch.tensor(ate_pol, dtype = torch.float32)
+    else:
+      # Calculation of `ate` for oblique incidence.
+      n_hat = np.zeros((1, pixelsX, pixelsY, 3))
+      n_hat[:, :, :, 0] = 1
+      n_hat = torch.tensor(n_hat, dtype = torch.float32)
+      kinc_pol_iter = kinc_pol[pol, :, :, :]
+      kinc_pol_iter = kinc_pol_iter[None, :, :, :]
+      ate_cross = torch.cross(n_hat, kinc_pol_iter)
+      ate_pol =  ate_cross / torch.linalg.norm(ate_cross, dim = 3, keepdim = True)
+
+    if firstPol:
+      ate = ate_pol
+      firstPol = False
+    else:
+      ate = torch.cat([ate, ate_pol], dim = 0)
+
+  atm_cross = torch.cross(kinc_pol, ate)
+  atm = atm_cross / torch.linalg.norm(atm_cross, dim = 3, keepdim = True)
+  ate = ate.type(torch.complex64)
+  atm = atm.type(torch.complex64)
+
+  # Decompose the TE and TM polarization into x and y components.
+  EP = pte * ate + ptm * atm
+  EP_x = EP[:, :, :, 0]
+  EP_x = EP_x[:, :, :, None]
+  EP_y = EP[:, :, :, 1]
+  EP_y = EP_y[:, :, :, None]
+
+  esrc_x = EP_x * delta
+  esrc_y = EP_y * delta
+
+  esrc = torch.cat([esrc_x, esrc_y], dim = 3)
+  esrc = esrc[:, :, :, :, None]
+
+  W_ref_inv = torch.linalg.inv(W_ref)
+
+  ### Step 10: Compute reflected and transmitted fields ###
+  csrc = torch.matmul(W_ref_inv, esrc)
+
+  # Compute tranmission and reflection mode coefficients.
+  cref = torch.matmul(SG['S11'], csrc)
+  ctrn = torch.matmul(SG['S21'], csrc)
+  eref = torch.matmul(W_ref, cref)
+  etrn = torch.matmul(W_trn, ctrn)
+
+  rx = eref[:, :, :, 0 : np.prod(PQ), :]
+  ry = eref[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+  tx = etrn[:, :, :, 0 : np.prod(PQ), :]
+  ty = etrn[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+
+  # Compute longitudinal components.
+  KZref_inv = torch.linalg.inv(KZref)
+  KZtrn_inv = torch.linalg.inv(KZtrn)
+  rz = torch.matmul(KX, rx) + torch.matmul(KY, ry)
+  rz = torch.matmul(-KZref_inv, rz)
+  tz = torch.matmul(KX, tx) + torch.matmul(KY, ty)
+  tz = torch.matmul(-KZtrn_inv, tz)
+
+  ### Step 11: Compute diffraction efficiences ###
+  rx2 = torch.real(rx) ** 2 + torch.imag(rx) ** 2
+  ry2 = torch.real(ry) ** 2 + torch.imag(ry) ** 2
+  rz2 = torch.real(rz) ** 2 + torch.imag(rz) ** 2
+  R2 = rx2 + ry2 + rz2
+  R = torch.real(-KZref / ur1) / torch.real(kinc_z0 / ur1)
+  R = torch.matmul(R, R2)
+  R = torch.reshape(R, (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  REF = torch.sum(R, dim = [3, 4])
+
+  tx2 = torch.real(tx) ** 2 + torch.imag(tx) ** 2
+  ty2 = torch.real(ty) ** 2 + torch.imag(ty) ** 2
+  tz2 = torch.real(tz) ** 2 + torch.imag(tz) ** 2
+  T2 = tx2 + ty2 + tz2
+  T = torch.real(KZtrn / ur2) / torch.real(kinc_z0 / ur2)
+  T = torch.matmul(T, T2)
+  T = torch.reshape(T, (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  TRN = torch.sum(T, dim = [3, 4])
+  
+  # Store the transmission/reflection coefficients and powers in a dictionary.
+  outputs = dict({})
+  outputs['rx'] = rx
+  outputs['ry'] = ry
+  outputs['rz'] = rz
+  outputs['R'] = R
+  outputs['REF'] = REF
+  outputs['tx'] = tx
+  outputs['ty'] = ty
+  outputs['tz'] = tz
+  outputs['T'] = T
+  outputs['TRN'] = TRN
+  
+  return outputs","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/src/fab.py",".py","5532","131","# Copyright (c) 2020, Shane Colburn, University of Washington
+# This file is part of rcwa_tf
+# Written by Shane Colburn (Email: scolbur2@uw.edu)
+
+import tensorflow as tf
+import numpy as np
+
+    
+def convolve_density_with_blur(density, blur):
+  '''
+    This function computes the convolution of two inputs to return a blurred
+    density function.
+    Args:
+        density: A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, 
+        pixelsY, Nlayers - 1, Nx, Ny)` specifying a density function with values
+        in the range from 0 to 1 on the Nx and Ny dimensions.
+
+        blur: A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, 
+        pixelsY, Nlayers - 1, Nx, Ny)` specifying a blur function on the Nx and
+        Ny dimensions.
+    Returns:
+        A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, pixelsY, 
+        Nlayers - 1, Nx, Ny)` specifying the blurred density.
+    '''
+
+  _, _, _, _, Nx, Ny = density.shape
+
+  # Padding to accomodate linear convolution.
+  paddings = ((0, 0), (0, 0), (0, 0), (0, 0), (Nx // 2, Nx // 2), (Ny // 2, Ny // 2))
+  density_padded = tf.pad(density, paddings = paddings)
+  density_padded = tf.cast(density_padded, dtype = tf.complex64)
+
+  blur_padded = tf.pad(blur, paddings = paddings)
+  blur_padded = tf.cast(blur_padded, dtype = tf.complex64)
+
+  # Perform the convolution in the Fourier domain and return the image.
+  convolved = tf.signal.ifftshift(tf.signal.ifft2d(tf.signal.fft2d(density_padded) * tf.signal.fft2d(blur_padded)), axes = (4, 5))
+  x_low = Nx // 2
+  x_high = x_low + Nx
+  y_low = Ny // 2
+  y_high = y_low + Ny
+  convolved_cropped = convolved[:, :, :, :, x_low : x_high, y_low : y_high]
+    
+  return tf.abs(convolved_cropped)
+
+
+def blur_unit_cell(eps_r, params):
+  '''
+    This function blurs a unit cell to remove high spatial frequency features.
+    Args:
+        eps_r: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, Nlayer - 1, Nx, Ny)`
+        and dtype `tf.float32` specifying the permittivity at each point in the 
+        unit cell grid.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, pixelsY, 
+        Nlayers - 1, Nx, Ny)` specifying the blurred real space permittivity
+        on a Cartesian grid.
+    '''
+  # Define the cartesian cross section.
+  dx = params['Lx'] / params['Nx'] # grid resolution along x
+  dy = params['Ly'] / params['Ny'] # grid resolution along y
+  xa = np.linspace(0, params['Nx'] - 1, params['Nx']) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, params['Ny'] - 1, params['Ny']) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Blur function.
+  R = params['blur_radius']
+  circ = tf.cast(x_mesh ** 2 + y_mesh **2 < R ** 2, dtype = tf.float32)
+  decay = tf.cast(R - tf.math.sqrt(x_mesh ** 2 + y_mesh ** 2), dtype = tf.float32)
+  weight = circ * decay
+  weight = weight[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  weight = weight / tf.math.reduce_sum(weight)
+
+  # Blur the unit cell permittivity.
+  density = (eps_r - params['eps_min']) / (params['eps_max'] - params['eps_min'])
+  density_blurred = convolve_density_with_blur(density, weight)
+
+  return density_blurred * (params['eps_max'] - params['eps_min']) + params['eps_min']
+
+
+def threshold(eps_r, params):
+  '''
+    This function applies a non-differentiable threshold operation to snap a 
+    design to binary permittivity values.
+    Args:
+        eps_r: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, Nlayer - 1, Nx, Ny)`
+        and dtype `tf.float32` specifying the permittivity at each point in the 
+        unit cell grid.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, pixelsY, 
+        Nlayers - 1, Nx, Ny)` specifying the binarized permittivity.
+    '''
+  # Apply the threshold.
+  eps_thresh = eps_r.numpy()
+  eps_thresh = (eps_thresh - params['eps_min']) / (params['eps_max'] - params['eps_min'])
+  eps_thresh = eps_thresh > 0.5
+  eps_thresh = eps_thresh * (params['eps_max'] - params['eps_min']) + params['eps_min']
+
+  return tf.convert_to_tensor(eps_thresh, dtype = tf.float32)
+
+
+def binary_push(eps_r, weight, params):
+  '''
+    This function applies a differentiable threshold operation that pushes
+    permittivity values towards a binary structure by means of a sigmoid.
+    Args:
+        eps_r: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, Nlayer - 1, Nx, Ny)`
+        and dtype `tf.float32` specifying the permittivity at each point in the 
+        unit cell grid.
+
+        weight: A `float` specifying the coefficient of the sigmoid argument that
+        specifices how hard to ""push"" the permittivity to a binary structure.
+        The higher the weight, the more binary the structure will be.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        A `tf.Tensor` of dtype `tf.float32` and shape `(1, pixelsX, pixelsY, 
+        Nlayers - 1, Nx, Ny)` specifying the permittivity after pushing the
+        permittivity closer to a binary structure.
+    '''
+  density = (eps_r - params['eps_min']) / (params['eps_max'] - params['eps_min'])
+  density_pushed = tf.math.sigmoid(weight * (density - 0.5))
+  eps_pushed = density_pushed * (params['eps_max'] - params['eps_min']) + params['eps_min']
+  return tf.convert_to_tensor(eps_pushed, dtype = tf.float32)
+","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/src/solver.py",".py","81862","2131","import tensorflow as tf
+import numpy as np
+import rcwa_utils
+import tensor_utils
+import utils
+import json
+import sys
+
+def initialize_params(wavelengths = [632.0],
+                      thetas = [0.0],
+                      phis = [0.0],
+                      pte = [1.0],
+                      ptm = [0.0],
+                      pixelsX = 1,
+                      pixelsY = 1,
+                      erd = 6.76,
+                      ers = 2.25,
+                      PQ = [11, 11],
+                      Lx = 0.7 * 632.0,
+                      Ly = 0.7 * 632.0,
+                      L = [632.0, 632.0],
+                      Nx = 512,
+                      eps_min = 1.0,
+                      eps_max = 12.11,
+                      blur_radius = 100.0):
+  '''
+    Initializes simulation parameters and hyperparameters.
+    Args:
+        wavelengths: A `list` of dtype `float` and length `batchSize` specifying
+        the set of wavelengths over which to optimize.
+
+        thetas: A `list` of dtype `float` and length `batchSize` specifying
+        the set of polar angles over which to optimize.
+
+        phis: A `list` of dtype `float` and length `batchSize` specifying the 
+        set of azimuthal angles over which to optimize.
+
+        pte: A `list` of dtype `float` and length `batchSize` specifying the set
+        of TE polarization component magnitudes over which to optimize. A 
+        magnitude of 0.0 means no TE component. Under normal incidence, the TE 
+        polarization is parallel to the y-axis.
+
+        ptm: A `list` of dtype `float` and length `batchSize` specifying the set
+        of TM polarization component magnitudes over which to optimize. A 
+        magnitude of 0.0 means no TM component. Under normal incidence, the TM 
+        polarization is parallel to the x-axis.
+
+        pixelsX: An `int` specifying the x dimension of the metasurface in
+        pixels that are of width `params['Lx']`.
+
+        pixelsY: An `int` specifying the y dimension of the metasurface in
+        pixels that are of width `params['Ly']`.
+
+        erd: A `float` specifying the relative permittivity of the non-vacuum,
+        constituent material of the device layer for shape optimizations.
+
+        ers: A `float` specifying the relative permittivity of the substrate
+        layer.
+
+        PQ: A `list` of dtype `int` and length 2 specifying the number of 
+        Fourier harmonics in the x and y directions. The numbers should be odd
+        values.
+
+        Lx: A `float` specifying the unit cell pitch in the x direction in
+        nanometers.
+
+        Ly: A `float` specifying the unit cell pitch in the y direction in
+        nanometers.
+
+        L: A `list` of dtype `float` specifying the layer thicknesses in 
+        nanometers.
+
+        Nx: An `int` specifying the number of sample points along the x 
+        direction in the unit cell.
+
+        eps_min: A `float` specifying the minimum allowed permittivity for 
+        topology optimizations.
+
+        eps_max: A `float` specifying the maximum allowed permittivity for 
+        topology optimizations.
+
+        blur_radius: A `float` specifying the radius of the blur function with 
+        which a topology optimized permittivity density should be convolved.
+        
+    Returns:
+        params: A `dict` containing simulation and optimization settings.
+  '''
+
+  # Define the `params` dictionary.
+  params = dict({})
+
+  # Units and tensor dimensions.
+  params['micrometers'] = 1E-6
+  params['degrees'] = np.pi / 180
+  params['batchSize'] = len(wavelengths)
+  params['pixelsX'] = pixelsX
+  params['pixelsY'] = pixelsY
+  params['Nlay'] = len(L)
+
+  # Simulation tensor shapes.
+  batchSize = params['batchSize']
+  simulation_shape = (batchSize, pixelsX, pixelsY)
+
+  # Batch parameters (wavelength, incidence angle, and polarization).
+  lam0 = params['micrometers'] * tf.convert_to_tensor(wavelengths, dtype = tf.float32)
+  lam0 = lam0[:, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis]
+  lam0 = tf.tile(lam0, multiples = (1, pixelsX, pixelsY, 1, 1, 1))
+  params['lam0'] = lam0
+
+  theta = params['degrees'] * tf.convert_to_tensor(thetas, dtype = tf.float32)
+  theta = theta[:, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis]
+  theta = tf.tile(theta, multiples = (1, pixelsX, pixelsY, 1, 1, 1))
+  params['theta'] = theta
+
+  phi = params['degrees'] * tf.convert_to_tensor(phis, dtype = tf.float32)
+  phi = phi[:, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis]
+  phi = tf.tile(phi, multiples = (1, pixelsX, pixelsY, 1, 1, 1))
+  params['phi'] = phi
+
+  pte = tf.convert_to_tensor(pte, dtype = tf.complex64)
+  pte = pte[:, tf.newaxis, tf.newaxis, tf.newaxis]
+  pte = tf.tile(pte, multiples = (1, pixelsX, pixelsY, 1))
+  params['pte'] = pte
+
+  ptm = tf.convert_to_tensor(ptm, dtype = tf.complex64)
+  ptm = ptm[:, tf.newaxis, tf.newaxis, tf.newaxis]
+  ptm = tf.tile(ptm, multiples = (1, pixelsX, pixelsY, 1))
+  params['ptm'] = ptm
+
+  # Device parameters.
+  params['ur1'] = 1.0 # permeability in reflection region
+  params['er1'] = 1.0 # permittivity in reflection region
+  params['ur2'] = 1.0 # permeability in transmission region
+  params['er2'] = 1.0 # permittivity in transmission region
+  params['urd'] = 1.0 # permeability of device
+  params['erd'] = erd # permittivity of device
+  params['urs'] = 1.0 # permeability of substrate
+  params['ers'] = ers # permittivity of substrate
+  params['Lx'] = Lx * params['micrometers'] # period along x
+  params['Ly'] = Ly * params['micrometers'] # period along y
+  length_shape = (1, 1, 1, params['Nlay'], 1, 1)
+  L = tf.convert_to_tensor(L, dtype = tf.complex64)
+  L = L[tf.newaxis, tf.newaxis, tf.newaxis, :, tf.newaxis, tf.newaxis]
+  params['L'] = L * params['micrometers'] #* tf.ones(shape = length_shape, dtype = tf.complex64)
+  params['length_min'] = 0.1
+  params['length_max'] = 2.0
+
+  # RCWA parameters.
+  params['PQ'] = PQ # number of spatial harmonics along x and y
+  params['Nx'] = Nx # number of point along x in real-space grid
+  if params['PQ'][1] == 1:
+    params['Ny'] = 1
+  else:
+    params['Ny'] = int(np.round(params['Nx'] * params['Ly'] / params['Lx'])) # number of point along y in real-space grid
+
+  # Coefficient for the argument of tf.math.sigmoid() when generating
+  # permittivity distributions with geometric parameters.
+  params['sigmoid_coeff'] = 1000.0
+
+  # Polynomial order for rectangular resonators definition.
+  params['rectangle_power'] = 200
+
+  # Allowed permittivity range.
+  params['eps_min'] = eps_min
+  params['eps_max'] = eps_max
+
+  # Upsampling for Fourier optics propagation.
+  params['upsample'] = 1
+
+  # Duty Cycle limits for gratings.
+  params['duty_min'] = 0.1
+  params['duty_max'] = 0.9
+
+  # Permittivity density blur radius.
+  params['blur_radius'] = blur_radius * params['micrometers']
+
+  return params
+
+
+def generate_coupled_cylindrical_resonators(r_x, r_y, params):
+    '''
+    Generates permittivity/permeability for a unit cell comprising 4 coupled
+    elliptical resonators.
+    Args:
+        r_x: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 4)` specifying the 
+        x-axis diameters of the four cylinders.
+
+        r_y: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 4)` specifying the 
+        y-axis diameters of the four cylinders.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+    '''
+
+    # Retrieve simulation size parameters.
+    batchSize = params['batchSize']
+    pixelsX = params['pixelsX']
+    pixelsY = params['pixelsY']
+    Nlay = params['Nlay']
+    Nx = params['Nx']
+    Ny = params['Ny']
+    Lx = params['Lx']
+    Ly = params['Ly']
+
+    # Initialize relative permeability.
+    materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+    UR = params['urd'] * np.ones(materials_shape)
+
+    # Define the cartesian cross section.
+    dx = Lx / Nx # grid resolution along x
+    dy = Ly / Ny # grid resolution along y
+    xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+    xa = xa - np.mean(xa) # center x axis at zero
+    ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+    ya = ya - np.mean(ya) # center y axis at zero
+    [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+    # Convert to tensors and expand and tile to match the simulation shape.
+    y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+    y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+    y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+    x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+    x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+    x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+    # Nanopost centers.
+    c1_x = -Lx / 4
+    c1_y = -Ly / 4
+    c2_x = -Lx / 4
+    c2_y = Ly / 4
+    c3_x = Lx / 4
+    c3_y = -Ly / 4
+    c4_x = Lx / 4
+    c4_y = Ly / 4
+
+    # Clip the optimization ranges.
+    r_x = params['Lx'] * tf.clip_by_value(r_x, clip_value_min = 0.05, clip_value_max = 0.23)
+    r_y = params['Ly'] * tf.clip_by_value(r_y, clip_value_min = 0.05, clip_value_max = 0.23)
+    r_x = tf.tile(r_x, multiples = (batchSize, 1, 1, 1))
+    r_y = tf.tile(r_y, multiples = (batchSize, 1, 1, 1))
+    r_x = r_x[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+    r_y = r_y[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+
+    # Calculate the nanopost boundaries.
+    c1 = 1 - ((x_mesh - c1_x) / r_x[:, :, :, :, :, :, 0]) ** 2 - ((y_mesh - c1_y) / r_y[:, :, :, :, :, :, 0]) ** 2
+    c2 = 1 - ((x_mesh - c2_x) / r_x[:, :, :, :, :, :, 1]) ** 2 - ((y_mesh - c2_y) / r_y[:, :, :, :, :, :, 1]) ** 2
+    c3 = 1 - ((x_mesh - c3_x) / r_x[:, :, :, :, :, :, 2]) ** 2 - ((y_mesh - c3_y) / r_y[:, :, :, :, :, :, 2]) ** 2
+    c4 = 1 - ((x_mesh - c4_x) / r_x[:, :, :, :, :, :, 3]) ** 2 - ((y_mesh - c4_y) / r_y[:, :, :, :, :, :, 3]) ** 2
+
+    # Build device layer.
+    ER_c1 = tf.math.sigmoid(params['sigmoid_coeff'] * c1)
+    ER_c2 = tf.math.sigmoid(params['sigmoid_coeff'] * c2)
+    ER_c3 = tf.math.sigmoid(params['sigmoid_coeff'] * c3)
+    ER_c4 = tf.math.sigmoid(params['sigmoid_coeff'] * c4)
+    ER_t = 1 + (params['erd'] - 1) * (ER_c1 + ER_c2 + ER_c3 + ER_c4)
+
+    # Build substrate and concatenate along the layers dimension.
+    device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+    ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+    ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+    # Cast to complex for subsequent calculations.
+    ER_t = tf.cast(ER_t, dtype = tf.complex64)
+    UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+    UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+    return ER_t, UR_t
+
+
+def generate_coupled_rectangular_resonators(r_x, r_y, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising 4 coupled
+    rectangular cross section scatterers.
+    Args:
+        r_x: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 4)` specifying the 
+        x-axis widths of the four rectangles.
+
+        r_y: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 4)` specifying the 
+        y-axis widths of the four rectangles.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+  Lx = params['Lx']
+  Ly = params['Ly']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = Lx / Nx # grid resolution along x
+  dy = Ly / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Nanopost centers.
+  c1_x = -Lx / 4
+  c1_y = -Ly / 4
+  c2_x = -Lx / 4
+  c2_y = Ly / 4
+  c3_x = Lx / 4
+  c3_y = -Ly / 4
+  c4_x = Lx / 4
+  c4_y = Ly / 4
+
+  # Nanopost width ranges.
+  r_x = params['Lx'] * tf.clip_by_value(r_x, clip_value_min = 0.05, clip_value_max = 0.23)
+  r_y = params['Ly'] * tf.clip_by_value(r_y, clip_value_min = 0.05, clip_value_max = 0.23)
+  r_x = tf.tile(r_x, multiples = (batchSize, 1, 1, 1))
+  r_y = tf.tile(r_y, multiples = (batchSize, 1, 1, 1))
+  r_x = r_x[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  r_y = r_y[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+
+  # Calculate the nanopost boundaries.
+  c1 = 1 - ((x_mesh - c1_x) / r_x[:, :, :, :, :, :, 0]) ** params['rectangle_power'] - ((y_mesh - c1_y) / r_y[:, :, :, :, :, :, 0]) ** params['rectangle_power']
+  c2 = 1 - ((x_mesh - c2_x) / r_x[:, :, :, :, :, :, 1]) ** params['rectangle_power'] - ((y_mesh - c2_y) / r_y[:, :, :, :, :, :, 1]) ** params['rectangle_power']
+  c3 = 1 - ((x_mesh - c3_x) / r_x[:, :, :, :, :, :, 2]) ** params['rectangle_power'] - ((y_mesh - c3_y) / r_y[:, :, :, :, :, :, 2]) ** params['rectangle_power']
+  c4 = 1 - ((x_mesh - c4_x) / r_x[:, :, :, :, :, :, 3]) ** params['rectangle_power'] - ((y_mesh - c4_y) / r_y[:, :, :, :, :, :, 3]) ** params['rectangle_power']
+
+  # Build device layer.
+  ER_c1 = tf.math.sigmoid(params['sigmoid_coeff'] * c1)
+  ER_c2 = tf.math.sigmoid(params['sigmoid_coeff'] * c2)
+  ER_c3 = tf.math.sigmoid(params['sigmoid_coeff'] * c3)
+  ER_c4 = tf.math.sigmoid(params['sigmoid_coeff'] * c4)
+  ER_t = 1 + (params['erd'] - 1) * (ER_c1 + ER_c2 + ER_c3 + ER_c4)
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_rectangular_resonators(r_x, r_y, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a single, 
+    centered rectangular cross section scatterer.
+
+    Args:
+        r_x: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        x-axis widths of the rectangle.
+
+        r_y: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        y-axis widths of the rectangle.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+  Lx = params['Lx']
+  Ly = params['Ly']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = Lx / Nx # grid resolution along x
+  dy = Ly / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Limit the optimization ranges.
+  r_x = params['Lx'] * tf.clip_by_value(r_x, clip_value_min = 0.05, clip_value_max = 0.46)
+  r_y = params['Ly'] * tf.clip_by_value(r_y, clip_value_min = 0.05, clip_value_max = 0.46)
+  r_x = tf.tile(r_x, multiples = (batchSize, 1, 1, 1))
+  r_y = tf.tile(r_y, multiples = (batchSize, 1, 1, 1))
+  r_x = r_x[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  r_y = r_y[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+
+  r1 = 1 - tf.abs((x_mesh / 2 / r_x[:, :, :, :, :, :, 0]) - (y_mesh / 2 / r_y[:, :, :, :, :, :, 0])) - tf.abs((x_mesh / 2 / r_x[:, :, :, :, :, :, 0]) + (y_mesh / 2 / r_y[:, :, :, :, :, :, 0]))
+
+  # Build device layer.
+  ER_r1 = tf.math.sigmoid(params['sigmoid_coeff'] * r1)
+  ER_t = 1 + (params['erd'] - 1) * ER_r1
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_elliptical_resonators(r_x, r_y, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a single, 
+    centered elliptical cross section scatterer.
+
+    Args:
+        r_x: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        x-axis diameter of the ellipse.
+
+        r_y: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        y-axis diameter of the ellipse.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+  Lx = params['Lx']
+  Ly = params['Ly']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = Lx / Nx # grid resolution along x
+  dy = Ly / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Limit the optimization ranges.
+  r_x = params['Lx'] * tf.clip_by_value(r_x, clip_value_min = 0.05, clip_value_max = 0.46)
+  r_y = params['Ly'] * tf.clip_by_value(r_y, clip_value_min = 0.05, clip_value_max = 0.46)
+  r_x = tf.tile(r_x, multiples = (batchSize, 1, 1, 1))
+  r_y = tf.tile(r_y, multiples = (batchSize, 1, 1, 1))
+  r_x = r_x[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  r_y = r_y[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis, :]
+
+  # Calculate the ellipse boundary.
+  c1 = 1 - (x_mesh / r_x[:, :, :, :, :, :, 0]) ** 2 - (y_mesh / r_y[:, :, :, :, :, :, 0]) ** 2
+  
+  # Build device layer.
+  ER_c1 = tf.math.sigmoid(params['sigmoid_coeff'] * c1)
+  ER_t = 1 + (params['erd'] - 1) * ER_c1
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_cylindrical_nanoposts(duty, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a single, 
+    centered circular cross section scatterer.
+
+    Args:
+        duty: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        duty cycle (diameter / period) of the cylindrical nanopost.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = params['Lx'] / Nx # grid resolution along x
+  dy = params['Ly'] / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Build device layer.
+  a = tf.clip_by_value(duty, clip_value_min = params['duty_min'], clip_value_max = params['duty_max'])
+  a = a[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis]
+  a = tf.tile(a, multiples = (1, 1, 1, 1, Nx, Ny))
+  radius = 0.5 * params['Ly'] * a
+  sigmoid_arg = (1 - (x_mesh / radius) ** 2 - (y_mesh / radius) ** 2)
+  ER_t = tf.math.sigmoid(params['sigmoid_coeff'] * sigmoid_arg)
+  ER_t = 1 + (params['erd'] - 1) * ER_t
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_stacked_cylindrical_nanoposts(duty, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a stacked
+    cylinders.
+
+    Args:
+        duty: A `tf.Tensor` of shape `(1, 1, 1, Nlay - 1, 1, 1)` specifying the 
+        thicknesses of the cylinders in each layer, excluding the substrate 
+        tihckness.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = params['Lx'] / Nx # grid resolution along x
+  dy = params['Ly'] / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, Nlay - 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, Nlay - 1, 1, 1))
+
+  # Build device layer.
+  a = tf.clip_by_value(duty, clip_value_min = params['duty_min'], clip_value_max = params['duty_max'])
+  a = a[:, :, :, :, tf.newaxis, tf.newaxis]
+  a = tf.tile(a, multiples = (1, 1, 1, 1, Nx, Ny))
+  radius = 0.5 * params['Ly'] * a
+  sigmoid_arg = (1 - (x_mesh / radius) ** 2 - (y_mesh / radius) ** 2)
+  ER_t = tf.math.sigmoid(params['sigmoid_coeff'] * sigmoid_arg)
+  ER_t = 1 + (params['erd'] - 1) * ER_t
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_rectangular_lines(duty, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a single
+    rectangle that spans the full y length and with width defined along the x
+    direction.
+
+    Args:
+        duty: A `tf.Tensor` of shape `(1, pixelsX, pixelsY)` specifying the duty
+        cycle (i.e., width / pitch) along the x direction for the rectangle.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = params['Lx'] / Nx # grid resolution along x
+  dy = params['Ly'] / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Build device layer.
+  a = tf.clip_by_value(duty, clip_value_min = params['duty_min'], clip_value_max = params['duty_max'])
+  a = a[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis]
+  a = tf.tile(a, multiples = (1, 1, 1, 1, Nx, Ny))
+  radius = 0.5 * params['Ly'] * a
+  sigmoid_arg = 1 - tf.math.abs(x_mesh / radius)
+  ER_t = tf.math.sigmoid(params['sigmoid_coeff'] * sigmoid_arg)
+  ER_t = 1 + (params['erd'] - 1) * ER_t
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_plasmonic_cylindrical_nanoposts(duty, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a single, 
+    centered circular cross section plasmonic scatterer with a complex-valued
+    permittivity.
+
+    Args:
+        duty: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, 1)` specifying the 
+        duty cycle (diameter / period) of the cylindrical nanopost.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Define the cartesian cross section.
+  dx = params['Lx'] / Nx # grid resolution along x
+  dy = params['Ly'] / Ny # grid resolution along y
+  xa = np.linspace(0, Nx - 1, Nx) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, Ny - 1, Ny) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya,xa)
+
+  # Convert to tensors and expand and tile to match the simulation shape.
+  y_mesh = tf.convert_to_tensor(y_mesh, dtype = tf.float32)
+  y_mesh = y_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  y_mesh = tf.tile(y_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+  x_mesh = tf.convert_to_tensor(x_mesh, dtype = tf.float32)
+  x_mesh = x_mesh[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  x_mesh = tf.tile(x_mesh, multiples = (batchSize, pixelsX, pixelsY, 1, 1, 1))
+
+  # Build device layer.
+  a = tf.clip_by_value(duty, clip_value_min = params['duty_min'], clip_value_max = params['duty_max'])
+  a = a[:, :, :, tf.newaxis, tf.newaxis, tf.newaxis]
+  a = tf.tile(a, multiples = (1, 1, 1, 1, Nx, Ny))
+  radius = 0.5 * params['Ly'] * a
+  sigmoid_arg = (1 - (x_mesh / radius) ** 2 - (y_mesh / radius) ** 2)
+  ER_t = tf.math.sigmoid(params['sigmoid_coeff'] * sigmoid_arg)
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  ER_t = 1 + (params['erd'] - 1) * ER_t
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.complex64)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def generate_arbitrary_epsilon(eps_r, params):
+  '''
+    Generates permittivity/permeability for a unit cell comprising a continuously
+    varying permittivity for each pixel in the Cartesian grid.
+
+    Args:
+        eps_r: A `tf.Tensor` of shape `(1, pixelsX, pixelsY, Nlayer - 1, Nx, Ny)`
+        and type `tf.float32` specifying the permittivity at each point in the 
+        unit cell grid. The `Nlayer - 1` eps_r.shape[3] length corresponds to 
+        there being a fixed substrate that is unchanging between iterations.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        specifying the relative permeability distribution of the unit cell.
+  '''
+
+  # Retrieve simulation size parameters.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  Nx = params['Nx']
+  Ny = params['Ny']
+
+  # Initialize relative permeability.
+  materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+  UR = params['urd'] * np.ones(materials_shape)
+
+  # Set the permittivity.
+  ER_t = tf.clip_by_value(eps_r, clip_value_min = params['eps_min'], clip_value_max = params['eps_max'])
+  ER_t = tf.tile(ER_t, multiples = (batchSize, 1, 1, 1, 1, 1))
+
+  # Build substrate and concatenate along the layers dimension.
+  device_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+  ER_substrate = params['ers'] * tf.ones(device_shape, dtype = tf.float32)
+  ER_t = tf.concat(values = [ER_t, ER_substrate], axis = 3)
+
+  # Cast to complex for subsequent calculations.
+  ER_t = tf.cast(ER_t, dtype = tf.complex64)
+  UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+  UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+  return ER_t, UR_t
+
+
+def make_propagator(params, f):
+  '''
+    Pre-computes the band-limited angular spectrum propagator for modelling
+    free-space propagation for the distance and sampling as specified in `params`.
+
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+
+        f: A `float` specifying the focal length, or distance to propagate, in
+        meters.
+    Returns:
+        propagator: a `tf.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `tf.complex64` defining the 
+        reciprocal space, band-limited angular spectrum propagator.
+  '''
+
+  # Simulation tensor shape.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  upsample = params['upsample']
+
+  # Propagator definition.
+  k = 2 * np.pi / params['lam0'][:, 0, 0, 0, 0, 0]
+  k = k[:, np.newaxis, np.newaxis]
+  samp = params['upsample'] * pixelsX
+  k = tf.tile(k, multiples = (1, 2 * samp - 1, 2 * samp - 1))
+  k = tf.cast(k, dtype = tf.complex64)  
+  k_xlist_pos = 2 * np.pi * np.linspace(0, 1 / (2 *  params['Lx'] / params['upsample']), samp)  
+  front = k_xlist_pos[-(samp - 1):]
+  front = -front[::-1]
+  k_xlist = np.hstack((front, k_xlist_pos))
+  k_x = np.kron(k_xlist, np.ones((2 * samp - 1, 1)))
+  k_x = k_x[np.newaxis, :, :]
+  k_y = np.transpose(k_x, axes = [0, 2, 1])
+  k_x = tf.convert_to_tensor(k_x, dtype = tf.complex64)
+  k_x = tf.tile(k_x, multiples = (batchSize, 1, 1))
+  k_y = tf.convert_to_tensor(k_y, dtype = tf.complex64)
+  k_y = tf.tile(k_y, multiples = (batchSize, 1, 1))
+  k_z_arg = tf.square(k) - (tf.square(k_x) + tf.square(k_y))
+  k_z = tf.sqrt(k_z_arg)
+  propagator_arg = 1j * k_z * f
+  propagator = tf.exp(propagator_arg)
+
+  # Limit transfer function bandwidth to prevent aliasing.
+  kx_limit = 2 * np.pi * (((1 / (pixelsX * params['Lx'])) * f) ** 2 + 1) ** (-0.5) / params['lam0'][:, 0, 0, 0, 0, 0]
+  kx_limit = tf.cast(kx_limit, dtype = tf.complex64)
+  ky_limit = kx_limit
+  kx_limit = kx_limit[:, tf.newaxis, tf.newaxis]
+  ky_limit = ky_limit[:, tf.newaxis, tf.newaxis]
+
+  # Apply the antialiasing filter.
+  '''
+  if params['enable_graphmode']:
+     # For graph mode, removed call to .numpy() as this is disallowed by tf.function decorator.
+     ellipse_kx = tf.math.real(tf.square(k_x / kx_limit) + tf.square(k_y / k)) <= 1
+     ellipse_ky = tf.math.real(tf.square(k_x / k) + tf.square(k_y / ky_limit)) <= 1   
+     propagator = propagator * tf.cast(ellipse_kx, tf.complex64) * tf.cast(ellipse_ky, tf.complex64)
+  
+  else:
+  '''
+  ellipse_kx = tf.math.real(tf.square(k_x / kx_limit) + tf.square(k_y / k)).numpy() <= 1
+  ellipse_ky = tf.math.real(tf.square(k_x / k) + tf.square(k_y / ky_limit)).numpy() <= 1   
+  propagator = propagator * ellipse_kx * ellipse_ky
+
+  return propagator
+
+
+def propagate(field, propagator, upsample):
+  '''
+    Propagates a batch of input fields to a parallel output plane using the 
+    band-limited angular spectrum method.
+
+    Args:
+        field: A `tf.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `tf.complex64` specifying the 
+        input electric fields to be diffracted to the output plane.
+
+        propagator: a `tf.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `tf.complex64` defining the 
+        reciprocal space, band-limited angular spectrum propagator.
+
+        upsample: An odd-valued `int` specifying the factor by which the
+        transverse field data stored in `field` should be upsampled.
+    Returns:
+        out: A `tf.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+        params['upsample'] * pixelsY)` and dtype `tf.complex64` specifying the 
+        the electric fields at the output plane.
+  '''
+
+  # Zero pad `field` to be a stack of 2n-1 x 2n-1 matrices
+  # Put batch parameter last for padding then transpose back.
+  _, _, m = field.shape
+  n = upsample * m
+  
+  field = tf.transpose(field, perm = [1, 2, 0])
+  field_real = tf.math.real(field)
+  field_imag = tf.math.imag(field)
+  field_real = tf.image.resize(field_real, [n, n], method = 'nearest')
+  field_imag = tf.image.resize(field_imag, [n, n], method = 'nearest')
+  field = tf.cast(field_real, dtype = tf.complex64) + 1j * tf.cast(field_imag, dtype = tf.complex64)
+  field = tf.image.resize_with_crop_or_pad(field, 2 * n - 1, 2 * n - 1)
+  field = tf.transpose(field, perm = [2, 0, 1])
+
+  # Apply the propagator in Fourier space.
+  field_freq = tf.signal.fftshift(tf.signal.fft2d(field), axes = (1, 2))
+
+  field_filtered = tf.signal.ifftshift(field_freq * propagator, axes = (1, 2))
+  out = tf.signal.ifft2d(field_filtered)
+
+  # Crop back down to n x n matrices.
+  out = tf.transpose(out, perm = [1, 2, 0])
+  out = tf.image.resize_with_crop_or_pad(out, n, n)
+  out = tf.transpose(out, perm = [2, 0, 1])
+
+  return out
+
+propagate_recompute = tf.recompute_grad(propagate)
+
+def define_input_fields(params):
+  '''
+    Given the batch of input conditions with different wavelengths and incidence
+    angles, this gives the input source fields incident on the metasurface.
+
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY)` and dtype 
+        `tf.complex64` specifying the source fields injected onto a metasurface
+        at the input.
+  '''
+
+  # Define the cartesian cross section.
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  dx = params['Lx'] # grid resolution along x
+  dy = params['Ly'] # grid resolution along y
+  xa = np.linspace(0, pixelsX - 1, pixelsX) * dx # x axis array
+  xa = xa - np.mean(xa) # center x axis at zero
+  ya = np.linspace(0, pixelsY - 1, pixelsY) * dy # y axis vector
+  ya = ya - np.mean(ya) # center y axis at zero
+  [y_mesh, x_mesh] = np.meshgrid(ya, xa)
+  x_mesh = x_mesh[np.newaxis, :, :]
+  y_mesh = y_mesh[np.newaxis, :, :]
+
+  # Extract the batch of wavelengths and input thetas.
+  lam_phase_test = params['lam0'][:, 0, 0, 0, 0, 0]
+  lam_phase_test = lam_phase_test[:, tf.newaxis, tf.newaxis]
+  theta_phase_test = params['theta'][:, 0, 0, 0, 0, 0]
+  theta_phase_test = theta_phase_test[:, tf.newaxis, tf.newaxis]
+
+  # Apply a linear phase ramp based on the wavelength and thetas.
+  '''
+  if params['enable_graphmode']:
+     # For graph mode, replaced np.sin() with tf.math.sin(), as passing tensors to numpy functions
+     # is disallowed by tf.function decorator
+     phase_def = 2 * np.pi * tf.math.sin(theta_phase_test) * x_mesh / lam_phase_test
+    
+  else:
+  '''
+  phase_def = 2 * np.pi * np.sin(theta_phase_test) * x_mesh / lam_phase_test
+  
+  phase_def = tf.cast(phase_def, dtype = tf.complex64)
+
+  return tf.exp(1j * phase_def)
+
+
+def simulate(ER_t, UR_t, params = initialize_params()):
+  '''
+    Calculates the transmission/reflection coefficients for a unit cell with a
+    given permittivity/permeability distribution and the batch of input conditions 
+    (e.g., wavelengths, wavevectors, polarizations) for a fixed real space grid
+    and number of Fourier harmonics.
+
+    Args:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `tf.complex64` specifying the relative permittivity distribution
+        of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `tf.complex64` specifying the relative permeability distribution
+        of the unit cell.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        outputs: A `dict` containing the keys {'rx', 'ry', 'rz', 'R', 'ref', 
+        'tx', 'ty', 'tz', 'T', 'TRN'} corresponding to the computed reflection/tranmission
+        coefficients and powers.
+  '''
+
+  # Extract commonly used parameters from the `params` dictionary.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  Nlay = params['Nlay']
+  PQ = params['PQ']
+
+  ### Step 1: Build convolution matrices for the permittivity and permeability ###
+  ERC = rcwa_utils.convmat(ER_t, PQ[0], PQ[1])
+  URC = rcwa_utils.convmat(UR_t, PQ[0], PQ[1])
+
+  ### Step 2: Wave vector expansion ###
+  I = np.eye(np.prod(PQ), dtype = complex)
+  I = tf.convert_to_tensor(I, dtype = tf.complex64)
+  I = I[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  I = tf.tile(I, multiples = (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+  
+  Z = np.zeros((np.prod(PQ), np.prod(PQ)), dtype = complex)
+  Z = tf.convert_to_tensor(Z, dtype = tf.complex64)
+  Z = Z[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  Z = tf.tile(Z, multiples = (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+    
+  n1 = np.sqrt(params['er1'])
+  n2 = np.sqrt(params['er2'])
+
+  k0 = tf.cast(2 * np.pi / params['lam0'], dtype = tf.complex64)
+  kinc_x0 = tf.cast(n1 * tf.sin(params['theta']) * tf.cos(params['phi']), dtype = tf.complex64)
+  kinc_y0 = tf.cast(n1 * tf.sin(params['theta']) * tf.sin(params['phi']), dtype = tf.complex64)
+  kinc_z0 = tf.cast(n1 * tf.cos(params['theta']), dtype = tf.complex64)
+  kinc_z0 = kinc_z0[:, :, :, 0, :, :]
+
+  # Unit vectors
+  T1 = np.transpose([2 * np.pi / params['Lx'], 0])
+  T2 = np.transpose([0, 2 * np.pi / params['Ly']])
+  p_max = tf.math.floordiv(PQ[0], 2)
+  q_max = tf.math.floordiv(PQ[1], 2)
+  p = tf.constant(np.linspace(-p_max, p_max, PQ[0]), dtype = tf.complex64) # indices along T1
+  p = p[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, tf.newaxis]
+  p = tf.tile(p, multiples = (1, pixelsX, pixelsY, Nlay, 1, 1))
+  q = tf.constant(np.linspace(-q_max, q_max, PQ[1]), dtype = tf.complex64) # indices along T2
+  q = q[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  q = tf.tile(q, multiples = (1, pixelsX, pixelsY, Nlay, 1, 1))
+
+  # Build Kx and Ky matrices
+  kx_zeros = tf.zeros(PQ[1], dtype = tf.complex64)
+  kx_zeros = kx_zeros[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  ky_zeros = tf.zeros(PQ[0], dtype = tf.complex64)
+  ky_zeros = ky_zeros[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, tf.newaxis]
+  kx = kinc_x0 - 2 * np.pi * p / (k0 * params['Lx']) - kx_zeros
+  ky = kinc_y0 - 2 * np.pi * q / (k0 * params['Ly']) - ky_zeros
+
+  kx_T = tf.transpose(kx, perm = [0, 1, 2, 3, 5, 4])
+  KX = tf.reshape(kx_T, shape = (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KX = tf.linalg.diag(KX)
+
+  ky_T = tf.transpose(ky, perm = [0, 1, 2, 3, 5, 4])
+  KY = tf.reshape(ky_T, shape = (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KY = tf.linalg.diag(KY)
+
+  KZref = tf.linalg.matmul(tf.math.conj(params['ur1'] * I), tf.math.conj(params['er1'] * I))
+  KZref = KZref - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZref = tf.math.sqrt(KZref)
+  KZref = -tf.math.conj(KZref)
+
+  KZtrn = tf.linalg.matmul(tf.math.conj(params['ur2'] * I), tf.math.conj(params['er2'] * I))
+  KZtrn = KZtrn - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZtrn = tf.math.sqrt(KZtrn)
+  KZtrn = tf.math.conj(KZtrn)
+
+  ### Step 3: Free Space ###
+  KZ = I - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZ = tf.math.sqrt(KZ)
+  KZ = tf.math.conj(KZ)
+
+  Q_free_00 = tf.linalg.matmul(KX, KY)
+  Q_free_01 = I - tf.linalg.matmul(KX, KX)
+  Q_free_10 = tf.linalg.matmul(KY, KY) - I
+  Q_free_11 = -tf.linalg.matmul(KY, KX)
+  Q_free_row0 = tf.concat([Q_free_00, Q_free_01], axis = 5)
+  Q_free_row1 = tf.concat([Q_free_10, Q_free_11], axis = 5)
+  Q_free = tf.concat([Q_free_row0, Q_free_row1], axis = 4)
+
+  W0_row0 = tf.concat([I, Z], axis = 5)
+  W0_row1 = tf.concat([Z, I], axis = 5)
+  W0 = tf.concat([W0_row0, W0_row1], axis = 4)
+
+  LAM_free_row0 = tf.concat([1j * KZ, Z], axis = 5)
+  LAM_free_row1 = tf.concat([Z, 1j * KZ], axis = 5)
+  LAM_free = tf.concat([LAM_free_row0, LAM_free_row1], axis = 4)
+
+  V0 = tf.linalg.matmul(Q_free, tf.linalg.inv(LAM_free))
+
+  ### Step 4: Initialize Global Scattering Matrix ###
+  SG = dict({})
+  SG_S11 = tf.zeros(shape = (2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = tf.complex64)
+  SG['S11'] = tensor_utils.expand_and_tile_tf(SG_S11, batchSize, pixelsX, pixelsY)
+
+  SG_S12 = tf.eye(num_rows = 2 * np.prod(PQ), dtype = tf.complex64)
+  SG['S12'] = tensor_utils.expand_and_tile_tf(SG_S12, batchSize, pixelsX, pixelsY)
+
+  SG_S21 = tf.eye(num_rows = 2 * np.prod(PQ), dtype = tf.complex64)
+  SG['S21'] = tensor_utils.expand_and_tile_tf(SG_S21, batchSize, pixelsX, pixelsY)
+
+  SG_S22 = tf.zeros(shape = (2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = tf.complex64)
+  SG['S22'] = tensor_utils.expand_and_tile_tf(SG_S22, batchSize, pixelsX, pixelsY)
+
+  ### Step 5: Calculate eigenmodes ###
+
+  # Build the eigenvalue problem.
+  P_00 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_00 = tf.linalg.matmul(P_00, KY)
+
+  P_01 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_01 = tf.linalg.matmul(P_01, KX)
+  P_01 = URC - P_01
+
+  P_10 = tf.linalg.matmul(KY, tf.linalg.inv(ERC))
+  P_10 = tf.linalg.matmul(P_10, KY) - URC
+
+  P_11 = tf.linalg.matmul(-KY, tf.linalg.inv(ERC))
+  P_11 = tf.linalg.matmul(P_11, KX)
+
+  P_row0 = tf.concat([P_00, P_01], axis = 5)
+  P_row1 = tf.concat([P_10, P_11], axis = 5)
+  P = tf.concat([P_row0, P_row1], axis = 4)
+
+  Q_00 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_00 = tf.linalg.matmul(Q_00, KY)
+
+  Q_01 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_01 = tf.linalg.matmul(Q_01, KX)
+  Q_01 = ERC - Q_01
+
+  Q_10 = tf.linalg.matmul(KY, tf.linalg.inv(URC))
+  Q_10 = tf.linalg.matmul(Q_10, KY) - ERC
+
+  Q_11 = tf.linalg.matmul(-KY, tf.linalg.inv(URC))
+  Q_11 = tf.linalg.matmul(Q_11, KX)
+
+  Q_row0 = tf.concat([Q_00, Q_01], axis = 5)
+  Q_row1 = tf.concat([Q_10, Q_11], axis = 5)
+  Q = tf.concat([Q_row0, Q_row1], axis = 4)
+
+  # Compute eignmodes for the layers in each pixel for the whole batch.
+  OMEGA_SQ = tf.linalg.matmul(P, Q)
+  LAM, W = tensor_utils.eig_general(OMEGA_SQ)
+  LAM = tf.sqrt(LAM)
+  LAM = tf.linalg.diag(LAM)
+
+  V = tf.linalg.matmul(Q, W)
+  V = tf.linalg.matmul(V, tf.linalg.inv(LAM))
+
+  # Scattering matrices for the layers in each pixel for the whole batch.
+  W_inv = tf.linalg.inv(W)
+  V_inv = tf.linalg.inv(V)
+  A = tf.linalg.matmul(W_inv, W0) + tf.linalg.matmul(V_inv, V0)
+  B = tf.linalg.matmul(W_inv, W0) - tf.linalg.matmul(V_inv, V0)
+  X = tf.linalg.expm(-LAM * k0 * params['L'])
+
+  S = dict({})
+  A_inv = tf.linalg.inv(A)
+  S11_left = tf.linalg.matmul(X, B)
+  S11_left = tf.linalg.matmul(S11_left, A_inv)
+  S11_left = tf.linalg.matmul(S11_left, X)
+  S11_left = tf.linalg.matmul(S11_left, B)
+  S11_left = A - S11_left
+  S11_left = tf.linalg.inv(S11_left)
+
+  S11_right = tf.linalg.matmul(X, B)
+  S11_right = tf.linalg.matmul(S11_right, A_inv)
+  S11_right = tf.linalg.matmul(S11_right, X)
+  S11_right = tf.linalg.matmul(S11_right, A)
+  S11_right = S11_right - B
+  S['S11'] = tf.linalg.matmul(S11_left, S11_right)
+
+  S12_right = tf.linalg.matmul(B, A_inv)
+  S12_right = tf.linalg.matmul(S12_right, B)
+  S12_right = A - S12_right
+  S12_left = tf.linalg.matmul(S11_left, X)
+  S['S12'] = tf.linalg.matmul(S12_left, S12_right)
+
+  S['S21'] = S['S12']
+  S['S22'] = S['S11']
+
+  # Update the global scattering matrices.
+  for l in range(Nlay):
+    S_layer = dict({})
+    S_layer['S11'] = S['S11'][:, :, :, l, :, :]
+    S_layer['S12'] = S['S12'][:, :, :, l, :, :]
+    S_layer['S21'] = S['S21'][:, :, :, l, :, :]
+    S_layer['S22'] = S['S22'][:, :, :, l, :, :]
+    SG = rcwa_utils.redheffer_star_product(SG, S_layer)
+
+  ### Step 6: Reflection side ###
+  # Eliminate layer dimension for tensors as they are unchanging on this dimension.
+  KX = KX[:, :, :, 0, :, :]
+  KY = KY[:, :, :, 0, :, :]
+  KZref = KZref[:, :, :, 0, :, :]
+  KZtrn = KZtrn[:, :, :, 0, :, :]
+  Z = Z[:, :, :, 0, :, :]
+  I = I[:, :, :, 0, :, :]
+  W0 = W0[:, :, :, 0, :, :]
+  V0 = V0[:, :, :, 0, :, :]
+
+  Q_ref_00 = tf.linalg.matmul(KX, KY)
+  Q_ref_01 = params['ur1'] * params['er1'] * I - tf.linalg.matmul(KX, KX)
+  Q_ref_10 = tf.linalg.matmul(KY, KY) - params['ur1'] * params['er1'] * I
+  Q_ref_11 = -tf.linalg.matmul(KY, KX)
+  Q_ref_row0 = tf.concat([Q_ref_00, Q_ref_01], axis = 4)
+  Q_ref_row1 = tf.concat([Q_ref_10, Q_ref_11], axis = 4)
+  Q_ref = tf.concat([Q_ref_row0, Q_ref_row1], axis = 3)
+
+  W_ref_row0 = tf.concat([I, Z], axis = 4)
+  W_ref_row1 = tf.concat([Z, I], axis = 4)
+  W_ref = tf.concat([W_ref_row0, W_ref_row1], axis = 3)
+
+  LAM_ref_row0 = tf.concat([-1j * KZref, Z], axis = 4)
+  LAM_ref_row1 = tf.concat([Z, -1j * KZref], axis = 4)
+  LAM_ref = tf.concat([LAM_ref_row0, LAM_ref_row1], axis = 3)
+
+  V_ref = tf.linalg.matmul(Q_ref, tf.linalg.inv(LAM_ref))
+
+  W0_inv = tf.linalg.inv(W0)
+  V0_inv = tf.linalg.inv(V0)
+  A_ref = tf.linalg.matmul(W0_inv, W_ref) + tf.linalg.matmul(V0_inv, V_ref)
+  A_ref_inv = tf.linalg.inv(A_ref)
+  B_ref = tf.linalg.matmul(W0_inv, W_ref) - tf.linalg.matmul(V0_inv, V_ref)
+
+  SR = dict({})
+  SR['S11'] = tf.linalg.matmul(-A_ref_inv, B_ref)
+  SR['S12'] = 2 * A_ref_inv
+  SR_S21 = tf.linalg.matmul(B_ref, A_ref_inv)
+  SR_S21 = tf.linalg.matmul(SR_S21, B_ref)
+  SR['S21'] = 0.5 * (A_ref - SR_S21)
+  SR['S22'] = tf.linalg.matmul(B_ref, A_ref_inv)
+
+  ### Step 7: Transmission side ###
+  Q_trn_00 = tf.linalg.matmul(KX, KY)
+  Q_trn_01 = params['ur2'] * params['er2'] * I - tf.linalg.matmul(KX, KX)
+  Q_trn_10 = tf.linalg.matmul(KY, KY) - params['ur2'] * params['er2'] * I
+  Q_trn_11 = -tf.linalg.matmul(KY, KX)
+  Q_trn_row0 = tf.concat([Q_trn_00, Q_trn_01], axis = 4)
+  Q_trn_row1 = tf.concat([Q_trn_10, Q_trn_11], axis = 4)
+  Q_trn = tf.concat([Q_trn_row0, Q_trn_row1], axis = 3)
+
+  W_trn_row0 = tf.concat([I, Z], axis = 4)
+  W_trn_row1 = tf.concat([Z, I], axis = 4)
+  W_trn = tf.concat([W_trn_row0, W_trn_row1], axis = 3)
+
+  LAM_trn_row0 = tf.concat([1j * KZtrn, Z], axis = 4)
+  LAM_trn_row1 = tf.concat([Z, 1j * KZtrn], axis = 4)
+  LAM_trn = tf.concat([LAM_trn_row0, LAM_trn_row1], axis = 3)
+
+  V_trn = tf.linalg.matmul(Q_trn, tf.linalg.inv(LAM_trn))
+
+  W0_inv = tf.linalg.inv(W0)
+  V0_inv = tf.linalg.inv(V0)
+  A_trn = tf.linalg.matmul(W0_inv, W_trn) + tf.linalg.matmul(V0_inv, V_trn)
+  A_trn_inv = tf.linalg.inv(A_trn)
+  B_trn = tf.linalg.matmul(W0_inv, W_trn) - tf.linalg.matmul(V0_inv, V_trn)
+
+  ST = dict({})
+  ST['S11'] = tf.linalg.matmul(B_trn, A_trn_inv)
+  ST_S12 = tf.linalg.matmul(B_trn, A_trn_inv)
+  ST_S12 = tf.linalg.matmul(ST_S12, B_trn)
+  ST['S12'] = 0.5 * (A_trn - ST_S12)
+  ST['S21'] = 2 * A_trn_inv
+  ST['S22'] = tf.linalg.matmul(-A_trn_inv, B_trn)
+
+  ### Step 8: Compute global scattering matrix ###
+  SG = rcwa_utils.redheffer_star_product(SR, SG)
+  SG = rcwa_utils.redheffer_star_product(SG, ST)
+
+  ### Step 9: Compute source parameters ###
+
+  # Compute mode coefficients of the source.
+  delta = np.zeros((batchSize, pixelsX, pixelsY, np.prod(PQ)))
+  delta[:, :, :, int(np.prod(PQ) / 2.0)] = 1
+
+  # Incident wavevector.
+  kinc_x0_pol = tf.math.real(kinc_x0[:, :, :, 0, 0])
+  kinc_y0_pol = tf.math.real(kinc_y0[:, :, :, 0, 0])
+  kinc_z0_pol = tf.math.real(kinc_z0[:, :, :, 0])
+  kinc_pol = tf.concat([kinc_x0_pol, kinc_y0_pol, kinc_z0_pol], axis = 3)
+
+  # Calculate TE and TM polarization unit vectors.
+  firstPol = True
+  for pol in range(batchSize):
+    if (kinc_pol[pol, 0, 0, 0] == 0.0 and kinc_pol[pol, 0, 0, 1] == 0.0):
+      ate_pol = np.zeros((1, pixelsX, pixelsY, 3))
+      ate_pol[:, :, :, 1] = 1
+      ate_pol = tf.convert_to_tensor(ate_pol, dtype = tf.float32)
+    else:
+      # Calculation of `ate` for oblique incidence.
+      n_hat = np.zeros((1, pixelsX, pixelsY, 3))
+      n_hat[:, :, :, 0] = 1
+      n_hat = tf.convert_to_tensor(n_hat, dtype = tf.float32)
+      kinc_pol_iter = kinc_pol[pol, :, :, :]
+      kinc_pol_iter = kinc_pol_iter[tf.newaxis, :, :, :]
+      ate_cross = tf.linalg.cross(n_hat, kinc_pol_iter)
+      ate_pol =  ate_cross / tf.norm(ate_cross, axis = 3, keepdims = True)
+
+    if firstPol:
+      ate = ate_pol
+      firstPol = False
+    else:
+      ate = tf.concat([ate, ate_pol], axis = 0)
+
+  atm_cross = tf.linalg.cross(kinc_pol, ate)
+  atm = atm_cross / tf.norm(atm_cross, axis = 3, keepdims = True)
+  ate = tf.cast(ate, dtype = tf.complex64)
+  atm = tf.cast(atm, dtype = tf.complex64)
+
+  # Decompose the TE and TM polarization into x and y components.
+  EP = params['pte'] * ate + params['ptm'] * atm
+  EP_x = EP[:, :, :, 0]
+  EP_x = EP_x[:, :, :, tf.newaxis]
+  EP_y = EP[:, :, :, 1]
+  EP_y = EP_y[:, :, :, tf.newaxis]
+
+  esrc_x = EP_x * delta
+  esrc_y = EP_y * delta
+  esrc = tf.concat([esrc_x, esrc_y], axis = 3)
+  esrc = esrc[:, :, :, :, tf.newaxis]
+
+  W_ref_inv = tf.linalg.inv(W_ref)
+
+  ### Step 10: Compute reflected and transmitted fields ###
+  csrc = tf.linalg.matmul(W_ref_inv, esrc)
+
+  # Compute tranmission and reflection mode coefficients.
+  cref = tf.linalg.matmul(SG['S11'], csrc)
+  ctrn = tf.linalg.matmul(SG['S21'], csrc)
+  eref = tf.linalg.matmul(W_ref, cref)
+  etrn = tf.linalg.matmul(W_trn, ctrn)
+
+  rx = eref[:, :, :, 0 : np.prod(PQ), :]
+  ry = eref[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+  tx = etrn[:, :, :, 0 : np.prod(PQ), :]
+  ty = etrn[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+
+  # Compute longitudinal components.
+  KZref_inv = tf.linalg.inv(KZref)
+  KZtrn_inv = tf.linalg.inv(KZtrn)
+  rz = tf.linalg.matmul(KX, rx) + tf.linalg.matmul(KY, ry)
+  rz = tf.linalg.matmul(-KZref_inv, rz)
+  tz = tf.linalg.matmul(KX, tx) + tf.linalg.matmul(KY, ty)
+  tz = tf.linalg.matmul(-KZtrn_inv, tz)
+
+  ### Step 11: Compute diffraction efficiences ###
+  rx2 = tf.math.real(rx) ** 2 + tf.math.imag(rx) ** 2
+  ry2 = tf.math.real(ry) ** 2 + tf.math.imag(ry) ** 2
+  rz2 = tf.math.real(rz) ** 2 + tf.math.imag(rz) ** 2
+  R2 = rx2 + ry2 + rz2
+  R = tf.math.real(-KZref / params['ur1']) / tf.math.real(kinc_z0 / params['ur1'])
+  R = tf.linalg.matmul(R, R2)
+  R = tf.reshape(R, shape = (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  REF = tf.math.reduce_sum(R, axis = [3, 4])
+
+  tx2 = tf.math.real(tx) ** 2 + tf.math.imag(tx) ** 2
+  ty2 = tf.math.real(ty) ** 2 + tf.math.imag(ty) ** 2
+  tz2 = tf.math.real(tz) ** 2 + tf.math.imag(tz) ** 2
+  T2 = tx2 + ty2 + tz2
+  T = tf.math.real(KZtrn / params['ur2']) / tf.math.real(kinc_z0 / params['ur2'])
+  T = tf.linalg.matmul(T, T2)
+  T = tf.reshape(T, shape = (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  TRN = tf.math.reduce_sum(T, axis = [3, 4])
+
+  # Store the transmission/reflection coefficients and powers in a dictionary.
+  outputs = dict({})
+  outputs['rx'] = rx
+  outputs['ry'] = ry
+  outputs['rz'] = rz
+  outputs['R'] = R
+  outputs['REF'] = REF
+  outputs['tx'] = tx
+  outputs['ty'] = ty
+  outputs['tz'] = tz
+  outputs['T'] = T
+  outputs['TRN'] = TRN
+
+  return outputs
+
+
+def solver_step1(ER_t, UR_t, PQ_f):
+
+  ERC = rcwa_utils.convmat(ER_t, int(PQ_f[0]), int(PQ_f[1]))
+  URC = rcwa_utils.convmat(UR_t, int(PQ_f[0]), int(PQ_f[1]))
+  
+  return ERC, URC
+
+solver_step1_recompute = tf.recompute_grad(solver_step1)
+
+
+def solver_step2(batchSize_f, pixelsX_f, pixelsY_f, Nlay_f, Lx, Ly,
+                        theta, phi, lam0, er1, er2, ur1, ur2, PQ_f):
+  
+  # Convert int params back to correct type.
+  PQ = tf.cast(PQ_f, dtype=tf.int16).numpy()
+  batchSize = int(batchSize_f)
+  pixelsX = int(pixelsX_f)
+  pixelsY = int(pixelsY_f)
+  Nlay = int(Nlay_f)
+  
+  I = np.eye(np.prod(PQ), dtype = complex)
+  I = tf.convert_to_tensor(I, dtype = tf.complex64)
+  I = I[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  I = tf.tile(I, multiples = (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+  
+  Z = np.zeros((np.prod(PQ), np.prod(PQ)), dtype = complex)
+  Z = tf.convert_to_tensor(Z, dtype = tf.complex64)
+  Z = Z[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  Z = tf.tile(Z, multiples = (batchSize, pixelsX, pixelsY, Nlay, 1, 1))
+    
+  n1 = np.sqrt(er1)
+  n2 = np.sqrt(er2)
+
+  k0 = tf.cast(2 * np.pi / lam0, dtype = tf.complex64)
+  kinc_x0 = tf.cast(n1 * tf.sin(theta) * tf.cos(phi), dtype = tf.complex64)
+  kinc_y0 = tf.cast(n1 * tf.sin(theta) * tf.sin(phi), dtype = tf.complex64)
+  kinc_z0 = tf.cast(n1 * tf.cos(theta), dtype = tf.complex64)
+  kinc_z0 = kinc_z0[:, :, :, 0, :, :]
+
+  # Unit vectors
+  T1 = np.transpose([2 * np.pi / Lx, 0])
+  T2 = np.transpose([0, 2 * np.pi / Ly])
+  p_max = np.floor(PQ[0] / 2.0)
+  q_max = np.floor(PQ[1] / 2.0)
+  p = tf.constant(np.linspace(-p_max, p_max, PQ[0]), dtype = tf.complex64) # indices along T1
+  p = p[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, tf.newaxis]
+  p = tf.tile(p, multiples = (1, pixelsX, pixelsY, Nlay, 1, 1))
+  q = tf.constant(np.linspace(-q_max, q_max, PQ[1]), dtype = tf.complex64) # indices along T2
+  q = q[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  q = tf.tile(q, multiples = (1, pixelsX, pixelsY, Nlay, 1, 1))
+
+  # Build Kx and Ky matrices
+  kx_zeros = tf.zeros(PQ[1], dtype = tf.complex64)
+  kx_zeros = kx_zeros[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :]
+  ky_zeros = tf.zeros(PQ[0], dtype = tf.complex64)
+  ky_zeros = ky_zeros[tf.newaxis, tf.newaxis, tf.newaxis, tf.newaxis, :, tf.newaxis]
+  kx = kinc_x0 - 2 * np.pi * p / (k0 * Lx) - kx_zeros
+  ky = kinc_y0 - 2 * np.pi * q / (k0 * Ly) - ky_zeros
+
+  kx_T = tf.transpose(kx, perm = [0, 1, 2, 3, 5, 4])
+  KX = tf.reshape(kx_T, shape = (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KX = tf.linalg.diag(KX)
+
+  ky_T = tf.transpose(ky, perm = [0, 1, 2, 3, 5, 4])
+  KY = tf.reshape(ky_T, shape = (batchSize, pixelsX, pixelsY, Nlay, np.prod(PQ)))
+  KY = tf.linalg.diag(KY)
+
+  KZref = tf.linalg.matmul(tf.math.conj(ur1 * I), tf.math.conj(er1 * I))
+  KZref = KZref - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZref = tf.math.sqrt(KZref)
+  KZref = -tf.math.conj(KZref)
+
+  KZtrn = tf.linalg.matmul(tf.math.conj(ur2 * I), tf.math.conj(er2 * I))
+  KZtrn = KZtrn - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZtrn = tf.math.sqrt(KZtrn)
+  KZtrn = tf.math.conj(KZtrn)
+  
+  return I, Z, k0, kinc_x0, kinc_y0, kinc_z0, KX, KY, KZref, KZtrn
+
+solver_step2_recompute = tf.recompute_grad(solver_step2)
+
+
+def solver_step3(I, Z, KX, KY):
+
+  KZ = I - tf.linalg.matmul(KX, KX) - tf.linalg.matmul(KY, KY)
+  KZ = tf.math.sqrt(KZ)
+  KZ = tf.math.conj(KZ)
+
+  Q_free_00 = tf.linalg.matmul(KX, KY)
+  Q_free_01 = I - tf.linalg.matmul(KX, KX)
+  Q_free_10 = tf.linalg.matmul(KY, KY) - I
+  Q_free_11 = -tf.linalg.matmul(KY, KX)
+  Q_free_row0 = tf.concat([Q_free_00, Q_free_01], axis = 5)
+  Q_free_row1 = tf.concat([Q_free_10, Q_free_11], axis = 5)
+  Q_free = tf.concat([Q_free_row0, Q_free_row1], axis = 4)
+
+  W0_row0 = tf.concat([I, Z], axis = 5)
+  W0_row1 = tf.concat([Z, I], axis = 5)
+  W0 = tf.concat([W0_row0, W0_row1], axis = 4)
+
+  LAM_free_row0 = tf.concat([1j * KZ, Z], axis = 5)
+  LAM_free_row1 = tf.concat([Z, 1j * KZ], axis = 5)
+  LAM_free = tf.concat([LAM_free_row0, LAM_free_row1], axis = 4)
+
+  V0 = tf.linalg.matmul(Q_free, tf.linalg.inv(LAM_free))
+
+  return V0, W0
+
+solver_step3_recompute = tf.recompute_grad(solver_step3)
+
+
+def solver_step4(batchSize_f, pixelsX_f, pixelsY_f, PQ_f):
+
+  # Convert int params back to correct type.
+  PQ = tf.cast(PQ_f, dtype=tf.int16).numpy()
+  batchSize = int(batchSize_f)
+  pixelsX = int(pixelsX_f)
+  pixelsY = int(pixelsY_f)
+  
+  SG = dict({})
+  SG_S11 = tf.zeros(shape = (2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = tf.complex64)
+  SG['S11'] = tensor_utils.expand_and_tile_tf(SG_S11, batchSize, pixelsX, pixelsY)
+
+  SG_S12 = tf.eye(num_rows = 2 * np.prod(PQ), dtype = tf.complex64)
+  SG['S12'] = tensor_utils.expand_and_tile_tf(SG_S12, batchSize, pixelsX, pixelsY)
+
+  SG_S21 = tf.eye(num_rows = 2 * np.prod(PQ), dtype = tf.complex64)
+  SG['S21'] = tensor_utils.expand_and_tile_tf(SG_S21, batchSize, pixelsX, pixelsY)
+
+  SG_S22 = tf.zeros(shape = (2 * np.prod(PQ), 2 * np.prod(PQ)), dtype = tf.complex64)
+  SG['S22'] = tensor_utils.expand_and_tile_tf(SG_S22, batchSize, pixelsX, pixelsY)
+
+  return SG
+
+solver_step4_recompute = tf.recompute_grad(solver_step4)
+
+
+def solver_step5(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG):
+
+  # Convert int params back to correct type.
+  Nlay = int(Nlay_f)
+    
+  # Build the eigenvalue problem.
+  P_00 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_00 = tf.linalg.matmul(P_00, KY)
+
+  P_01 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_01 = tf.linalg.matmul(P_01, KX)
+  P_01 = URC - P_01
+
+  P_10 = tf.linalg.matmul(KY, tf.linalg.inv(ERC))
+  P_10 = tf.linalg.matmul(P_10, KY) - URC
+
+  P_11 = tf.linalg.matmul(-KY, tf.linalg.inv(ERC))
+  P_11 = tf.linalg.matmul(P_11, KX)
+
+  P_row0 = tf.concat([P_00, P_01], axis = 5)
+  P_row1 = tf.concat([P_10, P_11], axis = 5)
+  P = tf.concat([P_row0, P_row1], axis = 4)
+
+  Q_00 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_00 = tf.linalg.matmul(Q_00, KY)
+
+  Q_01 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_01 = tf.linalg.matmul(Q_01, KX)
+  Q_01 = ERC - Q_01
+
+  Q_10 = tf.linalg.matmul(KY, tf.linalg.inv(URC))
+  Q_10 = tf.linalg.matmul(Q_10, KY) - ERC
+
+  Q_11 = tf.linalg.matmul(-KY, tf.linalg.inv(URC))
+  Q_11 = tf.linalg.matmul(Q_11, KX)
+
+  Q_row0 = tf.concat([Q_00, Q_01], axis = 5)
+  Q_row1 = tf.concat([Q_10, Q_11], axis = 5)
+  Q = tf.concat([Q_row0, Q_row1], axis = 4)
+
+  # Compute eignmodes for the layers in each pixel for the whole batch.
+  OMEGA_SQ = tf.linalg.matmul(P, Q)
+  LAM, W = tensor_utils.eig_general(OMEGA_SQ)
+  LAM = tf.sqrt(LAM)
+  LAM = tf.linalg.diag(LAM)
+
+  V = tf.linalg.matmul(Q, W)
+  V = tf.linalg.matmul(V, tf.linalg.inv(LAM))
+
+  # Scattering matrices for the layers in each pixel for the whole batch.
+  W_inv = tf.linalg.inv(W)
+  V_inv = tf.linalg.inv(V)
+  A = tf.linalg.matmul(W_inv, W0) + tf.linalg.matmul(V_inv, V0)
+  B = tf.linalg.matmul(W_inv, W0) - tf.linalg.matmul(V_inv, V0)
+  X = tf.linalg.expm(-LAM * k0 * L)
+
+  S = dict({})
+  A_inv = tf.linalg.inv(A)
+  S11_left = tf.linalg.matmul(X, B)
+  S11_left = tf.linalg.matmul(S11_left, A_inv)
+  S11_left = tf.linalg.matmul(S11_left, X)
+  S11_left = tf.linalg.matmul(S11_left, B)
+  S11_left = A - S11_left
+  S11_left = tf.linalg.inv(S11_left)
+
+  S11_right = tf.linalg.matmul(X, B)
+  S11_right = tf.linalg.matmul(S11_right, A_inv)
+  S11_right = tf.linalg.matmul(S11_right, X)
+  S11_right = tf.linalg.matmul(S11_right, A)
+  S11_right = S11_right - B
+  S['S11'] = tf.linalg.matmul(S11_left, S11_right)
+
+  S12_right = tf.linalg.matmul(B, A_inv)
+  S12_right = tf.linalg.matmul(S12_right, B)
+  S12_right = A - S12_right
+  S12_left = tf.linalg.matmul(S11_left, X)
+  S['S12'] = tf.linalg.matmul(S12_left, S12_right)
+
+  S['S21'] = S['S12']
+  S['S22'] = S['S11']
+
+  # Update the global scattering matrices.
+  for l in range(Nlay):
+    S_layer = dict({})
+    S_layer['S11'] = S['S11'][:, :, :, l, :, :]
+    S_layer['S12'] = S['S12'][:, :, :, l, :, :]
+    S_layer['S21'] = S['S21'][:, :, :, l, :, :]
+    S_layer['S22'] = S['S22'][:, :, :, l, :, :]
+    SG = rcwa_utils.redheffer_star_product(SG, S_layer)
+
+  return S, SG
+
+def solver_step5p1(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG):
+
+  # Convert int params back to correct type.
+  Nlay = int(Nlay_f)
+
+  #print(ERC)
+    
+  # Build the eigenvalue problem.
+  P_00 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_00 = tf.linalg.matmul(P_00, KY)
+
+  P_01 = tf.linalg.matmul(KX, tf.linalg.inv(ERC))
+  P_01 = tf.linalg.matmul(P_01, KX)
+  P_01 = URC - P_01
+
+  P_10 = tf.linalg.matmul(KY, tf.linalg.inv(ERC))
+  P_10 = tf.linalg.matmul(P_10, KY) - URC
+
+  P_11 = tf.linalg.matmul(-KY, tf.linalg.inv(ERC))
+  P_11 = tf.linalg.matmul(P_11, KX)
+
+  P_row0 = tf.concat([P_00, P_01], axis = 5)
+  P_row1 = tf.concat([P_10, P_11], axis = 5)
+  P = tf.concat([P_row0, P_row1], axis = 4)
+
+  Q_00 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_00 = tf.linalg.matmul(Q_00, KY)
+
+  Q_01 = tf.linalg.matmul(KX, tf.linalg.inv(URC))
+  Q_01 = tf.linalg.matmul(Q_01, KX)
+  Q_01 = ERC - Q_01
+
+  Q_10 = tf.linalg.matmul(KY, tf.linalg.inv(URC))
+  Q_10 = tf.linalg.matmul(Q_10, KY) - ERC
+
+  Q_11 = tf.linalg.matmul(-KY, tf.linalg.inv(URC))
+  Q_11 = tf.linalg.matmul(Q_11, KX)
+
+  Q_row0 = tf.concat([Q_00, Q_01], axis = 5)
+  Q_row1 = tf.concat([Q_10, Q_11], axis = 5)
+  Q = tf.concat([Q_row0, Q_row1], axis = 4)
+    
+  # Compute eignmodes for the layers in each pixel for the whole batch.
+  OMEGA_SQ = tf.linalg.matmul(P, Q)
+  LAM, W = tensor_utils.eig_general(OMEGA_SQ)
+  LAM = tf.sqrt(LAM)
+  LAM = tf.linalg.diag(LAM)
+
+  return P, Q, LAM, W
+
+def solver_step5p2(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, P, Q, LAM, W):
+
+  # Convert int params back to correct type.
+  Nlay = int(Nlay_f)
+
+  V = tf.linalg.matmul(Q, W)
+  V = tf.linalg.matmul(V, tf.linalg.inv(LAM))
+
+  # Scattering matrices for the layers in each pixel for the whole batch.
+  W_inv = tf.linalg.inv(W)
+  V_inv = tf.linalg.inv(V)
+  
+  A = tf.linalg.matmul(W_inv, W0) + tf.linalg.matmul(V_inv, V0)   
+  B = tf.linalg.matmul(W_inv, W0) - tf.linalg.matmul(V_inv, V0)
+  
+  return A, B
+
+def solver_step5p3(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, LAM):
+  
+  arg = -LAM * k0 * L
+
+  # This is pixelsX * pixelsY matrix exponentials of size (p^2 * 2)x(q^2 * 2)
+  #X = tf.linalg.expm(arg) # EXPENSIVE CALCULATION
+  
+  X0 = tf.linalg.expm(arg[:,:9,:,:,:])
+
+  return X0
+
+def solver_step5p4(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, LAM, X0):
+  
+  arg = -LAM * k0 * L
+
+  X1 = tf.linalg.expm(arg[:,9:,:,:,:])
+    
+  X = tf.concat([X0, X1], axis=1)
+  
+  return X
+
+def solver_step5p5(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, A, B, X):
+
+  # Convert int params back to correct type.
+  Nlay = int(Nlay_f)
+
+  S = dict({})
+  A_inv = tf.linalg.inv(A)
+  S11_left = tf.linalg.matmul(X, B)
+  S11_left = tf.linalg.matmul(S11_left, A_inv)
+  S11_left = tf.linalg.matmul(S11_left, X)
+  S11_left = tf.linalg.matmul(S11_left, B)
+  S11_left = A - S11_left
+  S11_left = tf.linalg.inv(S11_left)
+
+  S11_right = tf.linalg.matmul(X, B)
+  S11_right = tf.linalg.matmul(S11_right, A_inv)
+  S11_right = tf.linalg.matmul(S11_right, X)
+  S11_right = tf.linalg.matmul(S11_right, A)
+  S11_right = S11_right - B
+  S['S11'] = tf.linalg.matmul(S11_left, S11_right)
+
+  S12_right = tf.linalg.matmul(B, A_inv)
+  S12_right = tf.linalg.matmul(S12_right, B)
+  S12_right = A - S12_right
+  S12_left = tf.linalg.matmul(S11_left, X)
+  S['S12'] = tf.linalg.matmul(S12_left, S12_right)
+
+  S['S21'] = S['S12']
+  S['S22'] = S['S11']
+
+  # Update the global scattering matrices.
+  for l in range(Nlay):
+    S_layer = dict({})
+    S_layer['S11'] = S['S11'][:, :, :, l, :, :]
+    S_layer['S12'] = S['S12'][:, :, :, l, :, :]
+    S_layer['S21'] = S['S21'][:, :, :, l, :, :]
+    S_layer['S22'] = S['S22'][:, :, :, l, :, :]
+    SG = rcwa_utils.redheffer_star_product(SG, S_layer)
+
+  return S, SG
+
+
+solver_step5_recompute = tf.recompute_grad(solver_step5)
+
+solver_step5p1_recompute = tf.recompute_grad(solver_step5p1)
+solver_step5p2_recompute = tf.recompute_grad(solver_step5p2)
+solver_step5p3_recompute = tf.recompute_grad(solver_step5p3)
+solver_step5p4_recompute = tf.recompute_grad(solver_step5p4)
+solver_step5p5_recompute = tf.recompute_grad(solver_step5p5)
+
+
+def solver_step6(I, Z, KX, KY, KZref, KZtrn, W0, V0, er1, ur1):
+
+  # Eliminate layer dimension for tensors as they are unchanging on this dimension.
+  KX = KX[:, :, :, 0, :, :]
+  KY = KY[:, :, :, 0, :, :]
+  KZref = KZref[:, :, :, 0, :, :]
+  KZtrn = KZtrn[:, :, :, 0, :, :]
+  Z = Z[:, :, :, 0, :, :]
+  I = I[:, :, :, 0, :, :]
+  W0 = W0[:, :, :, 0, :, :]
+  V0 = V0[:, :, :, 0, :, :]
+
+  Q_ref_00 = tf.linalg.matmul(KX, KY)
+  Q_ref_01 = ur1 * er1 * I - tf.linalg.matmul(KX, KX)
+  Q_ref_10 = tf.linalg.matmul(KY, KY) - ur1 * er1 * I
+  Q_ref_11 = -tf.linalg.matmul(KY, KX)
+  Q_ref_row0 = tf.concat([Q_ref_00, Q_ref_01], axis = 4)
+  Q_ref_row1 = tf.concat([Q_ref_10, Q_ref_11], axis = 4)
+  Q_ref = tf.concat([Q_ref_row0, Q_ref_row1], axis = 3)
+
+  W_ref_row0 = tf.concat([I, Z], axis = 4)
+  W_ref_row1 = tf.concat([Z, I], axis = 4)
+  W_ref = tf.concat([W_ref_row0, W_ref_row1], axis = 3)
+
+  LAM_ref_row0 = tf.concat([-1j * KZref, Z], axis = 4)
+  LAM_ref_row1 = tf.concat([Z, -1j * KZref], axis = 4)
+  LAM_ref = tf.concat([LAM_ref_row0, LAM_ref_row1], axis = 3)
+
+  V_ref = tf.linalg.matmul(Q_ref, tf.linalg.inv(LAM_ref))
+
+  W0_inv = tf.linalg.inv(W0)
+  V0_inv = tf.linalg.inv(V0)
+  A_ref = tf.linalg.matmul(W0_inv, W_ref) + tf.linalg.matmul(V0_inv, V_ref)
+  A_ref_inv = tf.linalg.inv(A_ref)
+  B_ref = tf.linalg.matmul(W0_inv, W_ref) - tf.linalg.matmul(V0_inv, V_ref)
+
+  SR = dict({})
+  SR['S11'] = tf.linalg.matmul(-A_ref_inv, B_ref)
+  SR['S12'] = 2 * A_ref_inv
+  SR_S21 = tf.linalg.matmul(B_ref, A_ref_inv)
+  SR_S21 = tf.linalg.matmul(SR_S21, B_ref)
+  SR['S21'] = 0.5 * (A_ref - SR_S21)
+  SR['S22'] = tf.linalg.matmul(B_ref, A_ref_inv)
+    
+  return I, Z, KX, KY, KZref, KZtrn, W0, V0, W_ref, SR
+
+solver_step6_recompute = tf.recompute_grad(solver_step6)
+
+
+def solver_step7(I, Z, KX, KY, KZref, KZtrn, W0, V0, er2, ur2):
+  
+  Q_trn_00 = tf.linalg.matmul(KX, KY)
+  Q_trn_01 = ur2 * er2 * I - tf.linalg.matmul(KX, KX)
+  Q_trn_10 = tf.linalg.matmul(KY, KY) - ur2 * er2 * I
+  Q_trn_11 = -tf.linalg.matmul(KY, KX)
+  Q_trn_row0 = tf.concat([Q_trn_00, Q_trn_01], axis = 4)
+  Q_trn_row1 = tf.concat([Q_trn_10, Q_trn_11], axis = 4)
+  Q_trn = tf.concat([Q_trn_row0, Q_trn_row1], axis = 3)
+
+  W_trn_row0 = tf.concat([I, Z], axis = 4)
+  W_trn_row1 = tf.concat([Z, I], axis = 4)
+  W_trn = tf.concat([W_trn_row0, W_trn_row1], axis = 3)
+
+  LAM_trn_row0 = tf.concat([1j * KZtrn, Z], axis = 4)
+  LAM_trn_row1 = tf.concat([Z, 1j * KZtrn], axis = 4)
+  LAM_trn = tf.concat([LAM_trn_row0, LAM_trn_row1], axis = 3)
+
+  V_trn = tf.linalg.matmul(Q_trn, tf.linalg.inv(LAM_trn))
+
+  W0_inv = tf.linalg.inv(W0)
+  V0_inv = tf.linalg.inv(V0)
+  A_trn = tf.linalg.matmul(W0_inv, W_trn) + tf.linalg.matmul(V0_inv, V_trn)
+  A_trn_inv = tf.linalg.inv(A_trn)
+  B_trn = tf.linalg.matmul(W0_inv, W_trn) - tf.linalg.matmul(V0_inv, V_trn)
+
+  ST = dict({})
+  ST['S11'] = tf.linalg.matmul(B_trn, A_trn_inv)
+  ST_S12 = tf.linalg.matmul(B_trn, A_trn_inv)
+  ST_S12 = tf.linalg.matmul(ST_S12, B_trn)
+  ST['S12'] = 0.5 * (A_trn - ST_S12)
+  ST['S21'] = 2 * A_trn_inv
+  ST['S22'] = tf.linalg.matmul(-A_trn_inv, B_trn)
+    
+  return W_trn, ST
+
+solver_step7_recompute = tf.recompute_grad(solver_step7)
+
+
+def solver_step8(SG, SR, ST):
+
+  SG = rcwa_utils.redheffer_star_product(SR, SG)
+  SG = rcwa_utils.redheffer_star_product(SG, ST)
+  
+  return SG
+
+solver_step8_recompute = tf.recompute_grad(solver_step8)
+
+
+def solver_step9(batchSize_f, pixelsX_f, pixelsY_f, PQ_f, pte, ptm, kinc_x0, kinc_y0, kinc_z0, W_ref):
+  
+  # Convert int params back to correct type.
+  PQ = tf.cast(PQ_f, dtype=tf.int16).numpy()
+  batchSize = int(batchSize_f)
+  pixelsX = int(pixelsX_f)
+  pixelsY = int(pixelsY_f)
+    
+  # Compute mode coefficients of the source.
+  delta = np.zeros((batchSize, pixelsX, pixelsY, np.prod(PQ)))
+  delta[:, :, :, int(np.prod(PQ) / 2.0)] = 1
+
+  # Incident wavevector.
+  kinc_x0_pol = tf.math.real(kinc_x0[:, :, :, 0, 0])
+  kinc_y0_pol = tf.math.real(kinc_y0[:, :, :, 0, 0])
+  kinc_z0_pol = tf.math.real(kinc_z0[:, :, :, 0])
+  kinc_pol = tf.concat([kinc_x0_pol, kinc_y0_pol, kinc_z0_pol], axis = 3)
+
+  # Calculate TE and TM polarization unit vectors.
+  firstPol = True
+  for pol in range(batchSize):
+    if (kinc_pol[pol, 0, 0, 0] == 0.0 and kinc_pol[pol, 0, 0, 1] == 0.0):
+      ate_pol = np.zeros((1, pixelsX, pixelsY, 3))
+      ate_pol[:, :, :, 1] = 1
+      ate_pol = tf.convert_to_tensor(ate_pol, dtype = tf.float32)
+    else:
+      # Calculation of `ate` for oblique incidence.
+      n_hat = np.zeros((1, pixelsX, pixelsY, 3))
+      n_hat[:, :, :, 0] = 1
+      n_hat = tf.convert_to_tensor(n_hat, dtype = tf.float32)
+      kinc_pol_iter = kinc_pol[pol, :, :, :]
+      kinc_pol_iter = kinc_pol_iter[tf.newaxis, :, :, :]
+      ate_cross = tf.linalg.cross(n_hat, kinc_pol_iter)
+      ate_pol =  ate_cross / tf.norm(ate_cross, axis = 3, keepdims = True)
+
+    if firstPol:
+      ate = ate_pol
+      firstPol = False
+    else:
+      ate = tf.concat([ate, ate_pol], axis = 0)
+
+  atm_cross = tf.linalg.cross(kinc_pol, ate)
+  atm = atm_cross / tf.norm(atm_cross, axis = 3, keepdims = True)
+  ate = tf.cast(ate, dtype = tf.complex64)
+  atm = tf.cast(atm, dtype = tf.complex64)
+
+  # Decompose the TE and TM polarization into x and y components.
+  EP = pte * ate + ptm * atm
+  EP_x = EP[:, :, :, 0]
+  EP_x = EP_x[:, :, :, tf.newaxis]
+  EP_y = EP[:, :, :, 1]
+  EP_y = EP_y[:, :, :, tf.newaxis]
+
+  esrc_x = EP_x * delta
+  esrc_y = EP_y * delta
+  esrc = tf.concat([esrc_x, esrc_y], axis = 3)
+  esrc = esrc[:, :, :, :, tf.newaxis]
+
+  W_ref_inv = tf.linalg.inv(W_ref)
+ 
+  return W_ref_inv, esrc
+
+solver_step9_recompute = tf.recompute_grad(solver_step9)
+
+
+def solver_step10(PQ_f, KX, KY, KZref, KZtrn, SG, W_ref, W_trn, W_ref_inv, esrc):
+  
+  # Convert int params back to correct type.
+  PQ = tf.cast(PQ_f, dtype=tf.int16).numpy()
+    
+  csrc = tf.linalg.matmul(W_ref_inv, esrc)
+
+  # Compute tranmission and reflection mode coefficients.
+  cref = tf.linalg.matmul(SG['S11'], csrc)
+  ctrn = tf.linalg.matmul(SG['S21'], csrc)
+  eref = tf.linalg.matmul(W_ref, cref)
+  etrn = tf.linalg.matmul(W_trn, ctrn)
+
+  rx = eref[:, :, :, 0 : np.prod(PQ), :]
+  ry = eref[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+  tx = etrn[:, :, :, 0 : np.prod(PQ), :]
+  ty = etrn[:, :, :, np.prod(PQ) : 2 * np.prod(PQ), :]
+
+  # Compute longitudinal components.
+  KZref_inv = tf.linalg.inv(KZref)
+  KZtrn_inv = tf.linalg.inv(KZtrn)
+  rz = tf.linalg.matmul(KX, rx) + tf.linalg.matmul(KY, ry)
+  rz = tf.linalg.matmul(-KZref_inv, rz)
+  tz = tf.linalg.matmul(KX, tx) + tf.linalg.matmul(KY, ty)
+  tz = tf.linalg.matmul(-KZtrn_inv, tz)
+ 
+  return rx, ry, rz, tx, ty, tz
+
+solver_step10_recompute = tf.recompute_grad(solver_step10)
+
+
+def solver_step11(batchSize_f, pixelsX_f, pixelsY_f, PQ_f, kinc_z0, KZref, KZtrn, ur1, ur2, rx, ry, rz, tx, ty, tz):
+  
+  # Convert int params back to correct type.
+  PQ = tf.cast(PQ_f, dtype=tf.int16).numpy()
+  batchSize = int(batchSize_f)
+  pixelsX = int(pixelsX_f)
+  pixelsY = int(pixelsY_f)
+
+  rx2 = tf.math.real(rx) ** 2 + tf.math.imag(rx) ** 2
+  ry2 = tf.math.real(ry) ** 2 + tf.math.imag(ry) ** 2
+  rz2 = tf.math.real(rz) ** 2 + tf.math.imag(rz) ** 2
+  R2 = rx2 + ry2 + rz2
+  R = tf.math.real(-KZref / ur1) / tf.math.real(kinc_z0 / ur1)
+  R = tf.linalg.matmul(R, R2)
+  R = tf.reshape(R, shape = (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  REF = tf.math.reduce_sum(R, axis = [3, 4])
+
+  tx2 = tf.math.real(tx) ** 2 + tf.math.imag(tx) ** 2
+  ty2 = tf.math.real(ty) ** 2 + tf.math.imag(ty) ** 2
+  tz2 = tf.math.real(tz) ** 2 + tf.math.imag(tz) ** 2
+  T2 = tx2 + ty2 + tz2
+  T = tf.math.real(KZtrn / ur2) / tf.math.real(kinc_z0 / ur2)
+  T = tf.linalg.matmul(T, T2)
+  T = tf.reshape(T, shape = (batchSize, pixelsX, pixelsY, PQ[0], PQ[1]))
+  TRN = tf.math.reduce_sum(T, axis = [3, 4])
+  
+  return R, REF, T, TRN
+
+solver_step11_recompute = tf.recompute_grad(solver_step11)
+
+
+def simulate_allsteps(ER_t, UR_t, params):
+  '''
+    Calculates the transmission/reflection coefficients for a unit cell with a
+    given permittivity/permeability distribution and the batch of input conditions 
+    (e.g., wavelengths, wavevectors, polarizations) for a fixed real space grid
+    and number of Fourier harmonics.
+
+    Args:
+        ER_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `tf.complex64` specifying the relative permittivity distribution
+        of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)`
+        and dtype `tf.complex64` specifying the relative permeability distribution
+        of the unit cell.
+
+        params: A `dict` containing simulation and optimization settings.
+    Returns:
+        outputs: A `dict` containing the keys {'rx', 'ry', 'rz', 'R', 'ref', 
+        'tx', 'ty', 'tz', 'T', 'TRN'} corresponding to the computed reflection/tranmission
+        coefficients and powers.
+  '''
+
+  # Extract parameters from the `params` dictionary.
+  batchSize = params['batchSize']
+  pixelsX = params['pixelsX']
+  pixelsY = params['pixelsY']
+  L = params['L']
+  Nlay = params['Nlay']
+  Lx = params['Lx']
+  Ly = params['Ly']
+  theta = params['theta']
+  phi = params['phi']
+  pte = params['pte']
+  ptm = params['ptm']
+  lam0 = params['lam0']
+  er1 = params['er1']
+  er2 = params['er2']
+  ur1 = params['ur1']
+  ur2 = params['ur2']
+  PQ = params['PQ']
+
+  # Convert int parameters to floats, as required by arguments to functions decorated
+  # with tf.recompute_grad.
+  PQ_f = tf.convert_to_tensor(PQ, dtype=tf.float32)
+  batchSize_f = float(batchSize)
+  pixelsX_f = float(pixelsX)
+  pixelsY_f = float(pixelsY)
+  Nlay_f = float(Nlay)
+
+  ### Step 1: Build convolution matrices for the permittivity and permeability ###
+  ERC, URC = solver_step1(ER_t, UR_t, PQ_f)
+  
+  ### Step 2: Wave vector expansion ###
+  I, Z, k0, kinc_x0, kinc_y0, kinc_z0, KX, KY, KZref, KZtrn = solver_step2(
+      batchSize_f, pixelsX_f, pixelsY_f, Nlay_f, Lx, Ly, theta, phi, lam0, er1, er2, ur1, ur2, PQ_f)
+
+  ### Step 3: Free Space ###
+  V0, W0 = solver_step3(I, Z, KX, KY)
+
+  ### Step 4: Initialize Global Scattering Matrix ###
+  SG = solver_step4(batchSize_f, pixelsX_f, pixelsY_f, PQ_f)
+  
+  ### Step 5: Calculate eigenmodes ###
+  #S, SG = solver_step5(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG)
+
+  P, Q, LAM, W = solver_step5p1(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG)
+
+  A, B = solver_step5p2(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, P, Q, LAM, W)
+
+  X0 = solver_step5p3(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, LAM)
+    
+  X = solver_step5p4(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, LAM, X0)
+    
+  S, SG = solver_step5p5(L, Nlay_f, ERC, URC, k0, KX, KY, V0, W0, SG, A, B, X)
+  
+  ### Step 6: Reflection side ###
+  I, Z, KX, KY, KZref, KZtrn, W0, V0, W_ref, SR = solver_step6(I, Z, KX, KY, KZref, KZtrn, W0, V0, er1, ur1)
+  
+  ### Step 7: Transmission side ###
+  W_trn, ST = solver_step7(I, Z, KX, KY, KZref, KZtrn, W0, V0, er2, ur2)
+  
+  ### Step 8: Compute global scattering matrix ###
+  SG = solver_step8(SG, SR, ST)
+  
+  ### Step 9: Compute source parameters ###
+  W_ref_inv, esrc = solver_step9(batchSize_f, pixelsX_f, pixelsY_f, PQ_f, pte, ptm, kinc_x0, kinc_y0, kinc_z0, W_ref)
+  
+  ### Step 10: Compute reflected and transmitted fields ###
+  rx, ry, rz, tx, ty, tz = solver_step10(PQ_f, KX, KY, KZref, KZtrn, SG, W_ref, W_trn, W_ref_inv, esrc)
+  
+  ### Step 11: Compute diffraction efficiences ###
+  R, REF, T, TRN = solver_step11(batchSize_f, pixelsX_f, pixelsY_f, PQ_f, kinc_z0, KZref, KZtrn, ur1, ur2, rx, ry, rz, tx, ty, tz)
+
+  # Store the transmission/reflection coefficients and powers in a dictionary.
+  outputs = dict({})
+  outputs['rx'] = rx
+  outputs['ry'] = ry
+  outputs['rz'] = rz
+  outputs['R'] = R
+  outputs['REF'] = REF
+  outputs['tx'] = tx
+  outputs['ty'] = ty
+  outputs['tz'] = tz
+  outputs['T'] = T
+  outputs['TRN'] = TRN
+
+  return outputs","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/src/tensor_utils.py",".py","5026","132","# Copyright (c) 2020, Shane Colburn, University of Washington
+# This file is part of rcwa_tf
+# Written by Shane Colburn (Email: scolbur2@uw.edu)
+
+import tensorflow as tf
+import numpy as np
+import json
+
+def expand_and_tile_np(array, batchSize, pixelsX, pixelsY):
+  '''
+    Expands and tile a numpy array for a given batchSize and number of pixels.
+    Args:
+        array: A `np.ndarray` of shape `(Nx, Ny)`.
+    Returns:
+        A `np.ndarray` of shape `(batchSize, pixelsX, pixelsY, Nx, Ny)` with
+        the values from `array` tiled over the new dimensions.
+  '''
+  array = array[np.newaxis, np.newaxis, np.newaxis, :, :]
+  return np.tile(array, reps = (batchSize, pixelsX, pixelsY, 1, 1))
+
+def expand_and_tile_tf(tensor, batchSize, pixelsX, pixelsY):
+  '''
+    Expands and tile a `tf.Tensor` for a given batchSize and number of pixels.
+    Args:
+        tensor: A `tf.Tensor` of shape `(Nx, Ny)`.
+    Returns:
+        A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nx, Ny)` with
+        the values from `tensor` tiled over the new dimensions.
+  '''
+  tensor = tensor[tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+  return tf.tile(tensor, multiples = (batchSize, pixelsX, pixelsY, 1, 1))
+
+
+def check_eig_tf(A, e, v, eps=1e-4):
+    l = tf.linalg.matvec(A,v)
+    r = v*e
+    mask = tf.abs(l-r) > eps
+    return (tf.reduce_sum(tf.cast(mask, dtype=tf.uint8)) == 0).numpy()
+
+
+@tf.custom_gradient
+def eig_general(A, eps = 1E-6):
+  '''
+    Computes the eigendecomposition of a batch of matrices, the same as 
+    `tf.eig()` but assumes the input shape also has extra dimensions for pixels
+    and layers. This function also provides the reverse mode gradient of the 
+    eigendecomposition as derived in 10.1109/ICASSP.2017.7952140. This applies 
+    for general, complex matrices that do not have to be self adjoint. This 
+    result gives the exact reverse mode gradient for nondegenerate eigenvalue 
+    problems. To extend to the case of degenerate eigenvalues common in RCWA, we
+    approximate the gradient by a Lorentzian broadening technique that 
+    introduces a small error but stabilizes the calculation. This is based on
+    10.1103/PhysRevX.9.031041.
+    Args:
+        A: A `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, Nlayers, Nx, 
+        Ny)` and dtype `tf.complex64` where the last two dimensions define 
+        matrices for which we will calculate the eigendecomposition of their 
+        reverse mode gradients.
+
+        eps: A `float` defining a regularization parameter used in the 
+        denominator of the Lorentzian broadening calculation to enable reverse
+        mode gradients for degenerate eigenvalues.
+
+    Returns:
+        A `Tuple(List[tf.Tensor, tf.Tensor], tf.Tensor)`, where the `List`
+        specifies the eigendecomposition as computed by `tf.eig()` and the 
+        second element of the `Tuple` gives the reverse mode gradient of the
+        eigendecompostion of the input argument `A`.
+  '''
+
+  # Perform the eigendecomposition.
+  eigenvalues, eigenvectors = tf.eig(A)
+
+  #with open(""tf.txt"", 'w', encoding=""utf-8"") as f: 
+  #  json.dump(tf.math.real(A).numpy().tolist(),f)
+
+  '''
+  batch0 = eigenvectors.shape[0]
+  batch1 = eigenvectors.shape[1]
+  batch2 = eigenvectors.shape[2]
+  batch3 = eigenvectors.shape[3]
+  rows = eigenvectors.shape[-2]
+  cols = eigenvectors.shape[-1]
+  
+  tf_good = []
+  for b0 in range(batch0):
+    for b1 in range(batch1):
+      for b2 in range(batch2):
+        for b3 in range(batch3):
+          for i in range(rows):
+            tf_good.append(check_eig_tf(A[b0,b1,b2,b3,:,:], eigenvalues[b0,b1,b2,b3,i], eigenvectors[b0,b1,b2,b3,:,i]))
+
+  print('Eig Check (True is pass):')
+  print((np.sum(tf_good) - len(tf_good)) == 0)
+  '''
+    
+  # Referse mode gradient calculation.
+  def grad(grad_D, grad_U):
+
+    # Use the pre-computed eigendecomposition.
+    nonlocal eigenvalues, eigenvectors
+    D = eigenvalues
+    U = eigenvectors
+
+    # Convert eigenvalues gradient to a diagonal matrix.
+    grad_D = tf.linalg.diag(grad_D)
+
+    # Extract the tensor dimensions for later use.
+    batchSize, pixelsX, pixelsY, Nlay, dim, _ = A.shape
+
+    # Calculate intermediate matrices.
+    I = tf.eye(num_rows = dim, dtype = tf.complex64)
+    D = tf.reshape(D, shape = (batchSize, pixelsX, pixelsY, Nlay, dim, 1))
+    shape_di = (batchSize, pixelsX, pixelsY, Nlay, dim, 1)
+    shape_dj = (batchSize, pixelsX, pixelsY, Nlay, 1, dim)
+    E = tf.ones(shape = shape_di, dtype = tf.complex64) * tf.linalg.adjoint(D)
+    E = E - D * tf.ones(shape = shape_dj, dtype = tf.complex64)
+    E = tf.linalg.adjoint(D) - D
+
+    # Lorentzian broadening.
+    F = E / (E ** 2 + eps)
+    F = F - I * F
+
+    # Compute the reverse mode gradient of the eigendecomposition of A.
+    grad_A = tf.math.conj(F) *  tf.linalg.matmul(tf.linalg.adjoint(U), grad_U)
+    grad_A = grad_D + grad_A
+    grad_A = tf.linalg.matmul(grad_A, tf.linalg.adjoint(U))
+    grad_A = tf.linalg.matmul(tf.linalg.inv(tf.linalg.adjoint(U)), grad_A)
+    return grad_A
+
+  return [eigenvalues, eigenvectors], grad
+","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/src/rcwa_utils.py",".py","5152","134","# Copyright (c) 2020, Shane Colburn, University of Washington
+# This file is part of rcwa_tf
+# Written by Shane Colburn (Email: scolbur2@uw.edu)
+
+import tensorflow as tf
+import numpy as np
+import json
+
+    
+def convmat(A, P, Q):
+    '''
+    This function computes a convolution matrix for a real space matrix `A` that
+    represents either a relative permittivity or permeability distribution for a
+    set of pixels, layers, and batch.
+    Args:
+        A: A `tf.Tensor` of dtype `complex` and shape `(batchSize, pixelsX, 
+        pixelsY, Nlayers, Nx, Ny)` specifying real space values on a Cartesian
+        grid.
+
+        P: A positive and odd `int` specifying the number of spatial harmonics 
+        along `T1`.
+
+        Q: A positive and odd `int` specifying the number of spatial harmonics 
+        along `T2`.
+    Returns:
+        A `tf.Tensor` of dtype `complex` and shape `(batchSize, pixelsX, 
+        pixelsY, Nlayers, P * Q, P * Q)` representing a stack of convolution 
+        matrices based on `A`.   
+    '''
+    
+    # Determine the shape of A.
+    batchSize, pixelsX, pixelsY, Nlayers, Nx, Ny = A.shape
+
+    # Compute indices of spatial harmonics.
+    NH = P * Q # total number of harmonics.
+    #P_f = tf.cast(P, tf.float32)
+    p_max = tf.math.floordiv(P, 2)
+    q_max = tf.math.floordiv(P, 2)
+
+    # Indices along T1 and T2.
+    p = np.linspace(-p_max, p_max, P)
+    q = np.linspace(-q_max, q_max, Q)
+    
+    # Compute array indices of the center harmonic.
+    p0 = int(np.floor(Nx / 2))
+    q0 = int(np.floor(Ny / 2))
+
+    # Fourier transform the real space distributions.
+    A = tf.signal.fftshift(tf.signal.fft2d(A), axes = (4, 5)) / (Nx * Ny)
+
+    # Build the matrix.
+    firstCoeff = True
+    for qrow in range(Q):
+        for prow in range(P):
+            for qcol in range(Q):
+                for pcol in range(P):
+                    pfft = int(p[prow] - p[pcol])
+                    qfft = int(q[qrow] - q[qcol])
+
+                    # Sequentially concatenate Fourier coefficients.
+                    value = A[:, :, :, :, p0 + pfft, q0 + qfft]
+                    value = value[:, :, :, :, tf.newaxis, tf.newaxis]
+                    if firstCoeff:
+                        firstCoeff = False
+                        C = value
+                    else:
+                        C = tf.concat([C, value], axis = 5)
+    
+    # Reshape the coefficients tensor into a stack of convolution matrices.
+    convMatrixShape = (batchSize, pixelsX, pixelsY, Nlayers, P * Q, P * Q)      
+    matrixStack = tf.reshape(C, shape = convMatrixShape)
+    
+    return matrixStack
+
+def redheffer_star_product(SA, SB):
+    '''
+    This function computes the redheffer star product of two block matrices, 
+    which is the result of combining the S-parameter of two systems.
+    Args:
+        SA: A `dict` of `tf.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of a system. `SA` needs to have the 
+        keys ('S11', 'S12', 'S21', 'S22'), where each key maps to a `tf.Tensor`
+        of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH)`, where NH is the 
+        total number of spatial harmonics.
+
+        SB: A `dict` of `tf.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of a second system. `SB` needs to have
+        the keys ('S11', 'S12', 'S21', 'S22'), where each key maps to a
+        `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH)`, where 
+        NH is the total number of spatial harmonics.
+    Returns:
+        A `dict` of `tf.Tensor` values specifying the block matrix 
+        corresponding to the S-parameters of the combined system. `SA` needs 
+        to have the keys ('S11', 'S12', 'S21', 'S22'), where each key maps to
+        a `tf.Tensor` of shape `(batchSize, pixelsX, pixelsY, 2*NH, 2*NH),
+        where NH is the total number of spatial harmonics.  
+    '''
+    # Define the identity matrix.
+    batchSize, pixelsX, pixelsY, dim, _ = SA['S11'].shape
+    I = tf.eye(num_rows = dim, dtype = tf.complex64)
+    I = I[tf.newaxis, tf.newaxis, tf.newaxis, :, :]
+    I = tf.tile(I, multiples = (batchSize, pixelsX, pixelsY, 1, 1))
+    
+    # Calculate S11.
+    S11 = tf.linalg.inv(I - tf.linalg.matmul(SB['S11'], SA['S22']))
+    S11 = tf.linalg.matmul(S11, SB['S11'])
+    S11 = tf.linalg.matmul(SA['S12'], S11)
+    S11 = SA['S11'] + tf.linalg.matmul(S11, SA['S21'])
+    
+    # Calculate S12.
+    S12 = tf.linalg.inv(I - tf.linalg.matmul(SB['S11'], SA['S22']))
+    S12 = tf.linalg.matmul(S12, SB['S12'])
+    S12 = tf.linalg.matmul(SA['S12'], S12)
+    
+    # Calculate S21.
+    S21 = tf.linalg.inv(I - tf.linalg.matmul(SA['S22'], SB['S11']))
+    S21 = tf.linalg.matmul(S21, SA['S21'])
+    S21 = tf.linalg.matmul(SB['S21'], S21)
+    
+    # Calculate S22.
+    S22 = tf.linalg.inv(I - tf.linalg.matmul(SA['S22'], SB['S11']))
+    S22 = tf.linalg.matmul(S22, SA['S22'])
+    S22 = tf.linalg.matmul(SB['S21'], S22)
+    S22 = SB['S22'] + tf.linalg.matmul(S22, SB['S12'])
+    
+    # Store S parameters in an output dictionary.
+    S = dict({})
+    S['S11'] = S11
+    S['S12'] = S12
+    S['S21'] = S21
+    S['S22'] = S22
+    
+    return S
+","Python"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/metasurfaces/monochromatic_metalens_example.ipynb",".ipynb","7194","280","{
+ ""cells"": [
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""liutV-O4UzMM""
+   },
+   ""source"": [
+    ""**Imports**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 1,
+   ""metadata"": {
+    ""id"": ""QOCg73BSUzSx""
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""import tensorflow as tf\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import sys\n"",
+    ""\n"",
+    ""sys.path.insert(1, '/home/deveringham/thesis/rcwa_tf/src/')\n"",
+    ""import rcwa_utils\n"",
+    ""import tensor_utils\n"",
+    ""import solver""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""E3JWQHQ4UzaV""
+   },
+   ""source"": [
+    ""**Loss Function Definition**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""metadata"": {
+    ""id"": ""ZWkgQTE9Uzgx""
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""def focal_spot():\n"",
+    ""\n"",
+    ""  # Global parameters dictionary.\n"",
+    ""  global params\n"",
+    ""\n"",
+    ""  # Generate permittivity and permeability distributions.\n"",
+    ""  ER_t, UR_t = solver.generate_coupled_cylindrical_resonators(r_x_var, r_y_var, params)\n"",
+    ""\n"",
+    ""  # Simulate the system.\n"",
+    ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+    ""  field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""  focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""  index = (params['pixelsX'] * params['upsample']) // 2\n"",
+    ""  f1 = tf.abs(focal_plane[0, index, index])\n"",
+    ""\n"",
+    ""  # Maximize the electric field magnitude at the desired focal spot.\n"",
+    ""  return -f1""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""IcYSGM85Uzou""
+   },
+   ""source"": [
+    ""**Setup and Initialize Variables**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""metadata"": {
+    ""id"": ""ha4It8BUUzvG""
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""# Initialize global params dictionary.\n"",
+    ""params = solver.initialize_params(wavelengths = [632.0],\n"",
+    ""                      thetas = [0.0],\n"",
+    ""                      phis = [0.0],\n"",
+    ""                      pte = [1.0],\n"",
+    ""                      ptm = [0.0],\n"",
+    ""                      pixelsX = 31,\n"",
+    ""                      pixelsY = 31)\n"",
+    ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+    ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+    ""params['PQ'] = [5, 5] # Fourier Harmonics.\n"",
+    ""params['Nx'] = 128\n"",
+    ""params['Ny'] = params['Nx']\n"",
+    ""params['upsample'] = 11\n"",
+    ""params['f'] = 1.0 * params['Lx'] * params['pixelsX']\n"",
+    ""\n"",
+    ""# Define the free-space propagator and input field distribution for the metasurface.\n"",
+    ""params['propagator'] = solver.make_propagator(params, params['f'])\n"",
+    ""params['input'] = solver.define_input_fields(params)\n"",
+    ""\n"",
+    ""# Define duty cycles for unit cells based on 4 coupled elliptical nanoposts.\n"",
+    ""var_shape = (1, params['pixelsX'], params['pixelsY'], 4)\n"",
+    ""r_x_initial = 0.175 * np.ones(shape = var_shape)\n"",
+    ""r_y_initial = r_x_initial\n"",
+    ""r_x_var = tf.Variable(r_x_initial, dtype = tf.float32)\n"",
+    ""r_y_var = tf.Variable(r_y_initial, dtype = tf.float32)""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""FtwT36NEUz1a""
+   },
+   ""source"": [
+    ""**Optimize**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""metadata"": {
+    ""colab"": {
+     ""base_uri"": ""https://localhost:8080/""
+    },
+    ""id"": ""24klN1XRUz7s"",
+    ""outputId"": ""212658f6-0c8a-4da0-f04b-d447204cdfb4""
+   },
+   ""outputs"": [
+    {
+     ""name"": ""stdout"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""Loss: -1.005650520324707\n"",
+      ""\n"",
+      ""Optimizing...\n"",
+      ""Iteration 0\n"",
+      ""Loss: -1.0122785568237305\n"",
+      ""Iteration 1\n"",
+      ""Loss: -1.0458399057388306\n"",
+      ""Iteration 2\n"",
+      ""Loss: -1.1301920413970947\n"",
+      ""Iteration 3\n"",
+      ""Loss: -1.228545069694519\n"",
+      ""Iteration 4\n"",
+      ""Loss: -1.3118735551834106\n"",
+      ""Iteration 5\n"",
+      ""Loss: -1.3770451545715332\n"",
+      ""Iteration 6\n"",
+      ""Loss: -1.4479831457138062\n"",
+      ""Iteration 7\n"",
+      ""Loss: -1.5153852701187134\n"",
+      ""Iteration 8\n"",
+      ""Loss: -1.6087987422943115\n"",
+      ""Iteration 9\n"",
+      ""Loss: -1.7865630388259888\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""# Number of optimization iterations.\n"",
+    ""N = 500\n"",
+    ""\n"",
+    ""# Define an optimizer and data to be stored.\n"",
+    ""opt = tf.keras.optimizers.Adam(learning_rate = 2E-4)\n"",
+    ""loss = np.zeros(N + 1)\n"",
+    ""\n"",
+    ""# Compute initial loss and duty cycle.\n"",
+    ""loss[0] = focal_spot().numpy()\n"",
+    ""print('Loss: ' + str(loss[0]))\n"",
+    ""print('\\nOptimizing...')\n"",
+    ""\n"",
+    ""# Optimize.\n"",
+    ""for i in range(N):\n"",
+    ""  opt.minimize(focal_spot, var_list = [r_x_var, r_y_var])\n"",
+    ""  loss[i + 1] = focal_spot().numpy()\n"",
+    ""  print('Iteration ' + str(i))\n"",
+    ""  print('Loss: ' + str(loss[i+1]))""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""hX6jzUfcU0CY""
+   },
+   ""source"": [
+    ""**Display Learning Curve**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""metadata"": {
+    ""colab"": {
+     ""base_uri"": ""https://localhost:8080/"",
+     ""height"": 278
+    },
+    ""id"": ""8Sz1QpoxU0Je"",
+    ""outputId"": ""ea785734-74ce-47f9-fed5-c346eb3bf546""
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""plt.plot(loss)\n"",
+    ""plt.xlabel('Iterations')\n"",
+    ""plt.ylabel('Loss')\n"",
+    ""plt.xlim(0, N)\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""markdown"",
+   ""metadata"": {
+    ""id"": ""Sqkosi67VvNc""
+   },
+   ""source"": [
+    ""**Calculate the Focal Plane Intensity of the Optimized Structure**""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""metadata"": {
+    ""colab"": {
+     ""base_uri"": ""https://localhost:8080/"",
+     ""height"": 286
+    },
+    ""id"": ""HhBoAefiVvVe"",
+    ""outputId"": ""417a8ea5-fcf4-42d3-8ddf-b05465345826""
+   },
+   ""outputs"": [],
+   ""source"": [
+    ""ER_t, UR_t = solver.generate_coupled_cylindrical_resonators(r_x_var, r_y_var, params)\n"",
+    ""outputs = solver.simulate(ER_t, UR_t, params)\n"",
+    ""field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]\n"",
+    ""focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])\n"",
+    ""plt.imshow(tf.abs(focal_plane[0, :, :]) ** 2)\n"",
+    ""plt.colorbar()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": null,
+   ""metadata"": {
+    ""id"": ""v9stKGmQWVR4""
+   },
+   ""outputs"": [],
+   ""source"": []
+  }
+ ],
+ ""metadata"": {
+  ""accelerator"": ""GPU"",
+  ""colab"": {
+   ""collapsed_sections"": [],
+   ""name"": ""monochromatic_metalens_example.ipynb"",
+   ""provenance"": []
+  },
+  ""kernelspec"": {
+   ""display_name"": ""Python 3"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.6.8""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 1
+}
+","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_plasmonic_grating_optimization.ipynb",".ipynb","19690","213","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_plasmonic_grating_optimization.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""Xc53ZNVtt2DK""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""-tBvcSHDt40H""
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": 1,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""ZZsj6Huot-sn""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""-ZzTt9P8t-CQ""
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""\n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permittivity and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_plasmonic_cylindrical_nanoposts(var_duty, params)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the reflectance.\n"",
+        ""  ref_lambda1 = outputs['REF'][0, 0, 0]\n"",
+        ""\n"",
+        ""  return (1 - ref_lambda1)""
+      ],
+      ""execution_count"": 2,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""nPtzCzc6uD-3""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""fOKOZVWDuEI-""
+      },
+      ""source"": [
+        ""# Initialize global `params` dictionary storing optimization and simulation settings.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0],\n"",
+        ""                                  thetas = [0.0],\n"",
+        ""                                  erd = -54.5958 - 1j*21.7288, # Negative imaginary part convention for loss\n"",
+        ""                                  ers = 2.25 + 1j * 0,\n"",
+        ""                                  PQ = [11, 11],\n"",
+        ""                                  L = [50.0, 632.0],\n"",
+        ""                                  Lx = 350.0,\n"",
+        ""                                  Ly = 350.0)\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variable.\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'])\n"",
+        ""duty_initial = 0.75 * np.ones(shape = var_shape)\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": 3,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""nZ8sGxj9uWVf""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""9XyygRg0uWdv"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/""
+        },
+        ""outputId"": ""7412c94d-ec04-4b4b-e524-1f368f0e7dbe""
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 50\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 1E-3)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""duty = np.zeros(N + 1)\n"",
+        ""length = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute the initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""print('Optimizing...')\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": 4,
+      ""outputs"": [
+        {
+          ""output_type"": ""stream"",
+          ""text"": [
+            ""Optimizing...\n""
+          ],
+          ""name"": ""stdout""
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""HAD2a8oludGP""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""Axql3myAaZ2V"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 278
+        },
+        ""outputId"": ""63112503-0282-4c38-d921-8a185e6dcec7""
+      },
+      ""source"": [
+        ""plt.plot(1 - loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Reflectance')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": 5,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""KMFkZuBMIyuG""
+      },
+      ""source"": [
+        """"
+      ],
+      ""execution_count"": 5,
+      ""outputs"": []
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_grating_bilayer.ipynb",".ipynb","21588","237","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_grating_bilayer.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""S6L3mz1Ub6ix"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""fX1EwfpHb6uM"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": 1,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""BVg1SxsMb67Q"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""qauhDr0mb7GX"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""  \n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permitivitty and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_stacked_cylindrical_nanoposts(var_duty, params)\n"",
+        ""\n"",
+        ""  # Set the device layer thickness based on the length variable.\n"",
+        ""  thickness_coeff = tf.clip_by_value(var_length, clip_value_min = params['length_min'], clip_value_max = params['length_max'])\n"",
+        ""  thickness_coeff = tf.cast(thickness_coeff, dtype = tf.complex64)\n"",
+        ""  substrate_length_shape = (1, 1, 1, 1, 1, 1)\n"",
+        ""  device_length_shape = (1, 1, 1, params['Nlay'] - 1, 1, 1)\n"",
+        ""  substrate_layer = tf.ones(shape = substrate_length_shape, dtype = tf.complex64)\n"",
+        ""  device_layer = thickness_coeff * tf.ones(shape = device_length_shape, dtype = tf.complex64)\n"",
+        ""  wavelength = params['lam0'][0, 0, 0, 0, 0, 0].numpy()\n"",
+        ""  params['L'] = wavelength * tf.concat([device_layer, substrate_layer], axis = 3)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the reflectance at the first angle and minimize the reflectance at the second angle.\n"",
+        ""  ref_theta1 = outputs['REF'][0, 0, 0]\n"",
+        ""  ref_theta2 = outputs['REF'][1, 0, 0]\n"",
+        ""\n"",
+        ""  return -ref_theta1 * (1 - ref_theta2)""
+      ],
+      ""execution_count"": 2,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""zCFVOxH7b7SQ"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""2tH-Uww7b7dP"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Initialize duty cycle variable and global params dictionary.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0, 632.0],\n"",
+        ""                      thetas = [2.5, 7.5],\n"",
+        ""                      phis = [0.0, 0.0],\n"",
+        ""                      pte = [1.0, 1.0],\n"",
+        ""                      ptm = [0.0, 0.0])\n"",
+        ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+        ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+        ""params['PQ'] = [11, 11] # Fourier Harmonics.\n"",
+        ""params['batchSize'] = 2\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variables for top and bottom cylinders.\n"",
+        ""params['Nlay'] = 3\n"",
+        ""Nlay = params['Nlay']\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'], params['Nlay'] - 1)\n"",
+        ""duty_initial = np.ones(shape = var_shape)\n"",
+        ""duty_initial[0, 0, 0, 0] = 0.8\n"",
+        ""duty_initial[0, 0, 0, 1] = 0.4\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)\n"",
+        ""\n"",
+        ""# Initialize grating thickness variables for top and bottom cylinders.\n"",
+        ""var_length_shape = (1, 1, 1, params['Nlay'] - 1, 1, 1)\n"",
+        ""length_initial = np.ones(shape = var_length_shape)\n"",
+        ""length_initial[0, 0, 0, 0, 0, 0] = 1.0\n"",
+        ""length_initial[0, 0, 0, 1, 0, 0] = 0.5\n"",
+        ""var_length = tf.Variable(length_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": 3,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""JYlaVZWPb7o_"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""Skw4sy9Nb701"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 35
+        },
+        ""outputId"": ""61e8f6ea-ca6e-4750-8e0b-996a1656d11a""
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 200\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 0.002)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""print('Optimizing...')\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty, var_length])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": 4,
+      ""outputs"": [
+        {
+          ""output_type"": ""stream"",
+          ""text"": [
+            ""Optimizing...\n""
+          ],
+          ""name"": ""stdout""
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""FPoVlWYGb7_-"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""NFznjv6pb8MK"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 279
+        },
+        ""outputId"": ""8031a1f8-82ce-4355-e4a8-507883723e78""
+      },
+      ""source"": [
+        ""plt.plot(loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Loss')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": 5,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_grating_0deg_5deg.ipynb",".ipynb","22160","215","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_grating_0deg_5deg.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""UBfRygZTd5K0"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""s_z3yt9Vd5Vf"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""qOjIlt6Ud5gE"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""74fUyYA-d5oq"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""\n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permitivitty and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_cylindrical_nanoposts(var_duty, params)\n"",
+        ""\n"",
+        ""  # Set the device layer thickness based on the length variable.\n"",
+        ""  thickness_coeff = tf.clip_by_value(var_length, clip_value_min = params['length_min'], clip_value_max = params['length_max'])\n"",
+        ""  thickness_coeff = tf.cast(thickness_coeff, dtype = tf.complex64)\n"",
+        ""  length_shape = (1, 1, 1, 1, 1, 1)\n"",
+        ""  substrate_layer = tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  device_layer = thickness_coeff * tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  wavelength = params['lam0'][0, 0, 0, 0, 0, 0].numpy()\n"",
+        ""  params['L'] = wavelength * tf.concat([device_layer, substrate_layer], axis = 3)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the product of the reflectances.\n"",
+        ""  ref_theta1 = outputs['REF'][0, 0, 0]\n"",
+        ""  ref_theta2 = outputs['REF'][1, 0, 0]\n"",
+        ""\n"",
+        ""  return -ref_theta1 * ref_theta2""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""n3tr9yegd5z6"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""GVhDWNNPd58S"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Initialize global params dictionary.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0, 632.0],\n"",
+        ""                      thetas = [0.0, 5.0],\n"",
+        ""                      phis = [0.0, 0.0],\n"",
+        ""                      pte = [1.0, 1.0],\n"",
+        ""                      ptm = [0.0, 0.0])\n"",
+        ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+        ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+        ""params['PQ'] = [11, 11] # Fourier Harmonics.\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variable.\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'])\n"",
+        ""duty_initial = 0.7 * np.ones(shape = var_shape)\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)\n"",
+        ""\n"",
+        ""# Initialize grating thickness variable.\n"",
+        ""length_initial = 1.0\n"",
+        ""var_length = tf.Variable(length_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""qMmIcj6Nd6GC"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""27VkA9_Fd6Oe"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 200\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 0.002)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty, var_length])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""1WXf-8_zd6Wr"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""cZ3nUIGXd6eH"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 278
+        },
+        ""outputId"": ""cd281953-7d57-4333-9e9f-82a86a57ded8""
+      },
+      ""source"": [
+        ""plt.plot(loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Loss')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": null,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_grating_two_polarizations.ipynb",".ipynb","20006","231","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_grating_two_polarizations.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""B3shUjcSg9gI"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""zpGwXPVBg9o9"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": 1,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""jhLbU0Flg9ym"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""FDAZFBPNg98C"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""\n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permitivitty and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_rectangular_lines(var_duty, params)\n"",
+        ""\n"",
+        ""  # Set the device layer thickness based on the length variable.\n"",
+        ""  thickness_coeff = tf.clip_by_value(var_length, clip_value_min = params['length_min'], clip_value_max = params['length_max'])\n"",
+        ""  thickness_coeff = tf.cast(thickness_coeff, dtype = tf.complex64)\n"",
+        ""  length_shape = (1, 1, 1, 1, 1, 1)\n"",
+        ""  substrate_layer = tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  device_layer = thickness_coeff * tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  wavelength = params['lam0'][0, 0, 0, 0, 0, 0].numpy()\n"",
+        ""  params['L'] = wavelength * tf.concat([device_layer, substrate_layer], axis = 3)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the reflectance for the first polarization and minimize the reflectance for the second polarization.\n"",
+        ""  ref_pol1 = outputs['REF'][0, 0, 0]\n"",
+        ""  ref_pol2 = outputs['REF'][1, 0, 0]\n"",
+        ""\n"",
+        ""  return -ref_pol1 * (1 - ref_pol2)""
+      ],
+      ""execution_count"": 2,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""kAqCAAi3g-FB"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""KJeN_eDJg-OK"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Initialize duty cycle variable and global params dictionary.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0, 632.0],\n"",
+        ""                      thetas = [0.0, 0.0],\n"",
+        ""                      phis = [0.0, 0.0],\n"",
+        ""                      pte = [1.0, 0.0],\n"",
+        ""                      ptm = [0.0, 1.0])\n"",
+        ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+        ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+        ""params['PQ'] = [11, 11] # Fourier Harmonics.\n"",
+        ""params['batchSize'] = 2\n"",
+        ""params['Lx'] = 0.75 * 632 * params['nanometers'] # period along x\n"",
+        ""params['Ly'] = params['Lx'] # period along y\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variable.\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'])\n"",
+        ""duty_initial = 0.4 * np.ones(shape = var_shape)\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)\n"",
+        ""\n"",
+        ""# Initialize grating thickness variable.\n"",
+        ""length_initial = 1.0\n"",
+        ""var_length = tf.Variable(length_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": 3,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""0wvOrox4g-Xl"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""N-VOqHLqg-hv"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 35
+        },
+        ""outputId"": ""7cc822d0-ea27-409b-c001-d89992a96083""
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 100\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 0.003)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""print('Optimizing...')\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty, var_length])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": 4,
+      ""outputs"": [
+        {
+          ""output_type"": ""stream"",
+          ""text"": [
+            ""Optimizing...\n""
+          ],
+          ""name"": ""stdout""
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""l4Sfb0cGg-rn"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""eH50N_cdg-0o"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 282
+        },
+        ""outputId"": ""1b5e76ac-8630-42dd-f08c-4642b7b92848""
+      },
+      ""source"": [
+        ""plt.plot(loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Loss')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": 5,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_grating_two_wavelengths.ipynb",".ipynb","19513","228","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_grating_two_wavelengths.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""iXojb3z1X_fo"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""HJ9-UNNjX_qN"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""76Zw4T37Yjsl"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""N52BVsa8YkBK"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""\n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permitivitty and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_cylindrical_nanoposts(var_duty, params)\n"",
+        ""\n"",
+        ""  # Set the device layer thickness based on the length variable.\n"",
+        ""  thickness_coeff = tf.clip_by_value(var_length, clip_value_min = params['length_min'], clip_value_max = params['length_max'])\n"",
+        ""  thickness_coeff = tf.cast(thickness_coeff, dtype = tf.complex64)\n"",
+        ""  length_shape = (1, 1, 1, 1, 1, 1)\n"",
+        ""  substrate_layer = tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  device_layer = thickness_coeff * tf.ones(shape = length_shape, dtype = tf.complex64)\n"",
+        ""  wavelength = params['lam0'][0, 0, 0, 0, 0, 0].numpy()\n"",
+        ""  params['L'] = wavelength * tf.concat([device_layer, substrate_layer], axis = 3)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the product of the reflectances.\n"",
+        ""  ref_lambda1 = outputs['REF'][0, 0, 0]\n"",
+        ""  ref_lambda2 = outputs['REF'][1, 0, 0]\n"",
+        ""\n"",
+        ""  return -ref_lambda1 * ref_lambda2""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""zsz_XCZjYqtr"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""trCBNNrHYq0a"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Initialize global params dictionary.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0, 530.0],\n"",
+        ""                      thetas = [0.0, 0.0],\n"",
+        ""                      phis = [0.0, 0.0],\n"",
+        ""                      pte = [1.0, 1.0],\n"",
+        ""                      ptm = [0.0, 0.0])\n"",
+        ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+        ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+        ""params['PQ'] = [11, 11] # Fourier Harmonics.\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variable.\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'])\n"",
+        ""duty_initial = 0.8 * np.ones(shape = var_shape)\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)\n"",
+        ""\n"",
+        ""# Initialize grating thickness variable.\n"",
+        ""length_initial = 0.25\n"",
+        ""var_length = tf.Variable(length_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": null,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""WvVK-VXMYq79"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""WBLtRB3gYrDG"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 34
+        },
+        ""outputId"": ""c2de1613-6457-41c9-c739-6e74e46f93bb""
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 200\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 0.0005)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""print('Optimizing...')\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty, var_length])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": null,
+      ""outputs"": [
+        {
+          ""output_type"": ""stream"",
+          ""text"": [
+            ""Optimizing...\n""
+          ],
+          ""name"": ""stdout""
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""jEcnCtAiYrLa"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""Axql3myAaZ2V"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 278
+        },
+        ""outputId"": ""6b69cc04-a97e-431c-b65e-f842b431fb61""
+      },
+      ""source"": [
+        ""plt.plot(loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Loss')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": null,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","rcwa_tf/examples/gratings/reflective_grating.ipynb",".ipynb","20472","225","{
+  ""nbformat"": 4,
+  ""nbformat_minor"": 0,
+  ""metadata"": {
+    ""colab"": {
+      ""name"": ""reflective_grating.ipynb"",
+      ""provenance"": [],
+      ""collapsed_sections"": [],
+      ""machine_shape"": ""hm""
+    },
+    ""kernelspec"": {
+      ""name"": ""python3"",
+      ""display_name"": ""Python 3""
+    },
+    ""accelerator"": ""GPU""
+  },
+  ""cells"": [
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""Xc53ZNVtt2DK"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Imports**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""-tBvcSHDt40H"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""import tensorflow as tf\n"",
+        ""import numpy as np\n"",
+        ""import rcwa_utils\n"",
+        ""import tensor_utils\n"",
+        ""import solver\n"",
+        ""import matplotlib.pyplot as plt""
+      ],
+      ""execution_count"": 1,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""ZZsj6Huot-sn"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Loss Function Definition**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""-ZzTt9P8t-CQ"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""def loss_func():\n"",
+        ""\n"",
+        ""  # Global parameters dictionary.\n"",
+        ""  global params\n"",
+        ""\n"",
+        ""  # Generate permittivity and permeability distributions.\n"",
+        ""  ER_t, UR_t = solver.generate_cylindrical_nanoposts(var_duty, params)\n"",
+        ""\n"",
+        ""  # Simulate the system.\n"",
+        ""  outputs = solver.simulate(ER_t, UR_t, params)\n"",
+        ""\n"",
+        ""  # Maximize the reflectance.\n"",
+        ""  ref_lambda1 = outputs['REF'][0, 0, 0]\n"",
+        ""\n"",
+        ""  return (1 - ref_lambda1)""
+      ],
+      ""execution_count"": 2,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""nPtzCzc6uD-3"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Setup and Initialize Variables**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""fOKOZVWDuEI-"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        ""# Initialize global `params` dictionary storing optimization and simulation settings.\n"",
+        ""params = solver.initialize_params(wavelengths = [632.0], thetas = [0.0])\n"",
+        ""params['erd'] = 6.76 # Grating layer permittivity.\n"",
+        ""params['ers'] = 2.25 # Subtrate layer permittivity.\n"",
+        ""params['PQ'] = [11, 11] # Fourier Harmonics.\n"",
+        ""\n"",
+        ""# Initialize grating duty cycle variable.\n"",
+        ""var_shape = (1, params['pixelsX'], params['pixelsY'])\n"",
+        ""duty_initial = 0.6 * np.ones(shape = var_shape)\n"",
+        ""var_duty = tf.Variable(duty_initial, dtype = tf.float32)""
+      ],
+      ""execution_count"": 3,
+      ""outputs"": []
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""nZ8sGxj9uWVf"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Optimize**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""9XyygRg0uWdv"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 34
+        },
+        ""outputId"": ""85f89e7f-a722-4395-d1fa-f505438fbc95""
+      },
+      ""source"": [
+        ""# Number of optimization iterations.\n"",
+        ""N = 49\n"",
+        ""\n"",
+        ""# Define an optimizer and data to be stored.\n"",
+        ""opt = tf.keras.optimizers.Adam(learning_rate = 1E-3)\n"",
+        ""loss = np.zeros(N + 1)\n"",
+        ""duty = np.zeros(N + 1)\n"",
+        ""length = np.zeros(N + 1)\n"",
+        ""\n"",
+        ""# Compute the initial loss.\n"",
+        ""loss[0] = loss_func().numpy()\n"",
+        ""\n"",
+        ""# Optimize.\n"",
+        ""print('Optimizing...')\n"",
+        ""for i in range(N):\n"",
+        ""  opt.minimize(loss_func, var_list = [var_duty])\n"",
+        ""  loss[i + 1] = loss_func().numpy()""
+      ],
+      ""execution_count"": 4,
+      ""outputs"": [
+        {
+          ""output_type"": ""stream"",
+          ""text"": [
+            ""Optimizing...\n""
+          ],
+          ""name"": ""stdout""
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""markdown"",
+      ""metadata"": {
+        ""id"": ""HAD2a8oludGP"",
+        ""colab_type"": ""text""
+      },
+      ""source"": [
+        ""**Display Learning Curve**""
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""Axql3myAaZ2V"",
+        ""colab_type"": ""code"",
+        ""colab"": {
+          ""base_uri"": ""https://localhost:8080/"",
+          ""height"": 278
+        },
+        ""outputId"": ""a9da9e39-361f-4c90-a583-a33aeb54c043""
+      },
+      ""source"": [
+        ""plt.plot(loss)\n"",
+        ""plt.xlabel('Iterations')\n"",
+        ""plt.ylabel('Transmittance')\n"",
+        ""plt.xlim(0, N)\n"",
+        ""plt.show()""
+      ],
+      ""execution_count"": 9,
+      ""outputs"": [
+        {
+          ""output_type"": ""display_data"",
+          ""data"": {
+            ""image/png"": 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\n"",
+            ""text/plain"": [
+              ""<Figure size 432x288 with 1 Axes>""
+            ]
+          },
+          ""metadata"": {
+            ""tags"": [],
+            ""needs_background"": ""light""
+          }
+        }
+      ]
+    },
+    {
+      ""cell_type"": ""code"",
+      ""metadata"": {
+        ""id"": ""60hyJXTYuoUH"",
+        ""colab_type"": ""code"",
+        ""colab"": {}
+      },
+      ""source"": [
+        """"
+      ],
+      ""execution_count"": 5,
+      ""outputs"": []
+    }
+  ]
+}","Unknown"
+"Metamaterial","deveringham/metalens_optimization","src/solver_metasurface.py",".py","17987","482","import tensorflow as tf
+import numpy as np
+import itertools
+import json
+import solver
+import rcwa_utils
+import tensor_utils
+import matplotlib.pyplot as plt
+from matplotlib import colors
+
+
+def generate_layered_metasurface(h, params):
+    '''
+    Generates permittivity/permeability for a multilayer metasurface design,
+    based on a height representation of the metasurface.
+
+    Args:
+        h: A `tf.Tensor` of shape `(pixelsX, pixelsY)` specifying the
+        metasurface height at each unit cell. Each entry in this tensor should
+        be a float in [0,params['Nlay']-1].
+        
+        params: A `dict` containing simulation and optimization settings.
+
+    Returns:
+        ER_t: A `tf.Tensor` of shape 
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permittivity distribution of the unit cell.
+
+        UR_t: A `tf.Tensor` of shape
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permeability distribution of the unit cell.
+
+    '''
+
+    # Retrieve simulation size parameters.
+    batchSize = params['batchSize']
+    pixelsX = params['pixelsX']
+    pixelsY = params['pixelsY']
+    Nlay = params['Nlay']
+    Nx = params['Nx']
+    Ny = params['Ny']
+
+    # Initialize relative permeability.
+    materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+    UR = params['urd'] * np.ones(materials_shape)
+    
+    # Limit optimization range.
+    h = tf.clip_by_value(h, clip_value_min = 0, clip_value_max = Nlay-1)
+    
+    # Convert height representation of to stacked representation.
+    z = diff_height_to_stacked(h, params)
+    
+    # Repeat entries in z so that it has the shape
+    # (batchSize, pixelsX, pixelsY, 1, Nx, Ny).
+    z = z[tf.newaxis, :, :, :, tf.newaxis, tf.newaxis]
+    z = tf.tile(z, multiples = (batchSize, 1, 1, 1, Nx, Ny))
+
+    # Build substrate layer and concatenate along the layers dimension.
+    layer_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+    ER_substrate = params['ers'] * tf.ones(layer_shape, dtype = tf.float32)
+    ER_t = tf.concat(values = [z, ER_substrate], axis = 3)
+
+    # Cast to complex for subsequent calculations.
+    ER_t = tf.cast(ER_t, dtype = tf.complex64)
+    UR_t = tf.convert_to_tensor(UR, dtype = tf.float32)
+    UR_t = tf.cast(UR_t, dtype = tf.complex64)
+
+    return ER_t, UR_t
+
+
+def diff_height_to_stacked(h, params):
+    '''
+    Performs a differentiable transformation from the continuous height
+    representation of a metasurface pixel to a continous stacked 
+    representation. This is achieved via differential thresholding based on a
+    sigmoid function.
+
+    The HEIGHT REPRESENTATION of a metasurface is a tensor containing floats
+    representing the material height at each metasurface pixel.
+
+    The STACKED REPRESENTAION of a metasurface is a 3D tensor specifying a
+    float relative permittivity of the device at each pixel and on each
+    layer. This does not include the substrate layer.
+
+    As params['sigmoid_coeff'] is increased, the thresholding becomes more
+    strict, until eventually the metasurface is restricted to be 'admissable' -
+    that is, each position in the stacked representation may take on only one
+    of the two allowed values.
+    
+    Args:
+        h: A `tf.Tensor` of shape `(pixelsX, pixelsY)` and type float
+            containing heights of each pixel.
+        
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        z: A `tf.Tensor` of shape `(pixelsX, pixelsY, Nlay-1)` and type float
+            containing relative permittivity of the device at each pixel and on
+            each non-substrate layer.
+
+    '''
+    
+    Nlay = params['Nlay']
+    z = tf.stack( [diff_threshold(h, thresh=Nlay-1-i,
+                                  coeff=params['sigmoid_coeff'],
+                                  offset=Nlay-2-i,
+                                  output_scaling = [params['eps_min'],params['eps_max']]) for i in range(Nlay-1) ] )
+
+    return tf.transpose(z, perm=[1,2,0])
+
+
+def diff_threshold(x, coeff=1, thresh=1, offset=0, output_scaling=[0,1]):
+    '''
+    Performs a differentiable thresholding operation on the input, based on a
+    sigmoid funciton.
+    
+    Args:
+        x: Float input to be thresholded. Can be a single number or a tensor
+            of any dimension.
+        
+        coeff: Float coefficient determining steepness of thresholding.
+        
+        thresh: Float thresholding cutoff, i.e. where in x the step should
+            occur.
+        
+        offset: Float minimum value assumed to occur in x. This value is
+            subtracted from x first before the operation is applied, such that
+            the sigmoid cutoff occurs halfway between in_offset and thresh.
+            
+        output_scaling: Float list of length 2 specifying limits to which
+            output should be renormalized.
+        
+        Both offsets should be < thresh, and coeff should be >= 0.
+    
+    Returns:
+        x: Thresholded input.
+
+    '''
+    
+    x_new = tf.math.sigmoid(coeff * (x - (offset + (thresh - offset)/2)) )
+    x_new = output_scaling[0] + (output_scaling[1] - output_scaling[0]) * x_new
+    return x_new
+
+
+def get_substrate_layer(params):
+    '''
+    Generates a tensor representing the substrate layer of the device.
+    
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        ER_substrate: A `tf.Tensor` of shape
+            `(batchSize, pixelsX, pixelsY, 1, Nx, Ny)' specifying the relative
+            permittivity distribution of the unit cell in the substrate layer.
+
+    '''
+    
+    # Retrieve simulation size parameters.
+    batchSize = params['batchSize']
+    pixelsX = params['pixelsX']
+    pixelsY = params['pixelsY']
+    Nlay = params['Nlay']
+    Nx = params['Nx']
+    Ny = params['Ny']
+    
+    # Build and return substrate layer.
+    layer_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+    ER_substrate = params['ers'] * tf.ones(layer_shape, dtype = tf.float32)
+    
+    return ER_substrate
+
+
+def init_layered_metasurface(params, initial_height=0):
+    '''
+    Generates an initial guess for optimization of a multilayer metasurface.
+
+    The provided guess is a height representation of the metasurface.
+
+    If params['random_init'] == 0, returns a initial guess with zero height
+    at all pixels. Otherwise, returns an initial guess with all pixels
+    at the height specified by initial_height.
+    
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+
+        initial_height: A float in the range [0, Nlay-1] specifying the an
+            initial guess for the height at each pixel.
+        
+    Returns:
+        init: A `np.array of shape `(pixelsX, pixelsY)` specifying an initial
+        guess for the height of the metasurface at each pixel.
+    
+    '''
+    
+    if params['enable_random_init']:
+        init = np.random.rand(params['pixelsX'], params['pixelsY'])
+        return init * (params['Nlay'] - 1)
+    else:
+        return np.ones(shape=(params['pixelsX'], params['pixelsY'])) * initial_height
+
+
+def display_layered_metasurface(ER_t, params):
+    '''
+    Displays stacked representation of a metasurface.
+
+    Args:
+        ER_t: A `tf.Tensor` of shape
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permittivity distribution of the unit cell.
+        
+        params: A `dict` containing simulation and optimization settings.
+
+    Returns: None
+
+    '''
+    
+    # Display the permittiviy profile.
+    norm = colors.Normalize(vmin=params['eps_min'], vmax=params['eps_max'])
+    images=[]
+    fig, axes = plt.subplots(params['Nlay'], 1, figsize=(12,12))
+    for l in range(params['Nlay']):
+        img = tf.transpose(tf.squeeze(ER_t[0,:,:,l,:,:]),[0,2,1,3])
+        img = tf.math.real(tf.reshape(img, (params['pixelsX']*params['Nx'],params['pixelsY']*params['Ny'])))
+        images.append(axes[l].matshow(img, interpolation='nearest'))
+        axes[l].get_xaxis().set_visible(False)
+        axes[l].get_yaxis().set_visible(False)
+        images[l].set_norm(norm)
+        
+    plt.show()
+
+
+def evaluate_solution(focal_plane, params):
+    '''
+    Generates an evaluation score of a metasurface solution which can be used
+    to compare a solution to others.
+    
+    Args:
+        focal_plane: A `tf.Tensor` of shape
+            `(batchSize, pixelsX * upsample, pixelsY * upsample)` describing 
+            electric field intensity on the focal plane.
+    
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        eval_score: Float evaluation score in range [0, inf).
+
+    '''
+    
+    r = params['focal_spot_radius']
+    index = (params['pixelsX'] * params['upsample']) // 2
+
+    eval_score = tf.math.reduce_sum(
+        tf.abs(focal_plane[0, index-r:index+r, index-r:index+r]) )
+
+    return float(eval_score.numpy())
+
+
+def optimize_device(user_params):
+    '''
+    Produces an optimized layered metasurface design for some given device and
+    optimization parameters.
+
+    Args:
+        user_params: A `dict` containing simulation and optimization settings.
+            As opposed to dicts named simply 'params' elsewhere in this code,
+            'user_params' contains only parameters which are able to be
+            directly configured by a user, and not those derived parameters
+            calculated by the RCWA solver.
+
+    Returns:
+        h: A `tf.Tensor` of shape `(pixelsX, pixelsY)` and type float
+            containing heights of each pixel in the optimized design.
+
+        loss: A 'tf.Tensor' of shape `(N+1)` and type float containing
+            containing calculated loss at each optimization iteration.
+
+        params: A `dict` containing simulation and optimization settings.
+            The same as the provided user_params, but also contains
+            derived parameters calculated by the RCWA solver.
+
+    '''
+    
+    # Initialize and populate dictionary of solver parameters, based on the
+    # dictionary of user-provided parameters.
+    params = solver.initialize_params(wavelengths=user_params['wavelengths'],
+                                      thetas=user_params['thetas'],
+                                      phis=user_params['phis'],
+                                      pte=user_params['pte'],
+                                      ptm=user_params['ptm'],
+                                      pixelsX=user_params['pixelsX'],
+                                      pixelsY=user_params['pixelsY'],
+                                      erd=user_params['erd'],
+                                      ers=user_params['ers'],
+                                      PQ=user_params['PQ'],
+                                      Lx=user_params['Lx'],
+                                      Ly=user_params['Ly'],
+                                      L=user_params['L'],
+                                      Nx=16,
+                                      eps_min=1.0,
+                                      eps_max=user_params['erd'])
+    
+    # Merge with the user-provided parameter dictionary.
+    params['N'] = user_params['N']
+    params['sigmoid_coeff'] = user_params['sigmoid_coeff']
+    params['sigmoid_update'] = user_params['sigmoid_update']
+    params['learning_rate'] = user_params['learning_rate']
+    params['focal_spot_radius'] = user_params['focal_spot_radius']
+    params['enable_random_init'] = user_params['enable_random_init']
+    params['initial_height'] = user_params['initial_height']
+    params['enable_debug'] = user_params['enable_debug']
+    params['enable_print'] = user_params['enable_print']
+    params['enable_logging'] = user_params['enable_logging']
+    
+    # Get the loss function.
+    loss_function = user_params['loss_function']
+    
+    # This flag is set if the solver encounters an error.
+    params['err'] = False
+    
+    # Define the free-space propagator and input field distribution
+    # for the metasurface.
+    params['f'] = user_params['f'] * 1E-9
+    params['upsample'] = user_params['upsample']
+    params['propagator'] = solver.make_propagator(params, params['f'])
+    params['input'] = solver.define_input_fields(params)
+    
+    # Get initial guess for metasurface heights.
+    h = tf.Variable(
+            init_layered_metasurface(params, initial_height=params['initial_height']),
+            dtype=tf.float32)
+    
+    # Define an optimizer.
+    # Store losses as a tensor so that it works in graph mode.
+    opt = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+    
+    # Begin optimization.
+    if params['enable_print']: print('Optimizing... ', end="""")
+    N = user_params['N']
+    loss = np.zeros(N+1)
+    for i in range(N):
+
+        if params['enable_print']: print(str(i) + ', ', end="""")
+        
+        # Calculate gradients.
+        with tf.GradientTape() as tape:
+            l = loss_function(h, params) 
+            grads = tape.gradient(l, [h])
+        
+        # Apply gradients to variables.
+        opt.apply_gradients(zip(grads, [h]))
+        
+        # Keep track of iteration loss.
+        loss[i] = l
+        
+        # Anneal sigmoid coefficient.
+        params['sigmoid_coeff'] += (params['sigmoid_update'] / N)
+    
+    if params['enable_print']: print('Done.')
+        
+    # Round off to a final, admissable, solution.
+    # Do a final range clip.
+    h = tf.clip_by_value(h, clip_value_min=0, clip_value_max=params['Nlay']-1)
+    
+    # Round heights to nearest integer.
+    h = tf.math.round(h)
+    
+    # Get final loss.
+    loss[N] = loss_function(h,params)
+    
+    # Get scattering pattern of final solution.
+    ER_t, UR_t = generate_layered_metasurface(h, params)
+    outputs = solver.simulate(ER_t, UR_t, params)
+    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]
+    focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])
+    
+    return h, loss, params, focal_plane
+
+
+def hyperparameter_gridsearch(user_params):
+    '''
+    Runs a grid search for good hyperparameters for layered metasurface
+    optimization.
+
+    Args:
+        user_params: A `dict` containing simulation and optimization settings.
+            As opposed to dicts named simply 'params' elsewhere in this code,
+            'user_params' contains only parameters which are able to be
+            directly configured by a user.
+
+            Specifically, the entry param_grid is used to configure ranges for
+            the grid search.
+
+    Returns:
+        results: A list of `dict`s, each of which corresponds to one
+            run of metasurface optimization for some selected hyperparameters.
+            Each dict contains a list of the used hyperparameters.
+    '''
+    
+    # Allocate list of results.
+    # Each entry is a dictionary containing the list of hyperparameters used,
+    # height representation of the resulting metasurface, focal plane intensity
+    # pattern produced by the metasurface, evaluation score of the metasurface,
+    # and list of optimization losses for that run.
+    results = []
+    
+    # Get dimensions of grid.
+    hp_grid = user_params['param_grid'].values()
+    hp_names = user_params['param_grid'].keys()
+    
+    if user_params['enable_print']: print('Beginning hyperparameter grid search...')
+    
+    # Iterate over the grid.
+    for hyperparams in itertools.product(*hp_grid):
+        
+        # Otherwise, proceed.
+        if user_params['enable_print']: print('\nTrying hyperparameters: ' + str(list(hp_names)))
+        if user_params['enable_print']: print(hyperparams)
+
+        # Update parameter list dict with selected parameters.
+        for i, name in enumerate(hp_names):
+            user_params[name] = hyperparams[i]
+
+        # Run optimization with selected parameters.
+        h, loss, params, focal_plane = optimize_device(user_params)
+
+        # Get the evaluation score of the resulting solution.
+        eval_score = evaluate_solution(focal_plane, params)
+
+        # Save result.
+        result = {'hyperparameter_names': list(hp_names),
+            'hyperparameters': hyperparams,
+            'h': h,
+            'loss': loss,
+            'focal_plane': focal_plane,
+            'eval_score': eval_score,
+            'params': params }
+        results.append(result)
+        
+        # Log result.
+        if params['enable_logging']:
+            hyperparameter_string = '-'.join([h + str(v) for (h,v) in zip(hp_names,hyperparams)])
+            log_result(result, user_params['log_filename_prefix'] + hyperparameter_string + user_params['log_filename_extension'])
+    
+    return results
+
+                           
+def log_result(result, log_filename):
+    
+    # Open log file in write mode.
+    with open(log_filename, 'w', encoding=""utf-8"") as f:
+        
+        # Get json representation of results dict and write to log file.
+        json.dump(make_result_loggable(result), f)
+
+        
+def make_result_loggable(result):
+    
+    # Modify result dict to only include necessary elements
+    # and ensure that they are all json serializable.
+    loggable_result = {'hyperparameter_names': result['hyperparameter_names'],
+                       'hyperparameters': result['hyperparameters'],
+                       'h': result['h'].numpy().tolist(),
+                       'loss': result['loss'].tolist(),
+                       'focal_plane': tf.cast(result['focal_plane'], tf.float32).numpy().tolist(),
+                       'eval_score': result['eval_score'] }
+    
+    return loggable_result
+
+
+def load_result(log_filename):
+     
+    # Open log file in read mode.
+    with open(log_filename, 'r', encoding=""utf-8"") as f:
+
+        # Read json representation of results from log file.
+        result = json.load(f)
+        result['h'] = tf.convert_to_tensor(result['h'], dtype=tf.float32)
+        result['loss'] = np.array(result['loss'])
+        result['focal_plane'] = tf.convert_to_tensor(result['focal_plane'], dtype=tf.float32)
+        return result
+","Python"
+"Metamaterial","deveringham/metalens_optimization","src/utils.py",".py","2855","75","'''
+utils.py
+
+Utility functions for running COPILOT metasurface optimization.
+These configure GPU / CPU device usage for both the TensorFlow
+and PyTorch implementations.
+'''
+
+import os
+from tensorflow.config.experimental import set_memory_growth
+from tensorflow.config import list_physical_devices
+from torch import set_default_tensor_type
+import subprocess as sp
+
+
+# MB
+def cpu_memory_info():
+    command = ""cat /proc/meminfo""
+    memory_info = sp.check_output(command.split()).decode('ascii').split('\n')
+    memory_total_info = memory_info[0]
+    memory_total_value = int(memory_total_info.split()[1])
+    
+    memory_used_info = memory_info[1]
+    memory_used_value = int(memory_used_info.split()[1])
+    
+    return memory_total_value / 1024, memory_used_value / 1024
+
+# MiB
+def gpu_memory_info():
+    command = ""nvidia-smi --query-gpu=memory.total --format=csv""
+    memory_total_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:]
+    memory_total_values = [int(x.split()[0]) for i, x in enumerate(memory_total_info)]
+
+    command = ""nvidia-smi --query-gpu=memory.used --format=csv""
+    memory_used_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:]
+    memory_used_values = [int(x.split()[0]) for i, x in enumerate(memory_used_info)]
+
+    return memory_total_values, memory_used_values
+
+
+def gpu_memory_info_str():
+    memory_total_values, memory_used_values = gpu_memory_info()
+    info_list_str = ' ('
+    for ind, (total_value, used_value) in enumerate(zip(memory_total_values, memory_used_values)):
+        info_list_str += 'GPU ' + str(ind) + ':' + f' {used_value / total_value * 100:.2f}' + '%, '
+    info_list_str = info_list_str[:-2] + ')'
+    return info_list_str
+
+
+def print_gpu_memory_usage():
+    memory_total_values, memory_used_values = gpu_memory_info()
+
+    print(' ')
+    for ind, (total_value, used_value) in enumerate(zip(memory_total_values, memory_used_values)):
+        str_total_value = str(total_value) + ' MiB'
+        str_used_value = str(used_value) + ' MiB'
+        str_ratio = f' ({used_value / total_value * 100:.2f}' + '%)'
+        print(
+            'GPU memory_usage in device ' + str(ind) + ': ' + str_used_value + ' /' + str_total_value + str_ratio +
+            ' ' + str(total_value - used_value) + ' MiB available')
+
+
+def config_gpu_memory_usage():
+    gpus = list_physical_devices(""GPU"")
+    if gpus:
+        try:
+            for gpu in gpus:
+                set_memory_growth(gpu, True)
+                # Limit memory growth to 10 GB on each gpu.
+                #tf.config.set_logical_device_configuration(gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=10*1024)])
+        except RuntimeError as errmsg:
+            print(errmsg)
+     
+    # Make PyTorch allocate tensors on the GPU by default.
+    set_default_tensor_type('torch.cuda.FloatTensor')","Python"
+"Metamaterial","deveringham/metalens_optimization","src/solver_metasurface_pt.py",".py","23112","608","'''
+solver_metasurface_pt.py
+
+Functions implementing optimization algorithm for COPILOT metalens devices,
+using PyTorch differentiable implementation of RCWA.
+
+The important user-facing functions here are -
+
+optimize_device: use to optimize a single device for some given parameters.
+
+hyperparameter_gridsearch: use to optimize many devices for a given grid of
+   algorithm hyperparameters.
+'''
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.lines as lines
+from matplotlib import colors
+import itertools
+import json
+import gc
+
+import solver_pt
+import rcwa_utils_pt
+
+
+def generate_layered_metasurface(h, params):
+    '''
+    Generates permittivity/permeability for a multilayer metasurface design,
+    based on a height representation of the metasurface.
+
+    Args:
+        h: A `torch.Tensor` of shape `(pixelsX, pixelsY)` specifying the
+        metasurface height at each unit cell. Each entry in this tensor should
+        be a float in [0,params['Nlay']-1].
+        
+        params: A `dict` containing simulation and optimization settings.
+
+    Returns:
+        ER_t: A `torch.Tensor` of shape 
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permittivity distribution of the unit cell.
+
+        UR_t: A `torch.Tensor` of shape
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permeability distribution of the unit cell.
+
+    '''
+
+    # Retrieve simulation size parameters.
+    batchSize = params['batchSize']
+    pixelsX = params['pixelsX']
+    pixelsY = params['pixelsY']
+    Nlay = params['Nlay']
+    Nx = params['Nx']
+    Ny = params['Ny']
+
+    # Initialize relative permeability.
+    materials_shape = (batchSize, pixelsX, pixelsY, Nlay, Nx, Ny)
+    UR_t = params['urd'] * torch.ones(materials_shape)
+    
+    # Limit optimization range.
+    h = torch.clamp(h, min = 0, max = Nlay-1) 
+    
+    # Convert height representation of to stacked representation.
+    z = diff_height_to_stacked(h, params)
+    
+    # Repeat entries in z so that it has the shape
+    # (batchSize, pixelsX, pixelsY, 1, Nx, Ny).
+    z = z[None, :, :, :, None, None]
+    z = torch.tile(z, (batchSize, 1, 1, 1, Nx, Ny))
+
+    # Build substrate layer and concatenate along the layers dimension.
+    layer_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+    ER_substrate = params['ers'] * torch.ones(layer_shape, dtype = torch.float32)
+    ER_t = torch.cat([z, ER_substrate], dim = 3)
+
+    # Cast to complex for subsequent calculations.
+    ER_t = ER_t.type(torch.complex64)
+    UR_t = UR_t.type(torch.complex64)
+
+    return ER_t, UR_t
+
+
+def diff_height_to_stacked(h, params):
+    '''
+    Performs a differentiable transformation from the continuous height
+    representation of a metasurface pixel to a continous stacked 
+    representation. This is achieved via differential thresholding based on a
+    sigmoid function.
+
+    The HEIGHT REPRESENTATION of a metasurface is a tensor containing floats
+    representing the material height at each metasurface pixel.
+
+    The STACKED REPRESENTAION of a metasurface is a 3D tensor specifying a
+    float relative permittivity of the device at each pixel and on each
+    layer. This does not include the substrate layer.
+
+    As params['sigmoid_coeff'] is increased, the thresholding becomes more
+    strict, until eventually the metasurface is restricted to be 'admissable' -
+    that is, each position in the stacked representation may take on only one
+    of the two allowed values.
+    
+    Args:
+        h: A `torch.Tensor` of shape `(pixelsX, pixelsY)` and type float
+            containing heights of each pixel.
+        
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        z: A `torch.Tensor` of shape `(pixelsX, pixelsY, Nlay-1)` and type float
+            containing relative permittivity of the device at each pixel and on
+            each non-substrate layer.
+
+    '''
+    
+    Nlay = params['Nlay']
+    z = torch.stack( [diff_threshold(h, thresh=Nlay-1-i,
+                                  coeff=params['sigmoid_coeff'],
+                                  offset=Nlay-2-i,
+                                  output_scaling = [params['eps_min'],params['eps_max']]) for i in range(Nlay-1) ] )
+
+    return torch.permute(z, [1,2,0])
+
+
+def diff_threshold(x, coeff=1, thresh=1, offset=0, output_scaling=[0,1]):
+    '''
+    Performs a differentiable thresholding operation on the input, based on a
+    sigmoid funciton.
+    
+    Args:
+        x: Float input to be thresholded. Can be a single number or a tensor
+            of any dimension.
+        
+        coeff: Float coefficient determining steepness of thresholding.
+        
+        thresh: Float thresholding cutoff, i.e. where in x the step should
+            occur.
+        
+        offset: Float minimum value assumed to occur in x. This value is
+            subtracted from x first before the operation is applied, such that
+            the sigmoid cutoff occurs halfway between in_offset and thresh.
+            
+        output_scaling: Float list of length 2 specifying limits to which
+            output should be renormalized.
+        
+        Both offsets should be < thresh, and coeff should be >= 0.
+    
+    Returns:
+        x: Thresholded input.
+
+    '''
+    
+    x_new = torch.sigmoid(coeff * (x - (offset + (thresh - offset)/2)) )
+    x_new = output_scaling[0] + (output_scaling[1] - output_scaling[0]) * x_new
+    return x_new
+
+
+def get_substrate_layer(params):
+    '''
+    Generates a tensor representing the substrate layer of the device.
+    
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        ER_substrate: A `torch.Tensor` of shape
+            `(batchSize, pixelsX, pixelsY, 1, Nx, Ny)' specifying the relative
+            permittivity distribution of the unit cell in the substrate layer.
+
+    '''
+    
+    # Retrieve simulation size parameters.
+    batchSize = params['batchSize']
+    pixelsX = params['pixelsX']
+    pixelsY = params['pixelsY']
+    Nlay = params['Nlay']
+    Nx = params['Nx']
+    Ny = params['Ny']
+    
+    # Build and return substrate layer.
+    layer_shape = (batchSize, pixelsX, pixelsY, 1, Nx, Ny)
+    ER_substrate = params['ers'] * torch.ones(layer_shape, dtype = torch.float32)
+    
+    return ER_substrate
+
+
+def init_layered_metasurface(params, initial_height=0):
+    '''
+    Generates an initial guess for optimization of a multilayer metasurface.
+
+    The provided guess is a height representation of the metasurface.
+
+    If params['random_init'] == 0, returns a initial guess with zero height
+    at all pixels. Otherwise, returns an initial guess with all pixels
+    at the height specified by initial_height.
+    
+    Args:
+        params: A `dict` containing simulation and optimization settings.
+
+        initial_height: A float in the range [0, Nlay-1] specifying the an
+            initial guess for the height at each pixel.
+        
+    Returns:
+        init: A `np.array of shape `(pixelsX, pixelsY)` specifying an initial
+        guess for the height of the metasurface at each pixel.
+    
+    '''
+    
+    if params['enable_random_init']:
+        init = torch.rand((params['pixelsX'], params['pixelsY']))
+        return init * (params['Nlay'] - 1)
+    else:
+        return torch.ones((params['pixelsX'], params['pixelsY'])) * initial_height
+
+
+def display_layered_metasurface(ER_t, params):
+    '''
+    Displays stacked representation of a metasurface.
+
+    Args:
+        ER_t: A `torch.Tensor` of shape
+        `(batchSize, pixelsX, pixelsY, Nlayer, Nx, Ny)` specifying the relative
+        permittivity distribution of the unit cell.
+        
+        params: A `dict` containing simulation and optimization settings.
+
+    Returns: None
+
+    '''
+    
+    # Display the permittiviy profile.
+    norm = colors.Normalize(vmin=params['eps_min'], vmax=params['eps_max'])
+    images=[]
+    fig, axes = plt.subplots(params['Nlay'], 1, figsize=(12,12))
+    for l in range(params['Nlay']):
+        img = torch.permute(torch.squeeze(ER_t[0,:,:,l,:,:]),(0,2,1,3))
+        img = torch.real(torch.reshape(img, (params['pixelsX']*params['Nx'],params['pixelsY']*params['Ny'])))
+        images.append(axes[l].matshow(img.detach().cpu().numpy(), interpolation='nearest'))
+        axes[l].get_xaxis().set_visible(False)
+        axes[l].get_yaxis().set_visible(False)
+        images[l].set_norm(norm)
+        
+    plt.show()
+    
+
+def display_multiple_metasurfaces(h_list, params):
+    '''
+    Displays stacked representations of a list of devices.
+
+    Args:
+        h_list: A list of `torch.Tensor`s of shape `(pixelsX, pixelsY)` specifying the height representation
+            of multiple metasurfaces.
+        
+        params: A `dict` containing simulation and optimization settings.
+
+    Returns: None
+
+    '''
+    
+    # Set up the plot.
+    norm = colors.Normalize(vmin=params['eps_min'], vmax=params['eps_max'])
+    images=[]
+    fig, axes = plt.subplots(params['Nlay'], len(h_list), figsize=(12,12))
+    
+    # For each device...
+    for d in range(len(h_list)):
+    
+        # Get stacked representation of the surface.
+        ER_t, UR_t = generate_layered_metasurface(torch.Tensor(h_list[d]), params)
+
+        # Then for each layer...
+        for l in range(params['Nlay']):
+            img = torch.permute(torch.squeeze(ER_t[0,:,:,l,:,:]),(0,2,1,3))
+            img = torch.real(torch.reshape(img, (params['pixelsX']*params['Nx'],params['pixelsY']*params['Ny'])))
+            images.append(axes[l,d].matshow(img.detach().cpu().numpy(), interpolation='nearest'))
+            axes[l,d].get_xaxis().set_visible(False)
+            axes[l,d].get_yaxis().set_visible(False)
+            images[-1].set_norm(norm)
+        
+        # Add line dividing devices.
+        #if d > 0:
+        #    div_x = d/(len(h_list))
+        #    fig.lines.append(plt.Line2D([div_x, div_x], [0, 1], transform=fig.transFigure, color='black'))
+        
+    plt.show()
+
+
+def evaluate_solution(focal_plane, params):
+    '''
+    Generates an evaluation score of a metasurface solution which can be used
+    to compare a solution to others.
+    
+    Args:
+        focal_plane: A `torch.Tensor` of shape
+            `(batchSize, pixelsX * upsample, pixelsY * upsample)` describing 
+            electric field intensity on the focal plane.
+    
+        params: A `dict` containing simulation and optimization settings.
+    
+    Returns:
+        eval_score: Float evaluation score in range [0, inf).
+
+    '''
+    
+    r = params['focal_spot_radius']
+    index = (params['pixelsX'] * params['upsample']) // 2
+
+    eval_score = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]) )
+
+    return float(eval_score)
+
+
+def optimize_device(user_params):
+    '''
+    Produces an optimized layered metasurface design for some given device and
+    optimization parameters.
+
+    Args:
+        user_params: A `dict` containing simulation and optimization settings.
+            As opposed to dicts named simply 'params' elsewhere in this code,
+            'user_params' contains only parameters which are able to be
+            directly configured by a user, and not those derived parameters
+            calculated by the RCWA solver.
+
+    Returns:
+        h: A `torch.Tensor` of shape `(pixelsX, pixelsY)` and type float
+            containing heights of each pixel in the optimized design.
+
+        loss: A 'torch.Tensor' of shape `(N+1)` and type float containing
+            containing calculated loss at each optimization iteration.
+
+        params: A `dict` containing simulation and optimization settings.
+            The same as the provided user_params, but also contains
+            derived parameters calculated by the RCWA solver.
+            
+        focal_plane: A `torch.Tensor` of shape `(batchSize, params['upsample'] * pixelsX,
+            params['upsample'] * pixelsY)` and dtype `torch.complex64` specifying the 
+            the electric fields at the output plane.
+
+    '''
+    
+    params = solver_pt.initialize_params(wavelengths=user_params['wavelengths'],
+                                  thetas=user_params['thetas'],
+                                  phis=user_params['phis'],
+                                  pte=user_params['pte'],
+                                  ptm=user_params['ptm'],
+                                  pixelsX=user_params['pixelsX'],
+                                  pixelsY=user_params['pixelsY'],
+                                  erd=user_params['erd'],
+                                  ers=user_params['ers'],
+                                  PQ=user_params['PQ'],
+                                  Lx=user_params['Lx'],
+                                  Ly=user_params['Ly'],
+                                  L=user_params['L'],
+                                  Nx=16,
+                                  eps_min=1.0,
+                                  eps_max=user_params['erd'])
+
+    # Merge with the user-provided parameter dictionary.
+    params['N'] = user_params['N']
+    params['sigmoid_coeff'] = user_params['sigmoid_coeff']
+    params['sigmoid_update'] = user_params['sigmoid_update']
+    params['learning_rate'] = user_params['learning_rate']
+    params['focal_spot_radius'] = user_params['focal_spot_radius']
+    params['enable_random_init'] = user_params['enable_random_init']
+    params['initial_height'] = user_params['initial_height']
+    params['enable_debug'] = user_params['enable_debug']
+    params['enable_print'] = user_params['enable_print']
+    params['enable_logging'] = user_params['enable_logging']
+    params['log_filename_prefix'] = user_params['log_filename_prefix']
+    params['log_filename_extension'] = user_params['log_filename_extension']
+    params['parameter_string'] = user_params['parameter_string']
+    params['loss_function'] = user_params['loss_function']
+
+    # Define the free-space propagator and input field distribution
+    # for the metasurface.
+    params['f'] = user_params['f'] * 1E-9
+    params['upsample'] = user_params['upsample']
+    params['propagator'] = solver_pt.make_propagator(params, params['f'])
+    params['input'] = solver_pt.define_input_fields(params)
+
+    # Get the initial height representation of the metasurface.
+    h = torch.autograd.Variable(init_layered_metasurface(params, initial_height=params['initial_height']),
+                                requires_grad=True)
+    
+    # Initialize list of height representations. Used to track device change over optimzation run.
+    h_list = []
+    h_list.append(h)
+
+    # Define optimizer.
+    opt = torch.optim.Adam([h], lr=params['learning_rate'])
+    loss = np.zeros(params['N']+1)
+
+    # Optimize.
+    if params['enable_print']: print('Optimizing... ', end="""")
+    for i in range(params['N']):
+        if params['enable_print']: print(str(i) + ', ', end="""")
+        
+        opt.zero_grad()
+        l = params['loss_function'](h, params)
+        l.backward()
+        opt.step()
+        loss[i] = l
+
+        # Anneal sigmoid coefficient.
+        params['sigmoid_coeff'] += (params['sigmoid_update'] / params['N'])
+        
+        # Track change in h.
+        h_list.append(h.clone().detach())
+        
+    if params['enable_print']: print('Done.')
+
+    # Round off to a final, admissable, solution.
+    # Do a final range clip.
+    h = torch.clamp(h, min=0, max=params['Nlay']-1)
+
+    # Round heights to nearest integer.
+    h = torch.round(h)
+
+    # Get final loss.
+    loss[-1] = params['loss_function'](h, params)
+    
+    # Get scattering pattern of final solution.
+    ER_t, UR_t = generate_layered_metasurface(h, params)
+    outputs = solver_pt.simulate(ER_t, UR_t, params)
+    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]
+    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])
+    
+    # Get the evaluation score of the resulting solution.
+    eval_score = evaluate_solution(focal_plane, params)
+
+    # Log result.
+    if params['enable_logging']:
+        hp_names = ['N', 'sigmoid_update', 'learning_rate', 'initial_height']
+        hyperparams = [user_params[name] for name in hp_names]
+        result = {'hyperparameter_names': list(hp_names),
+            'hyperparameters': hyperparams,
+            'h': h,
+            'loss': loss,
+            'focal_plane': focal_plane,
+            'eval_score': eval_score,
+            'params': params }
+        
+        log_result(result, params['log_filename_prefix'] + params['parameter_string'] + user_params['log_filename_extension'])
+    
+    return h, loss, params, focal_plane, h_list
+
+
+def hyperparameter_gridsearch(user_params):
+    '''
+    Runs a grid search for good hyperparameters for layered metasurface
+    optimization.
+
+    Args:
+        user_params: A `dict` containing simulation and optimization settings.
+            As opposed to dicts named simply 'params' elsewhere in this code,
+            'user_params' contains only parameters which are able to be
+            directly configured by a user.
+
+            Specifically, the entry param_grid is used to configure ranges for
+            the grid search.
+
+    Returns:
+        results: A list of `dict`s, each of which corresponds to one
+            run of metasurface optimization for some selected hyperparameters.
+            Each dict contains a list of the used hyperparameters, as well as
+            the height representation of the reulting metasurface, evaluation
+            score assigned to that metasurface, the loss curve for the
+            optimization run, and the focal plane scatter pattern produced
+            by the optimized device.
+    '''
+    
+    # Allocate list of results.
+    # Each entry is a dictionary containing the list of hyperparameters used,
+    # height representation of the resulting metasurface, focal plane intensity
+    # pattern produced by the metasurface, evaluation score of the metasurface,
+    # and list of optimization losses for that run.
+    results = []
+    
+    # Get dimensions of grid.
+    hp_grid = user_params['param_grid'].values()
+    hp_names = user_params['param_grid'].keys()
+    
+    if user_params['enable_print']: print('Beginning hyperparameter grid search...')
+    
+    # Iterate over the grid.
+    for hyperparams in itertools.product(*hp_grid):
+        
+        if user_params['enable_print']: print('\nTrying hyperparameters: ' + str(list(hp_names)))
+        if user_params['enable_print']: print(hyperparams)
+
+        # Update parameter list dict with selected parameters.
+        for i, name in enumerate(hp_names):
+            user_params[name] = hyperparams[i]
+            
+        # Update log file name.
+        hyperparameter_string = '-'.join([h + str(v) for (h,v) in zip(hp_names,hyperparams)])
+        user_params['parameter_string'] = hyperparameter_string
+
+        # Run optimization with selected parameters.
+        h, loss, params, focal_plane = optimize_device(user_params)
+        
+        # Get the evaluation score of the resulting solution.
+        eval_score = evaluate_solution(focal_plane, params)
+        
+        # Save result.
+        result = {'hyperparameter_names': list(hp_names),
+            'hyperparameters': hyperparams,
+            'h': h,
+            'loss': loss,
+            'focal_plane': focal_plane,
+            'eval_score': eval_score,
+            'params': params }
+        results.append(result)
+
+    return results
+
+                           
+def log_result(result, log_filename):
+    '''
+    Writes the result of a single optimization run to an output file.
+    
+    Args:
+        result: A dict which corresponds to one run of metasurface optimization for 
+            some selected hyperparameters. It should contain the keys:
+            
+            hyperparameter_names: A list of string names of the hyperparameters being optimized.
+            hyperparameters: A list of hyperparameter values used for this run, corresponding to
+                those in hyperparameter_names.
+               
+            h: A list of shape `(pixelsX, pixelsY)` and type float
+                containing heights of each pixel in the optimized design.
+
+            loss: A list of shape `(N+1)` and type float containing
+                containing calculated loss at each optimization iteration.
+                
+            focal_plane: A list of shape `(batchSize, params['upsample'] * pixelsX,
+                params['upsample'] * pixelsY)` and dtype float specifying the 
+                the real part of electric fields at the output plane.
+            
+            eval_score: Float evaluation score of the solution in range [0, inf).
+
+            params: A `dict` containing simulation and optimization settings.
+        
+        log_filename: A string specifying the relative path of the file to write result to.
+            File is created if it does not exist and overwritten if it does.
+
+    Returns:
+        None
+    '''
+    
+    # Open log file in write mode.
+    with open(log_filename, 'w', encoding=""utf-8"") as f:
+        
+        # Get json representation of results dict and write to log file.
+        json.dump(make_result_loggable(result), f)
+
+        
+def make_result_loggable(result):
+    '''
+    Prepares the results dictionary of an optimization run for writing to an output file.
+    
+    Args:
+        result: A dict which corresponds to one run of metasurface optimization for 
+            some selected hyperparameters, structured as in log_result, except that
+            h, loss, and focal_plane may be 'torch.Tensor's.
+
+    Returns:
+        loggable_result: A dict with the same contents as result but with element
+            types converted such that it can be passed to log_result.
+    '''
+    
+    # Modify result dict to only include necessary elements
+    # and ensure that they are all json serializable.
+    loggable_result = {'hyperparameter_names': result['hyperparameter_names'],
+                       'hyperparameters': result['hyperparameters'],
+                       'h': result['h'].detach().cpu().numpy().tolist(),
+                       'loss': result['loss'].tolist(),
+                       'focal_plane': result['focal_plane'].type(torch.float32).detach().cpu().numpy().tolist(),
+                       'eval_score': result['eval_score'] }
+    
+    return loggable_result
+
+
+def load_result(log_filename):
+    '''
+    Read results of an optimization run from a file.
+    
+    Args:
+        log_filename: A string specifying the relative path to the output file to be read.
+    
+    Returns:
+        result: A dict containing all information about the recorded optimization run,
+            structured as in log_result.
+    '''
+    
+    # Open log file in read mode.
+    with open(log_filename, 'r', encoding=""utf-8"") as f:
+
+        # Read json representation of results from log file.
+        result = json.load(f)
+        result['h'] = torch.tensor(result['h'], dtype=torch.float32)
+        result['loss'] = np.array(result['loss'])
+        result['focal_plane'] = torch.tensor(result['focal_plane'], dtype=torch.float32)
+        return result
+","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/nearfield_optimization_tf.py",".py","3429","103","# Use these parameters to choose which devices to use.
+use_GPU = True
+
+# Import device utils.
+import sys
+import os
+sys.path.append('./src/')
+sys.path.append('./rcwa_tf/src/')
+import utils
+
+# Configure GPUs.
+if (use_GPU): utils.config_gpu_memory_usage()
+
+# Measure GPU memory usage.
+if (use_GPU):
+    gpu_memory_init = utils.gpu_memory_info()
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import solver
+import solver_metasurface
+
+# Initialize parameters.
+user_params = {}
+
+# Tunable parameters.
+# These are the values used if hyperparameter grid search is disabled.
+user_params['pixelsX'] = int(sys.argv[1])
+user_params['N'] = int(sys.argv[2])
+user_params['sigmoid_update'] = float(sys.argv[3])
+user_params['learning_rate'] = float(sys.argv[4])
+user_params['initial_height'] = int(sys.argv[5])
+
+user_params['parameter_string'] = 'N' + str(user_params['N']) \
+    + '-sigmoid_update' + str(user_params['sigmoid_update']) \
+    + '-learning_rate' + str(user_params['learning_rate']) \
+    + '-initial_height' + str(user_params['initial_height'])
+
+# Source parameters.
+user_params['wavelengths'] = [120.0]
+user_params['thetas'] = [0.0]
+user_params['phis'] = [0.0]
+user_params['pte'] = [1.0]
+user_params['ptm'] = [0.0]
+
+# Device parmeters.
+user_params['pixelsY'] = user_params['pixelsX']
+user_params['erd'] = 11.9
+user_params['ers'] = user_params['erd']
+user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 950.0]
+user_params['Lx'] = 5000.0 / user_params['pixelsX']
+user_params['Ly'] = user_params['Lx']
+user_params['f'] = 0.0 # Focal distance (nm)
+
+# Solver parameters.
+user_params['PQ'] = [3,3]
+user_params['upsample'] = 11
+
+# Problem parameters.
+user_params['w_l1'] = 1.0
+user_params['sigmoid_coeff'] = 0.1
+user_params['focal_spot_radius'] = 10
+user_params['enable_random_init'] = False
+user_params['enable_debug'] = False
+user_params['enable_print'] = True
+user_params['enable_timing'] = True
+
+# Logging parameters.
+user_params['enable_logging'] = True
+user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'
+user_params['log_filename_extension'] = '.txt'
+
+def loss_function(h, params):
+    
+    # Generate permittivity and permeability distributions.
+    ER_t, UR_t = solver_metasurface.generate_layered_metasurface(h, params)
+
+    # Simulate the system.
+    outputs = solver.simulate_allsteps(ER_t, UR_t, params)
+    
+    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.
+    r = params['focal_spot_radius']
+    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]
+    focal_plane = solver.propagate(params['input'] * field, params['propagator'], params['upsample'])
+    index = (params['pixelsX'] * params['upsample']) // 2
+    l1 = tf.reduce_sum(tf.abs(focal_plane[0, index-r:index+r, index-r:index+r]))
+
+    # Final loss: (negative) field intensity at focal point + field intensity elsewhere.
+    return -params['w_l1']*l1
+
+# Set loss function.
+user_params['loss_function'] = loss_function
+    
+# Optimize.
+h, loss, params, focal_plane = solver_metasurface.optimize_device(user_params)
+
+gpu_memory_final = utils.gpu_memory_info()
+gpu_memory_used = [gpu_memory_final[1][0] - gpu_memory_init[1][0], gpu_memory_final[1][1] - gpu_memory_init[1][1]]
+
+with open('tf_mem.txt', 'a', encoding=""utf-8"") as f:
+    read_data = f.write(str(gpu_memory_used))","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/farfield_hyperparameter_search_pt.py",".py","597","21","import subprocess as sp
+import itertools
+
+param_grid = {'N': [100],
+              'sigmoid_update': [5.0, 10.0, 20.0],
+              'learning_rate': [4E-1, 8E-1, 1.2],
+              'initial_height': [0,1,2,3,4,5]}
+
+hp_grid = param_grid.values()
+hp_names = param_grid.keys()
+
+# Iterate over the grid.
+for hyperparams in itertools.product(*hp_grid):
+    
+    print('\nTrying hyperparameters: ' + str(list(hp_names)))
+    print(hyperparams)
+    
+    # Run a single optimization in a subprocess.
+    args = ['python3', 'farfield_optimization_pt.py'] + [str(h) for h in hyperparams]
+    sp.run(args)
+","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/nearfield_hyperparameter_search_pt.py",".py","597","21","import subprocess as sp
+import itertools
+
+param_grid = {'N': [50],
+              'sigmoid_update': [10.0],
+              'learning_rate': [0.6, 0.7, 0.8, 0.9, 1.0],
+              'initial_height': [3]}
+
+hp_grid = param_grid.values()
+hp_names = param_grid.keys()
+
+# Iterate over the grid.
+for hyperparams in itertools.product(*hp_grid):
+    
+    print('\nTrying hyperparameters: ' + str(list(hp_names)))
+    print(hyperparams)
+    
+    # Run a single optimization in a subprocess.
+    args = ['python3', 'nearfield_optimization_pt.py'] + [str(180)] + [str(h) for h in hyperparams]
+    sp.run(args)
+","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/farfield_optimization_pt.py",".py","3052","94","# Choose which device to use.
+use_GPU = True
+
+# Import device utils.
+import sys
+import os
+sys.path.append('./src/')
+sys.path.append('./rcwa_pt/src/')
+import utils
+
+if use_GPU:
+    # Configure GPUs.
+    utils.config_gpu_memory_usage()
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import solver_pt
+import solver_metasurface_pt
+
+# Initialize parameters.
+user_params = {}
+
+# Tunable parameters.
+# These are the values used if hyperparameter grid search is disabled.
+user_params['N'] = int(sys.argv[1])
+user_params['sigmoid_update'] = float(sys.argv[2])
+user_params['learning_rate'] = float(sys.argv[3])
+user_params['initial_height'] = int(sys.argv[4])
+
+user_params['parameter_string'] = 'N' + str(user_params['N']) \
+    + '-sigmoid_update' + str(user_params['sigmoid_update']) \
+    + '-learning_rate' + str(user_params['learning_rate']) \
+    + '-initial_height' + str(user_params['initial_height'])
+
+# Source parameters.
+user_params['wavelengths'] = [158.0]
+user_params['thetas'] = [0.0]
+user_params['phis'] = [0.0]
+user_params['pte'] = [1.0]
+user_params['ptm'] = [0.0]
+
+# Device parmeters.
+user_params['pixelsX'] = 180
+user_params['pixelsY'] = user_params['pixelsX']
+user_params['erd'] = 11.9
+user_params['ers'] = user_params['erd']
+user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 250.0]
+user_params['Lx'] = 5000.0 / user_params['pixelsX']
+user_params['Ly'] = user_params['Lx']
+user_params['f'] = 30000000.0 # Focal distance (nm)
+
+# Solver parameters.
+user_params['PQ'] = [3,3]
+user_params['upsample'] = 11
+
+# Problem parameters.
+user_params['w_l1'] = 1.0
+user_params['sigmoid_coeff'] = 1.0
+user_params['focal_spot_radius'] = 10
+user_params['enable_random_init'] = False
+user_params['enable_debug'] = False
+user_params['enable_print'] = True
+user_params['enable_timing'] = True
+
+# Logging parameters.
+user_params['enable_logging'] = True
+user_params['log_filename_prefix'] = './results/farfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'
+user_params['log_filename_extension'] = '.txt'
+
+def loss_function(h, params):
+    
+    # Generate permittivity and permeability distributions.
+    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)
+
+    # Simulate the system.
+    outputs = solver_pt.simulate(ER_t, UR_t, params)
+    
+    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.
+    r = params['focal_spot_radius']
+    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]
+    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])
+    index = (params['pixelsX'] * params['upsample']) // 2
+    l1 = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]))
+
+    # Final loss: (negative) field intensity at focal point + field intensity elsewhere.
+    return -params['w_l1']*l1
+
+# Set loss function.
+user_params['loss_function'] = loss_function
+    
+# Optimize.
+h, loss, params, focal_plane = solver_metasurface_pt.optimize_device(user_params)","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/nearfield_tf_vs_pt.py",".py","668","28","import subprocess as sp
+import itertools
+import time
+
+use_pt = True
+
+t = []
+for p in range(50,180):
+    
+    print('p: ' + str(p))
+    
+    # Measure.
+    time_start = time.time()
+    
+    if use_pt:
+        # Run a single optimization in a subprocess (pt).
+        args = ['python3', 'nearfield_optimization_pt.py'] + [str(p), str(0), str(10.0), str(0.8), str(0)]
+        sp.run(args)
+    
+    else:
+        # Run a single optimization in a subprocess (tf).
+        args = ['python3', 'nearfield_optimization_tf.py'] + [str(p), str(0), str(10.0), str(0.8), str(0)]
+        sp.run(args)
+    
+    time_end = time.time()
+    t.append(time_end - time_start)
+    
+print(t)","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/nearfield_gpu_vs_cpu.py",".py","417","21","import subprocess as sp
+import itertools
+import time
+
+t = []
+for p in range(10,180):
+    
+    print('p: ' + str(p))
+    
+    # Time.
+    time_start = time.time()
+    
+    # Run a single optimization in a subprocess.
+    args = ['python3', 'nearfield_optimization_pt.py'] + [str(p), str(0), str(10.0), str(0.8), str(0)]
+    sp.run(args)
+    
+    time_end = time.time()
+    t.append(time_end - time_start)
+   
+print(t)
+","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/nearfield_optimization_pt.py",".py","3046","94","# Choose which device to use.
+use_GPU = True
+
+# Import device utils.
+import sys
+import os
+sys.path.append('./src/')
+sys.path.append('./rcwa_pt/src/')
+import utils
+
+if use_GPU:
+    # Configure GPUs.
+    utils.config_gpu_memory_usage()
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import solver_pt
+import solver_metasurface_pt
+
+# Initialize parameters.
+user_params = {}
+
+# Tunable parameters.
+# These are the values used if hyperparameter grid search is disabled.
+user_params['N'] = int(sys.argv[1])
+user_params['sigmoid_update'] = float(sys.argv[2])
+user_params['learning_rate'] = float(sys.argv[3])
+user_params['initial_height'] = int(sys.argv[4])
+
+user_params['parameter_string'] = 'N' + str(user_params['N']) \
+    + '-sigmoid_update' + str(user_params['sigmoid_update']) \
+    + '-learning_rate' + str(user_params['learning_rate']) \
+    + '-initial_height' + str(user_params['initial_height'])
+
+# Source parameters.
+user_params['wavelengths'] = [120.0]
+user_params['thetas'] = [0.0]
+user_params['phis'] = [0.0]
+user_params['pte'] = [1.0]
+user_params['ptm'] = [0.0]
+
+# Device parmeters.
+user_params['pixelsX'] = 180
+user_params['pixelsY'] = user_params['pixelsX']
+user_params['erd'] = 11.9
+user_params['ers'] = user_params['erd']
+user_params['L'] = [50.0, 50.0, 50.0, 50.0, 50.0, 950.0]
+user_params['Lx'] = 5000.0 / user_params['pixelsX']
+user_params['Ly'] = user_params['Lx']
+user_params['f'] = 0.0 # Focal distance (nm)
+
+# Solver parameters.
+user_params['PQ'] = [3,3]
+user_params['upsample'] = 11
+
+# Problem parameters.
+user_params['w_l1'] = 1.0
+user_params['sigmoid_coeff'] = 0.1
+user_params['focal_spot_radius'] = 10
+user_params['enable_random_init'] = False
+user_params['enable_debug'] = False
+user_params['enable_print'] = True
+user_params['enable_timing'] = True
+
+# Logging parameters.
+user_params['enable_logging'] = True
+user_params['log_filename_prefix'] = './results/nearfield-' + str(user_params['pixelsX']) + 'x' + str(user_params['pixelsY']) + '-'
+user_params['log_filename_extension'] = '.txt'
+
+def loss_function(h, params):
+    
+    # Generate permittivity and permeability distributions.
+    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)
+
+    # Simulate the system.
+    outputs = solver_pt.simulate(ER_t, UR_t, params)
+    
+    # First loss term: maximize sum of electric field magnitude within some radius of the desired focal point.
+    r = params['focal_spot_radius']
+    field = outputs['ty'][:, :, :, np.prod(params['PQ']) // 2, 0]
+    focal_plane = solver_pt.propagate(params['input'] * field, params['propagator'], params['upsample'])
+    index = (params['pixelsX'] * params['upsample']) // 2
+    l1 = torch.sum(torch.abs(focal_plane[0, index-r:index+r, index-r:index+r]))
+
+    # Final loss: (negative) field intensity at focal point + field intensity elsewhere.
+    return -params['w_l1']*l1
+
+# Set loss function.
+user_params['loss_function'] = loss_function
+    
+# Optimize.
+h, loss, params, focal_plane = solver_metasurface_pt.optimize_device(user_params)","Python"
+"Metamaterial","deveringham/metalens_optimization","scripts/plot_generation.ipynb",".ipynb","157003","204","{
+ ""cells"": [
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 2,
+   ""id"": ""36dafe54"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""name"": ""stderr"",
+     ""output_type"": ""stream"",
+     ""text"": [
+      ""2023-03-17 16:05:26.510015: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n"",
+      ""To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"",
+      ""2023-03-17 16:05:26.657815: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 16:05:26.657836: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"",
+      ""2023-03-17 16:05:27.308552: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 16:05:27.308595: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n"",
+      ""2023-03-17 16:05:27.308600: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n""
+     ]
+    }
+   ],
+   ""source"": [
+    ""import sys\n"",
+    ""import os\n"",
+    ""sys.path.append('../src/')\n"",
+    ""sys.path.append('../rcwa_pt/src/')\n"",
+    ""import utils\n"",
+    ""import torch\n"",
+    ""import numpy as np\n"",
+    ""import matplotlib.pyplot as plt\n"",
+    ""import matplotlib.colors as colors\n"",
+    ""import matplotlib.animation as animation\n"",
+    ""import time\n"",
+    ""import solver_metasurface_pt""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 3,
+   ""id"": ""ae13364c"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""t_gpu = [9.352486371994019, 9.365387439727783, 9.222445249557495, 9.26182770729065, 9.260477304458618, 9.295940160751343, 9.376668930053711, 9.304535150527954, 9.42597246170044, 9.590601921081543, 9.854676008224487, 9.348648309707642, 9.66896367073059, 9.51150131225586, 9.955518245697021, 10.298295974731445, 10.180099487304688, 10.016309261322021, 9.919437170028687, 10.27823543548584, 10.58505916595459, 10.0498206615448, 9.966135263442993, 10.161046266555786, 10.13900899887085, 10.764947652816772, 10.89242172241211, 10.957701683044434, 10.475296258926392, 10.484134674072266, 11.030773878097534, 11.011009931564331, 11.1758131980896, 10.90687370300293, 11.055004596710205, 11.15333104133606, 11.614664077758789, 11.8080575466156, 11.987097263336182, 11.521169900894165, 11.669353485107422, 11.795734167098999, 12.268195629119873, 12.223302364349365, 12.489853858947754, 12.379245519638062, 12.38496208190918, 12.595110177993774, 12.88446307182312, 12.94929575920105, 12.623219728469849, 12.984482049942017, 13.046929359436035, 13.477948904037476, 13.883325338363647, 13.509201765060425, 13.557996988296509, 13.637295722961426, 13.834070444107056, 14.54967451095581, 14.087002754211426, 14.021963596343994, 14.925894975662231, 14.764579772949219, 15.132670164108276, 14.964730739593506, 15.11035966873169, 15.274447679519653, 15.873709917068481, 15.696542501449585, 15.73154616355896, 15.957948684692383, 17.10550808906555, 16.500210285186768, 16.74093198776245, 17.25803279876709, 18.102582693099976, 17.465776681900024, 17.731990337371826, 18.173645496368408, 18.269155740737915, 18.23880624771118, 18.36432695388794, 18.5979220867157, 19.06343173980713, 19.084798574447632, 19.43612504005432, 20.055106163024902, 20.455755949020386, 20.21488857269287, 20.762585878372192, 21.070176601409912, 21.335463285446167, 21.428975582122803, 21.417450428009033, 21.394421577453613, 21.993349313735962, 22.435445070266724, 22.561158418655396, 22.6764018535614, 23.251611948013306, 23.245906591415405, 23.190724849700928, 23.702364921569824, 24.05437994003296, 23.95227837562561, 24.916240215301514, 25.35464596748352, 24.843908548355103, 25.004029512405396, 25.856048822402954, 25.528083562850952, 26.202796459197998, 26.60081958770752, 26.503896474838257, 26.676666498184204, 27.14421582221985, 27.68572735786438, 27.98321294784546, 28.275208234786987, 28.04175305366516, 28.95330500602722, 28.562082767486572, 29.185215711593628, 30.03480553627014, 29.671706914901733, 29.82651686668396, 30.423075199127197, 30.298665761947632, 31.329976558685303, 32.394202709198, 32.25480318069458, 33.50320911407471, 33.509958267211914, 34.066564321517944, 34.6598424911499, 34.97721743583679, 35.65391397476196, 35.84573245048523, 35.67497253417969, 36.229363441467285, 36.86257028579712, 37.64646339416504, 38.276026010513306, 38.74083185195923, 38.99017262458801, 39.30568504333496, 39.897727727890015, 40.123730421066284, 40.38779592514038, 41.330145597457886, 41.53237318992615, 42.49771308898926, 41.69599437713623, 42.82122492790222, 42.43301439285278, 43.845197916030884, 44.26045894622803, 44.92796874046326, 44.9420907497406, 45.432116746902466, 44.93561053276062, 46.05262231826782, 46.2500901222229, 46.594477891922, 47.48746061325073, 46.988141775131226, 48.0954692363739, 48.74428915977478, 49.06060171127319]\n"",
+    ""t_cpu = [4.259648561477661, 3.906322956085205, 4.094226360321045, 4.102000951766968, 4.298899173736572, 4.422224044799805, 4.585261583328247, 4.739826440811157, 5.01600193977356, 5.056399822235107, 5.328705549240112, 5.385349750518799, 5.722073554992676, 6.093722820281982, 6.26479434967041, 6.501919746398926, 6.790149450302124, 7.018880367279053, 7.371773958206177, 7.597033977508545, 7.780315399169922, 8.147405624389648, 8.69942831993103, 8.780673742294312, 9.09121322631836, 9.500485897064209, 9.922213077545166, 10.262395143508911, 10.759379148483276, 11.261409997940063, 11.478652477264404, 11.673210144042969, 12.299792766571045, 12.473167896270752, 13.16890811920166, 13.832477331161499, 14.108141899108887, 14.512554168701172, 15.311897039413452, 15.660157680511475, 16.394815921783447, 16.66675615310669, 17.329363584518433, 17.865257740020752, 18.621787071228027, 18.985862731933594, 19.53387212753296, 20.051693201065063, 21.00439143180847, 21.079557418823242, 22.00771713256836, 22.526651859283447, 23.485133409500122, 23.57141423225403, 24.58724617958069, 25.076687812805176, 26.499017477035522, 26.417564153671265, 27.19490885734558, 28.140212535858154, 28.72412371635437, 29.071702480316162, 30.41608738899231, 31.013919591903687, 31.57890033721924, 32.55226445198059, 33.15858173370361, 34.29873776435852, 34.9839129447937, 35.781373262405396, 36.8708872795105, 37.7184476852417, 38.67619490623474, 39.160152196884155, 40.19017958641052, 41.7218759059906, 42.444307804107666, 44.08834171295166, 45.250645875930786, 45.229395627975464, 45.72807550430298, 46.4632887840271, 48.00239896774292, 48.41131782531738, 49.525784969329834, 50.55456757545471, 51.70102095603943, 53.14192843437195, 53.96801280975342, 55.1891884803772, 55.505889892578125, 56.714627742767334, 57.508577823638916, 58.61093759536743, 59.53227686882019, 61.63502073287964, 62.65109848976135, 63.86270594596863, 64.8605272769928, 65.67518734931946, 67.61428189277649, 68.65429520606995, 69.0892436504364, 69.41619801521301, 72.2787458896637, 73.9354407787323, 74.85191297531128, 76.95911765098572, 78.68664836883545, 78.83137488365173, 80.34922194480896, 81.45632934570312, 82.16357803344727, 83.1166443824768, 85.24173045158386, 87.15986919403076, 87.08471465110779, 89.20514416694641, 89.97305393218994, 91.33324575424194, 92.65137076377869, 93.60176515579224, 95.86598420143127, 97.0622980594635, 98.74591135978699, 99.95398616790771, 102.17806720733643, 104.10530972480774, 104.09795069694519, 105.58855199813843, 107.82662057876587, 110.08780598640442, 110.78830456733704, 112.63179135322571, 114.80755376815796, 115.6572196483612, 116.5863745212555, 119.50833225250244, 119.55228686332703, 121.69979238510132, 124.52626299858093, 126.65430235862732, 127.36136245727539, 127.896977186203, 129.7103500366211, 131.352952003479, 133.29086637496948, 134.75659489631653, 137.31118893623352, 137.96848058700562, 139.80468082427979, 141.678302526474, 144.33139395713806, 145.38346362113953, 147.72998905181885, 148.37241005897522, 150.6378312110901, 153.02787852287292, 153.5606827735901, 156.49497723579407, 157.45970273017883, 161.33135771751404, 160.84046339988708, 162.23117589950562, 165.30418229103088, 166.17024278640747, 168.6709451675415, 171.7683982849121, 172.06109929084778, 173.60751032829285]\n"",
+    ""x = range(10,len(t_gpu)+10)\n"",
+    ""plt.plot(x, t_gpu, label='GPU')\n"",
+    ""plt.plot(x, t_cpu, label='CPU')\n"",
+    ""plt.xlabel('Problem Size (Lens Resolution)')\n"",
+    ""plt.ylabel('Time to Perform One\\nOptimization Iteration (s)')\n"",
+    ""plt.legend()\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 4,
+   ""id"": ""84b1bdc9"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""mem_pt = [[0, 2301],[0, 2311],[0, 2343],[0, 2381],[0, 2389],[0, 2431],[0, 2437],[0, 2531],[0, 2577],[0, 2581],[0, 2579],[0, 2625],[0, 2653],[0, 2681],[0, 2751],[0, 2779],[0, 2835],[0, 2857],[0, 2941],[0, 2977],[0, 3047],[0, 3091],[0, 3141],[0, 3209],[0, 3263],[0, 3277],[0, 3413],[0, 3429],[0, 3497],[0, 3509],[0, 3645],[0, 3717],[0, 3755],[0, 3809],[0, 4023],[0, 4041],[0, 4109],[0, 4179],[0, 4205],[0, 4269],[0, 4359],[0, 4663],[0, 4697],[0, 4785],[0, 4853],[0, 4905],[0, 5117],[0, 5101],[0, 5193],[0, 5257],[0, 5317],[0, 5433],[0, 5495],[0, 5651],[0, 5735],[0, 5835],[0, 5889],[0, 5995],[0, 6057],[0, 6319],[0, 6393],[0, 6483],[0, 6557],[0, 6647],[0, 6737],[0, 6973],[0, 7053],[0, 7201],[0, 7293],[0, 7369],[0, 7633],[0, 7709],[0, 7843],[0, 7949],[0, 8031],[0, 8267],[0, 8487],[0, 8503],[0, 8595],[0, 8965],[0, 8983],[0, 9069],[0, 9309],[0, 9309],[0, 9555],[0, 9791],[0, 9803],[0, 9945],[0, 10209],[0, 10347],[0, 10431],[0, 10589],[0, 10849],[0, 11107],[0, 11093],[0, 11229],[0, 11509],[0, 11653],[0, 11927],[0, 11901],[0, 12203],[0, 12363],[0, 12463],[0, 12907],[0, 12927],[0, 13017],[0, 13193],[0, 13483],[0, 13651],[0, 13761],[0, 14071],[0, 14227],[0, 14547],[0, 14705],[0, 14793],[0, 14969],[0, 15119],[0, 15431],[0, 15591],[0, 15767],[0, 16067],[0, 16249],[0, 16389],[0, 16719],[0, 16869],[0, 17051],[0, 17345],[0, 17529],[0, 17689],[0, 18013],[0, 18177],[0, 18357],[0, 18655],[0, 18837],[0, 19003],[0, 19379],[0, 19789],[0, 19879],[0, 20291],[0, 20211],[0, 20589],[0, 20753],[0, 20937],[0, 21245],[0, 21483],[0, 22069],[0, 21985],[0, 22217],[0, 22535],[0, 22707],[0, 23363],[0, 23267],[0, 23497],[0, 23815],[0, 24005],[0, 24679],[0, 24567],[0, 24799],[0, 25121],[0, 25361],[0, 26003],[0, 25913],[0, 26105],[0, 26817],[0, 26715],[0, 27057],[0, 27635],[0, 27625],[0, 27863]]\n"",
+    ""#mem_tf = [[305, 967],[305, 967],[305, 967],[305, 1031],[305, 1031],[305, 1031],[305, 1031],[305, 1031],[305, 1031],[305, 1159],[305, 1159],[305, 1159],[305, 1159],[305, 1159],[305, 1159],[305, 1159],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1415],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1927],[305, 1939],[305, 1939],[305, 1939],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2963],[305, 2971],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 5019],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9131],[305, 9163],[305, 9163],[305, 9163],[305, 9163],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355],[305, 17355]]\n"",
+    ""mem_tf = [[0, 2851],[0, 3117],[0, 3251],[0, 3461],[0, 3775],[0, 4061],[0, 4219],[0, 4627],[0, 4791],[0, 5071],[0, 5241],[0, 5565],[0, 5735],[0, 5931],[0, 6335],[0, 6609],[0, 7051],[0, 7461],[0, 7759],[0, 8201],[0, 8499],[0, 8967],[0, 9485],[0, 9763],[0, 10273],[0, 10781],[0, 11119],[0, 11687],[0, 12201],[0, 12853],[0, 13235],[0, 13847],[0, 14415],[0, 15021],[0, 15709],[0, 16421],[0, 16729],[0, 17429],[0, 18117],[0, 18791],[0, 19483],[0, 20255],[0, 20905],[0, 21749],[0, 22411],[0, 23159],[0, 24283],[0, 24999],[0, 25767],[0, 26681],[0, 27427],[0, 28315],[0, 29489],[0, 30365],[0, 31293],[0, 32171],[0, 33333],[0, 34369],[0, 35349],[0, 36545],[0, 37503],[0, 38455],[0, 39737],[0, 40833],[0, 42135],[0, 43169],[0, 44653],[0, 45703],[0, 47049],[0, 48133]]\n"",
+    ""mem_pt_sum = [sum(m) for m in mem_pt]\n"",
+    ""mem_tf_sum = [sum(m) for m in mem_tf]\n"",
+    ""x_pt = range(10,len(mem_pt_sum)+10)\n"",
+    ""x_tf = range(10,len(mem_tf_sum)+10)\n"",
+    ""plt.plot(x_pt, mem_pt_sum, label='PyTorch')\n"",
+    ""plt.plot(x_tf, mem_tf_sum, label='TensorFlow')\n"",
+    ""plt.xlabel('Problem Size (Lens Resolution)')\n"",
+    ""plt.ylabel('Maximum GPU Memory Usage (MiB)')\n"",
+    ""plt.legend()\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 5,
+   ""id"": ""51a3d485"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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"",
+      ""text/plain"": [
+       ""<Figure size 640x480 with 1 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""c = 10\n"",
+    ""x_sig = torch.tensor(np.linspace(0,3,100))\n"",
+    ""plt.plot(x_sig, 1+torch.sigmoid(c*(x_sig-0.5))*(11.9-1))\n"",
+    ""plt.plot(x_sig, 1+torch.sigmoid(c*(x_sig-1.5))*(11.9-1))\n"",
+    ""plt.plot(x_sig, 1+torch.sigmoid(c*(x_sig-2.5))*(11.9-1))\n"",
+    ""plt.show()""
+   ]
+  },
+  {
+   ""cell_type"": ""code"",
+   ""execution_count"": 6,
+   ""id"": ""b4d9c169"",
+   ""metadata"": {},
+   ""outputs"": [
+    {
+     ""data"": {
+      ""image/png"": 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+      ""text/plain"": [
+       ""<Figure size 2000x2000 with 5 Axes>""
+      ]
+     },
+     ""metadata"": {},
+     ""output_type"": ""display_data""
+    }
+   ],
+   ""source"": [
+    ""params = {}\n"",
+    ""params['pixelsX'] = 25\n"",
+    ""params['pixelsY'] = params['pixelsX']\n"",
+    ""params['batchSize'] = 1\n"",
+    ""params['Nlay'] = 3\n"",
+    ""params['Nx'] = 16\n"",
+    ""params['Ny'] = params['Nx']\n"",
+    ""params['enable_random_init'] = True\n"",
+    ""params['eps_min'] = 1.0\n"",
+    ""params['eps_max'] = 11.9\n"",
+    ""params['urd'] = 1.0\n"",
+    ""params['ers'] = params['eps_max']\n"",
+    ""\n"",
+    ""params['sigmoid_coeff'] = 0.1\n"",
+    ""params['initial_height'] = 0\n"",
+    ""\n"",
+    ""h = torch.autograd.Variable(solver_metasurface_pt.init_layered_metasurface(params, initial_height=params['initial_height']),\n"",
+    ""                                requires_grad=True)\n"",
+    ""\n"",
+    ""# Display the permittiviy profile.\n"",
+    ""norm = colors.Normalize(vmin=params['eps_min'], vmax=params['eps_max'])\n"",
+    ""images=[]\n"",
+    ""fig, axes = plt.subplots(1, 5, figsize=(20,20))\n"",
+    ""for l in range(0,5):\n"",
+    ""    params['sigmoid_coeff'] += 3.0\n"",
+    ""    ER_t, UR_t = solver_metasurface_pt.generate_layered_metasurface(h, params)\n"",
+    ""    img = torch.permute(torch.squeeze(ER_t[0,:,:,1,:,:]),(0,2,1,3))\n"",
+    ""    img = torch.real(torch.reshape(img, (params['pixelsX']*params['Nx'],params['pixelsY']*params['Ny'])))\n"",
+    ""    images.append(axes[l].matshow(img.detach().cpu().numpy(), interpolation='nearest'))\n"",
+    ""    axes[l].get_xaxis().set_visible(False)\n"",
+    ""    axes[l].get_yaxis().set_visible(False)\n"",
+    ""    images[l].set_norm(norm)\n"",
+    ""\n"",
+    ""#fig.colorbar(images[0], ax=axes, orientation='horizontal', fraction=.1)\n"",
+    ""plt.show()""
+   ]
+  }
+ ],
+ ""metadata"": {
+  ""kernelspec"": {
+   ""display_name"": ""Python 3 (ipykernel)"",
+   ""language"": ""python"",
+   ""name"": ""python3""
+  },
+  ""language_info"": {
+   ""codemirror_mode"": {
+    ""name"": ""ipython"",
+    ""version"": 3
+   },
+   ""file_extension"": "".py"",
+   ""mimetype"": ""text/x-python"",
+   ""name"": ""python"",
+   ""nbconvert_exporter"": ""python"",
+   ""pygments_lexer"": ""ipython3"",
+   ""version"": ""3.10.6""
+  }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 5
+}
+","Unknown"
+"Metamaterial","THzbiophotonics/Fit-TDS","old/create_env.sh",".sh","326","10","#!/bin/bash
+sudo apt install build-essential libssl-dev libffi-dev python3-dev
+sudo apt install -y python3-venv
+python3 -m venv py3-env
+source ./py3-env/bin/activate
+pip install --upgrade pip
+py3-env/bin/pip install wheel
+py3-env/bin/pip install -r requirements.txt
+py3-env/bin/pip install git+https://github.com/madebr/pyOpt
+","Shell"
+"Metamaterial","THzbiophotonics/Fit-TDS","old/fit_tds.sh",".sh","78","6","#!/bin/bash
+source ./py3-env/bin/activate
+cd fit_tds
+python fit_TDSg.py
+cd ..
+","Shell"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/fit_TDSm.py",".py","19493","416","#!/usr/bin/python
+# -*- coding: latin-1 -*-
+
+
+import numpy as np   ## Library to simplify the linear algebra calculations
+
+
+
+j = 1j
+c = 2.998e8
+h = 6.62607015E-34
+k= 1.38064852E-23
+
+
+
+class Model:
+    def __init__(self):
+        self.name = """"
+        self.label = """"                          #label used to ask for the number of terms of the given model
+        self.explanation = """"                    #Text displayed in the reference box
+        self.isCumulative = False                #Can there be several terms with this model?
+        self.variableNames = []                  
+        self.variableUnits =  []
+        self.variableDescriptions = []           #if isCumulative=True, the index of the term will be added to the descriptions (\n is added anyway)
+        self.invalidNumberMessage = """"           #Message to show if the number of term entered is invalid
+
+    def epsilon(self,eps,w,paramList):
+        raise NotImplementedError()              #raises an exception if you use a model without implementing its epsilon method
+
+    def gauss(self,w,sigma):
+        return np.exp(-((w))**2/(2*sigma**2))/(np.sqrt(2*np.pi)*sigma)
+
+    def lor(self,w,chi,w0,gamma):
+        return (chi*w0)/(w0**2+j*gamma*w-w**2) #normalis� par w0 car int�gr�
+
+    def debye(self,w,chi,tau): 
+        return chi/(1+j*w*tau)
+
+    def lnl(self,nu,sigma):
+        return np.exp(-(np.log(nu))**2/(2*sigma**2))*1/(np.sqrt(2*np.pi)*sigma*nu)
+
+    def boltzmann(self,nu,nu0,T):
+        return (1-np.exp(-(nu*h)/(k*T))-((nu)*(h/(k*T)))*np.exp(-(nu)*(h/(k*T)))-1/2*((nu)*(h/(k*T)))**2*np.exp(-(nu)*(h/(k*T))))*np.heaviside(nu-nu0, 1)
+####################################
+######### Material Models ##########
+####################################
+
+class Drude(Model):
+    def __init__(self):
+        self.name = ""Drude""
+        self.label = ""Drude term""
+        self.explanation = ""Drude model depicts the permitivity Epsillon as Eps =Eps_0- Omega_p^2/(Omega^2-j*gamma*omega).""
+        self.isCumulative = False
+        self.variableNames = [""Omega_p"",""gamma""]
+        self.variableUnits =  [""radian/s"",""radian/s""]
+        self.variableDescriptions = [""Drude's Model Plasma frequency"",
+                                      ""Drude damping rate""]
+        self.invalidNumberMessage = """"
+
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        wp = variableDictionary.get(""Omega_p"")
+        gamma = variableDictionary.get(""gamma"")
+        return -wp**2/(1E0+w**2-j*gamma*w)
+
+class Popov(Model):
+    def __init__(self):
+        self.name = ""Popov""
+        self.label = ""Popov term""
+        self.explanation = ""Popov model depicts the permitivity Epsillon as Eps = Eps_0 +[ Delta_epsillon/[1+[[j*Omega*taudef]^-1+[j*Omega*tauosc]^-delta]^-1]].""
+        self.isCumulative = False
+        self.variableNames = ['Delta_Epsilon_Popov', 'Tau_{def}', 'Tau_{osc}', 'delta']     
+        self.variableUnits =  [""dimensionless"", ""s"", ""s"", ""dimensionless""]
+        self.variableDescriptions = [""Oscillator strength of the mode"", 'Time constant of defects', 'Time constant of oscillations', 'delta']
+        self.invalidNumberMessage = """"
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get(""Delta_Epsilon_Popov"")
+        taudef = variableDictionary.get(""Tau_{def}"")
+        tauosc = variableDictionary.get(""Tau_{osc}"")
+        delta = variableDictionary.get(""delta"")
+        return chi/(1E0+1/(np.exp(-np.log(j*w*taudef+j*1e-30))+np.exp(-delta*np.log(j*w*tauosc+j*1e-30))))
+        #return chi/(1E0+((1e-10*1j+j*w*taudef)**(-1)+(1j*1e-10+j*w*tauosc)**(-delta))**(-1))
+
+class Scattering(Model):
+    def __init__(self):
+        self.name = ""Scattering""
+        self.label = ""Scattering""
+        self.explanation = ""Do you want to take into account scattering ?""
+        self.isCumulative = False
+        self.variableNames = [""Beta"",""Scat_freq_min"",""Scat_freq_max""]
+        self.variableUnits =  [""1/m"",""Hz"",""Hz""]
+        self.variableDescriptions = [""Loss coefficient"",
+                                      ""Beginning frequency of scattering"",
+                                      ""Ending frequency of scattering""]
+        self.invalidNumberMessage = """"
+
+    def epsilon(self,eps,w,paramList):        
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        beta = variableDictionary.get(""Beta"")
+        Scat_freq_min = variableDictionary.get(""Scat_freq_min"")
+        Scat_freq_max = variableDictionary.get(""Scat_freq_max"")
+        omega = np.where(w<Scat_freq_max*2*np.pi,w,1e-299)
+        omega = np.where(omega>Scat_freq_min*2*np.pi,omega,1e-299)
+        alpha = beta*(omega/(2*np.pi*1e12))**3
+        n_diff = - j*alpha
+        return n_diff**2+2*np.sqrt(eps)*n_diff
+
+class Lorentz(Model):
+    def __init__(self):
+        self.name = ""Lorentz""
+        self.label = ""Number of Lorentz oscillators""
+        self.explanation = ""Lorentz model depicts the permitivity Epsillon as Eps = Eps_0 +[ Delta_epsillon*Omega_0^2]/[Omega_0^2+j*gamma*Omega-Omega^2].""
+        self.isCumulative = True
+        self.variableNames = ['Delta_Epsillon_Lorentz',
+                               '1/(2pi)*Omega0_Lorentz',
+                               '1/(2pi)*Gamma']
+        self.variableUnits =  [""dimensionless"", ""Hz"",""Hz""]
+        self.variableDescriptions = [""Oscillator strentgh of the mode #"",
+                                      'Frequency of the mode #',
+                                      'Linewidth of the mode #']
+        self.invalidNumberMessage = ""Invalid number of Lorentz Oscillators.""
+
+    def epsilon(self,eps,w,paramList):        
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get('Delta_Epsillon_Lorentz')
+        w0 = variableDictionary.get('1/(2pi)*Omega0_Lorentz')*2*np.pi
+        gamma = variableDictionary.get('1/(2pi)*Gamma')*2*np.pi
+        return chi*w0**2/(w0**2+j*gamma*w-w**2)
+
+class Voigt(Model):
+    def __init__(self):
+        self.name = ""Voigt""
+        self.label = ""Number of Voigt oscillators""
+        self.explanation = ""Voigt profile depicts the permitivity Epsillon as Eps = Eps_0 + Gaussian * Lorentzian.""
+        self.isCumulative = True
+        self.variableNames = [""sigma"",""Chi"",""Nu_0"",""GammaVoigt""]
+        self.variableUnits =  [""dimensionless"",""dimensionless"",""Hz"",""Hz""]
+        self.variableDescriptions = ['Width (sigma of the Gaussian) of the mode #',
+                                      ""Strength of the Lorentzian"", 'Central frequency of the mode #', 
+                                      'Width of the Lorentzian of the mode #'] 
+        self.invalidNumberMessage = ""Invalid number of Voigt Oscillators.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        wneg = -w[::-1]
+        wtot=np.append(wneg,w)
+        dw=w[2]-w[1]
+        chi = variableDictionary.get('Chi')
+        gamma = variableDictionary.get('GammaVoigt')*2*np.pi
+        sigma = variableDictionary.get('sigma')*2*np.pi
+        w0 = variableDictionary.get('Nu_0')*2*np.pi
+        gauss00 = self.gauss(0,sigma)
+        somme0 = self.lor(wtot,chi,w0,gamma)*gauss00
+        norm0 = gauss00/w0
+        if gamma/10 >= dw:
+            step0 = (gamma/(2*np.pi))/10
+        else:
+            step0 = dw/(2*np.pi)
+        count0 = step0            
+        gauss0 = gauss00
+        while gauss0 > gauss00/1000:
+            gauss0 = self.gauss((count0*2*np.pi+w0)-w0,sigma)
+            somme0 = somme0 + (self.lor(wtot,chi,(count0*2*np.pi+w0),gamma)+self.lor(wtot,chi,(-count0*2*np.pi+w0),gamma))*gauss0
+            norm0 = norm0 + 2*gauss0/((count0*2*np.pi+w0))
+            count0 = count0 + step0
+        C0 = somme0/norm0
+        return C0[len(wtot)-len(w):len(wtot)]
+        
+class LogN(Model):
+    def __init__(self):
+        self.name = ""LogN""
+        self.label = ""Number of log-normal distributions""
+        self.explanation = ""Log-Normal distribution depicts the permitivity Epsillon as Eps = Eps_0 + Log-Normal * Debye.""
+        self.isCumulative = True
+        self.variableNames = [""sigma_LN"",""Chi_LN"",""tau_LN""]
+        self.variableUnits =  [""Hz"",""dimensionless"",""s""]
+        self.variableDescriptions = ['Width of the Log-normal of the mode #','Oscillator strentgh of the mode #','Time constant of the mode #']
+
+        self.invalidNumberMessage = ""Invalid number of Log-normal distributions.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        dw=w[2]-w[1]
+        wneg = -w[::-1]
+        wtot=np.append(wneg,w)
+        chi = variableDictionary.get('Chi_LN')
+        sigma = variableDictionary.get('sigma_LN')*2*np.pi
+        tau = variableDictionary.get('tau_LN')
+        nu0=1/(2*np.pi*tau)
+        
+        lnl11 = self.lnl(nu0,0,sigma)
+        somme1 = self.debye(wtot,chi,tau)*lnl11
+        norm1 = lnl11
+        step1 = dw/(2*np.pi)
+        count1 = step1
+        lnl1 = lnl11
+        while lnl1 > lnl11/1000:
+            lnl1 = self.lnl((count1+nu0),0,sigma)
+            somme1 = somme1 + (self.debye(wtot,chi,1/((count1+nu0)*2*np.pi)))*lnl1#+debye(wtot,chi,1/((-count1+nu0)*2*np.pi)))*lnl1
+            norm1 = norm1 + 2*lnl1
+            count1 = count1 + step1
+        C1 = somme1/norm1
+        return C1[len(wtot)-len(w):len(wtot)]
+
+class Debye(Model):
+    def __init__(self):
+        self.name = ""Debye""
+        self.label = ""Number of Debye oscillators""
+        self.explanation = ""Debye model depicts the permitivity Epsillon as Eps = Eps_0 +[ Delta_epsillon/[1+j*Omega*tau].""
+        self.isCumulative = True
+        self.variableNames = [""Delta_Epsillon_Debye"",""tau""]
+        self.variableUnits =  [""dimensionless"", ""s""]     
+        self.variableDescriptions = [""Oscillator strentgh of the mode #"", 'Time constant #']
+
+        self.invalidNumberMessage = ""Invalid number of Debye Oscillators.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get('Delta_Epsillon_Debye')
+        tau = variableDictionary.get('tau')
+        return chi/(1+j*w*tau)
+
+
+class HN(Model):
+    def __init__(self):
+        self.name = ""HN""
+        self.label = ""Number of Havriliak-Negami terms""
+        self.explanation = ""Havriliak-Negami model depicts the permitivity Epsilon as Eps = Eps_0 + [ Delta_epsillon/[1+(j*Omega*tau)^alpha]^beta.""
+        self.isCumulative = True
+        self.variableNames = [""Delta_Epsillon_HN"",""tau_HN"",""Alpha"",""Beta_HN""]
+        self.variableUnits =  [""dimensionless"", ""s"", ""dimensionless"", ""dimensionless""]      
+        self.variableDescriptions = [""Oscillator strentgh of the mode #"", 'Time constant #', ""Alpha #"", ""Beta #""]
+        self.invalidNumberMessage = ""Invalid number of Havriliak-Negami terms.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get('Delta_Epsillon_HN')
+        tau = variableDictionary.get('tau_HN')
+        alpha = (variableDictionary.get('Alpha'))
+        beta = (variableDictionary.get('Beta_HN'))
+        eps = chi/(1+(j*w*tau)**alpha)**beta
+        return eps
+
+class Boltzmann(Model):
+    def __init__(self):
+        self.name = ""Boltzmann_continuum""
+        self.label = ""Number of Boltzmann terms""
+        self.explanation = ""Boltzmann continuum model depicts the permitivity Epsilon as a convolution of a Boltzmann distribution and a Lorentz term.""
+        self.isCumulative = True
+        self.variableNames = [""Chi_Boltzmann"",""nu0_Boltzmann"",""Gamma_Boltzmann"",""Temp_Boltzmann""]
+        self.variableUnits =  [""dimensionless"", ""Hz"", ""Hz"", ""K""]      
+        self.variableDescriptions = [""Oscillator strentgh of the mode #"", 'Frequency of the mode #', 'Linewidth of the mode #', ""Temperature of the mode #""]
+        self.invalidNumberMessage = ""Invalid number of Boltzmann continuum terms.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get('Chi_Boltzmann')
+        nu0 = variableDictionary.get('nu0_Boltzmann') #2pi ?
+        gamma = variableDictionary.get('Gamma_Boltzmann')*2*np.pi
+        T = variableDictionary.get('Temp_Boltzmann')
+        wneg = -w[::-1]
+        wtot=np.append(wneg,w)
+        dw = w[2]-w[1]
+        nutot=wtot/(2*np.pi)
+        somme = np.zeros(len(nutot))
+        norm = np.zeros(len(nutot))
+        if gamma/10 >= dw:
+            step = (gamma/(2*np.pi))/10
+        else:
+            step = dw/(2*np.pi)
+        count = nu0
+        while count < nutot[-1]:
+            somme = somme + self.lor(wtot,chi,count*2*np.pi,gamma)*self.boltzmann(count,nu0,T)
+            norm = norm + self.boltzmann(count,nu0,T)/(count*2*np.pi)
+            count = count + step
+        C2 = somme/norm
+        eps = C2[len(wtot)-len(w):len(wtot)]
+#        chi = variableDictionary.get('Chi_Boltzmann')
+#        nu0 = variableDictionary.get('nu0_Boltzmann')
+#        gamma = np.log(variableDictionary.get('Gamma_Boltzmann'))
+#        T = np.log(variableDictionary.get('Temp_Boltzmann'))
+#        wneg = -w[::-1]
+#        wtot=np.append(wneg,w)
+#        dw = w[2]-w[1]
+#        nutot=wtot/(2*np.pi)
+#        somme = np.zeros(len(nutot))
+#        norm = np.zeros(len(nutot))
+#        if gamma/10 >= dw:
+#            step = (gamma/(2*np.pi))/10
+#        else:
+#            step = dw/(2*np.pi)
+#        count = nu0
+#        while count < nutot[-1]:
+#            somme = somme + self.lor(wtot,chi,count*2*np.pi,gamma)/(count*2*np.pi)*self.boltzmann(count,nu0,T)
+#            norm = norm + self.boltzmann(count,nu0,T)/(count*2*np.pi)
+#            count = count + step
+#        C2 = somme/norm
+#        return C2[len(wtot)-len(w):len(wtot)]
+        return eps
+    
+class Roccard(Model):
+    def __init__(self):
+        self.name = ""Roccard""
+        self.label = ""Number of Roccard terms""
+        self.explanation = ""Roccard model depicts the permitivity Epsillon as Eps = Eps_0 +[ Delta_epsillon/[(1+j*Omega*tau1).(1+j*Omega*tau2)].""
+        self.isCumulative = True
+        self.variableNames = [""Delta_Epsillon_Roccard"",""tau_Rocc1"",""tau_Rocc2""]
+        self.variableUnits =  [""dimensionless"", ""s"", ""s""]     
+        self.variableDescriptions = [""Oscillator strength of the mode #"", 'Time constant #', 'Friction time #']
+
+        self.invalidNumberMessage = ""Invalid number of Roccard terms.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        chi = variableDictionary.get('Delta_Epsillon_Roccard')
+        tau1 = variableDictionary.get('tau_Rocc1')
+        tau2 = variableDictionary.get('tau_Rocc2')
+        return chi/((1+j*w*tau1)*(1+j*w*tau2))
+    
+class Titov33(Model):
+    def __init__(self):
+        self.name = ""Titov33""
+        self.label = ""Number of Titov terms""
+        self.explanation = ""Titov model depicts the permitivity Epsillon as Eps = Eps_0 +.""
+        self.isCumulative = True
+        self.variableNames = [""temperature_exp"",""Beta1"",""Beta2"",""Gamma1"",""Eta"",""SigmaV""]
+        self.variableUnits =  [""Celsius"", ""dimensionless"", ""dimensionless"",""dimensionless"", ""s"",""dimensionless""]     
+        self.variableDescriptions = [""temperature_exp"", ""Beta1"",""Beta2"",""Gamma1"",""Eta"",""SigmaV""]
+
+        self.invalidNumberMessage = ""Invalid number of Titov terms.""
+
+    def epsilon(self,eps,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        
+        T = variableDictionary.get('temperature_exp')
+        beta1 = variableDictionary.get('Beta1')
+        beta2 = variableDictionary.get('Beta2')
+        gamma1 = variableDictionary.get('Gamma1')
+        eta = variableDictionary.get('Eta')
+        sigmaV = variableDictionary.get('SigmaV')
+        #tauD=variableDictionary.get('TauD')
+        teta = (1-300/(273.15+T))
+        eps0 = 77.66 - 103.3*teta
+        eps_inf = eps[0]
+        tauBF = 1/(2*np.pi*(20.27+146.5*teta+314*teta**2))*1e-9
+        tauD = tauBF / (eta * (1/(2*beta2**2)-1/sigmaV+1))
+        deltaeps = eps0 - eps_inf
+        return (deltaeps/(1+j*eta*w*tauD))*(((2*beta2**2)/(2*beta2**2+j*eta*w*tauD))+((j*eta*w*tauD)/(sigmaV-2*(eta*w/gamma1)**2+2*j*beta1*eta*w/gamma1)))
+############################################################
+############################################################
+materialModels = [Drude(),Popov(),Lorentz(),Voigt(),LogN(),Debye(),HN(),Boltzmann(),Roccard(),Titov33(),Scattering()] #list of the models that will be used in the software
+
+#NOTE: Scattering() must be last in this list, as its epsilon function depends on the previous value of epsilon
+
+
+####################################
+######### Interface Models #########
+####################################
+
+
+
+class InterfaceModel:
+    def __init__(self):
+        self.name = """"
+        self.label = """"                          #label used to ask for the number of terms of the given model
+        self.explanation = """"                    #Text displayed in the reference box
+ #       self.isCumulative = False                #Can there be several terms with this model?
+        self.variableNames = []                  
+        self.variableUnits =  []
+        self.variableDescriptions = []           #if isCumulative=True, the index of the term will be added to the descriptions (\n is added anyway)
+ #       self.invalidNumberMessage = """"           #Message to show if the number of term entered is invalid
+
+    def H(self,w,paramList):
+        raise NotImplementedError()              #raises an exception if you use a model without implementing its transmission/reflection model
+
+
+class TDCMT(InterfaceModel):
+    def __init__(self):
+        self.name=""TDCMT""
+        self.label=""TDCMT""
+        self.explanation=""Time Domain Coupled Mode Theory""
+        self.variableNames=[""f0"",""dec0"",""dece""]
+        self.variableUnits=[""Hz"",""s^-1"",""s^-1""]
+        self.variableDescriptions=[""Central frequency of the mode of the resonator #"",
+                                   ""Non radiative decay rate of the mode #"",
+                                   ""Radiative decay rate of the mode of the resonator #""]
+        
+    def H(self,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        w0=variableDictionary.get('f0')*2*np.pi
+        dec0=variableDictionary.get('dec0') #decay rate = 1/tau
+        dece=variableDictionary.get('dece')
+        return dece/((j*(w-w0)+dece+dec0))
+
+class TDCMT2(InterfaceModel):
+    def __init__(self):
+        self.name=""TDCMT2""
+        self.label=""2nd order oscillator""
+        self.explanation=""Similar to Time Domain Coupled Mode Theory, but without neglecting the negative frequencies""
+        self.variableNames=[""f0"",""dec0"",""dece""]
+        self.variableUnits=[""Hz"",""s^-1"",""s^-1""]
+        self.variableDescriptions=[""Central frequency of the mode of the resonator #"",
+                                   ""Non radiative decay rate of the mode #"",
+                                   ""Radiative decay rate of the mode of the resonator #""]
+        
+    def H(self,w,paramList):
+        variableDictionary = dict(zip(self.variableNames, paramList))
+        w0=variableDictionary.get('f0')*2*np.pi
+        dec0=variableDictionary.get('dec0') #decay rate = 1/tau
+        dece=variableDictionary.get('dece')
+        return dece*2*j*w/((j*(w-w0)+dece+dec0)*(j*(w+w0)+dece+dec0))
+
+interfaceModels=[TDCMT(),TDCMT2()]","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/fit_TDSg.py",".py","164052","3714","#!/usr/bin/python
+# -*- coding: latin-1 -*-
+
+import os, sys
+import pickle
+import random
+import numpy as np
+import traceback
+from PyQt5.QtCore import *
+from PyQt5.QtGui import *
+from PyQt5.QtWidgets import *
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
+from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
+try:
+    import shutil
+except:
+    print(traceback.format_exc())
+    pass
+import h5py
+
+from fit_TDSc import Controler
+from epsillon3 import dielcal ## Library for resolving the inverse problem in our case (see the assumptions necessary to use this library)
+import fit_TDSf as TDS
+from fit_TDSf import Material
+from fit_TDSf import Layer
+from fit_TDSf import Layers
+
+import fit_TDSm as Model
+import csts as Csts
+
+from pathlib import Path as path_
+#import optimization as optim
+
+
+ROOT_DIR = path_(__file__).parent
+
+try:
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    myrank = comm.Get_rank()
+    size = comm.Get_size()
+except:
+    print(traceback.format_exc())
+    print('mpi4py is required for parallelization')
+    myrank=0
+
+
+import sip
+
+def deleteLayout(layout):
+    if layout is not None:
+        while layout.count():
+            item = layout.takeAt(0)
+            widget = item.widget()
+            if widget is not None:
+                widget.deleteLater()
+            else:
+                deleteLayout(item.layout())
+        sip.delete(layout)
+
+def to_sup(s):
+    """"""Convert a string of digit or integer to superscript""""""
+    sups = {u'0': u'\u2070',
+            u'1': u'\u00b9',
+            u'2': u'\u00b2',
+            u'3': u'\u00b3',
+            u'4': u'\u2074',
+            u'5': u'\u2075',
+            u'6': u'\u2076',
+            u'7': u'\u2077',
+            u'8': u'\u2078',
+            u'9': u'\u2079'}
+
+    return ''.join(sups.get(char, char) for char in str(s))
+
+graph_option='Transmission'
+graph_option_2=None
+
+
+
+class MyTableWidget(QWidget):
+
+    def __init__(self, parent,controler):
+        super(QWidget, self).__init__(parent)
+        self.layout = QVBoxLayout(self)
+
+        # Initialize tab screen
+        self.tabs = QTabWidget()
+        self.tabs.setUsesScrollButtons(True)
+        self.tab0 = Creation_tab(self,controler)
+        self.tab1 = Initialisation_tab(self,controler)
+        self.tab2 = Model_parameters_tab(self,controler)
+        self.tab3 = Optimization_tab(self,controler)
+
+        # Add tabs
+        self.tabs.addTab(self.tab0,""Create material"")
+        self.tabs.addTab(self.tab1,""Initialisation"")
+        self.tabs.addTab(self.tab2,""Model parameters"")
+        self.tabs.addTab(self.tab3,""Optimization"")
+        self.tabs.setCurrentIndex(1)
+
+        # Add tabs to widget
+        self.layout.addWidget(self.tabs)
+        self.setLayout(self.layout)
+
+###############################################################################
+###############################################################################
+##########################   Creation tab   ###################################
+###############################################################################
+###############################################################################
+
+class Creation_tab(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.setMinimumSize(640, 480)
+        self.controler = controler
+        
+        self.category_choice = QComboBox()
+        self.category_choice.addItems(['Material','Metasurface'])
+        self.category_choice.setMaximumWidth(440)
+        self.category_choice.currentIndexChanged.connect(self.refresh)
+        self.material_choices = material_choices_handler(self,controler)
+        self.material_choices.refresh()
+        self.material_param = material_parameters(self,controler)
+        self.log_box = log_material_choices(self,controler)
+        self.graphs = Graphs_Creation(self,controler)
+        
+        # Creation Layouts
+        main_layout = QHBoxLayout()
+        sub_layout_v1 = QVBoxLayout()
+        sub_layout_v2 = QVBoxLayout()
+        
+        # Organisation layouts
+        sub_layout_v1.addWidget(self.category_choice,0)
+        sub_layout_v1.addWidget(self.material_choices)
+        sub_layout_v1.addWidget(self.log_box)
+        sub_layout_v1.addWidget(self.material_param)
+        sub_layout_v2.addWidget(self.graphs)
+
+        main_layout.addLayout(sub_layout_v1)
+        main_layout.addLayout(sub_layout_v2)
+        self.setLayout(main_layout)
+        
+    def refresh(self): # /!\ Creation_tab is not a client of the controler, 
+    #this is not called by the controler, only when category_choice is changed.
+        deleteLayout(self.material_param.layout())
+        self.controler.nb_param0 = 0
+        self.controler.refreshAll0('')
+        
+class material_choices_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient0(self)
+
+    def refresh(self):
+        material_choices_instance = material_choices(self,self.controler,self.parent.category_choice.currentIndex())
+ #       if self.parent.category_choice.currentIndex() == 0:
+        try:
+            self.main_layout=QVBoxLayout()
+            self.main_layout.addWidget(material_choices_instance)
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+            self.setMaximumHeight(450)
+        except:
+            print(traceback.format_exc())
+#        if self.parent.category_choice.currentIndex() == 1:
+#            try:
+#                deleteLayout(self.layout())
+#                self.setMaximumHeight(0)
+#            except:
+#                print(traceback.format_exc())
+#                pass
+        
+class material_choices(QGroupBox):
+    def __init__(self, parent, controler,isInterface):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient0(self)
+        self.isInterface=isInterface
+        if not self.isInterface:
+            self.setTitle(""Material choices"")
+        else:
+            self.setTitle(""Metasurface choices"")
+        self.setFixedWidth(430)
+        self.setMaximumHeight(450)
+
+        # Creation widgets
+        label_width=200
+        action_widget_width=200
+        corrective_width_factor=0
+
+        self.label_model = []
+        self.enter_model = []
+
+        self.main_layout = QVBoxLayout()
+        sub_layout_h = []
+        if not self.isInterface:                                                                  #Material
+            for i in range(len(Model.materialModels)):
+                self.label_model.append(QLabel(Model.materialModels[i].label + to_sup(i+1)))
+                self.label_model[i].setMaximumWidth(label_width)
+                if Model.materialModels[i].isCumulative:
+                    self.enter_model.append(QLineEdit())
+                    self.enter_model[i].setMaximumWidth(action_widget_width + corrective_width_factor)
+                    self.enter_model[i].setMaximumHeight(30)
+                    self.enter_model[i].setText(""0"")
+                else:
+                    self.enter_model.append(QComboBox())
+                    self.enter_model[i].addItems(['No','Yes'])
+                    self.enter_model[i].setMaximumWidth(action_widget_width)                   
+                # Creation layouts
+                sub_layout_h.append(QHBoxLayout())
+                # Organisation Layouts
+                sub_layout_h[i].addWidget(self.label_model[i],0)
+                sub_layout_h[i].addWidget(self.enter_model[i],0)
+                self.main_layout.addLayout(sub_layout_h[i])
+        else:                                                                               #Metasurface
+            for i in range(len(Model.interfaceModels)):
+                self.label_model.append(QLabel(Model.interfaceModels[i].label + to_sup(len(Model.materialModels)+i+1)))
+                self.label_model[i].setMaximumWidth(label_width)
+                self.enter_model.append(QLineEdit())
+                self.enter_model[i].setMaximumWidth(action_widget_width + corrective_width_factor)
+                self.enter_model[i].setMaximumHeight(30)
+                self.enter_model[i].setText(""0"")
+                # Creation layouts
+                sub_layout_h.append(QHBoxLayout())
+                # Organisation Layouts
+                sub_layout_h[i].addWidget(self.label_model[i],0)
+                sub_layout_h[i].addWidget(self.enter_model[i],0)
+                self.main_layout.addLayout(sub_layout_h[i])
+        
+        
+
+       
+        # OK button
+        self.button_submit = QPushButton(""Submit"")
+        self.button_submit.clicked.connect(self.submit_model_param)
+
+        self.main_layout.addWidget(self.button_submit)
+
+        self.setLayout(self.main_layout)
+
+    def submit_model_param(self):
+        nbTerms = [] 
+
+        if not self.isInterface:                                         #Material
+            for j in range(len(Model.materialModels)):
+                try:
+                    if Model.materialModels[j].isCumulative:
+                        nbTerms.append(int(self.enter_model[j].text()))
+                    else:
+                        nbTerms.append(self.enter_model[j].currentIndex())
+                    if nbTerms[j]<0:
+                        self.controler.invalid_n_model0(Model.materialModels[j].invalidNumberMessage)
+                        return(0)
+                except:
+                    print(traceback.format_exc())
+                    self.controler.invalid_n_model0(Model.materialModels[j].invalidNumberMessage)
+                    return(0)
+        else:
+            for j in range(len(Model.interfaceModels)):
+                try:
+                    nbTerms.append(int(self.enter_model[j].text()))
+                    if nbTerms[j]<0:
+                        self.controler.invalid_n_model0(""Invalid number of term for the model: {}"".format(Model.interfaceModels[j].label))
+                        return(0)
+                except:
+                    print(traceback.format_exc())
+                    self.controler.invalid_n_model0(""Invalid number of term for the model: {}"".format(Model.interfaceModels[j].label))
+                    return(0)
+        self.controler.material_parameters(nbTerms,self.isInterface)
+
+
+    def refresh(self):
+        pass
+
+class log_material_choices(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.parent = parent
+        self.controler.addClient0(self)
+        self.setReadOnly(True)
+        self.setMaximumWidth(440)
+        self.setMaximumHeight(200)
+        for j in range(len(Model.materialModels)):
+            self.append(to_sup(j+1) + "" "" + Model.materialModels[j].explanation + ""\n"")
+        for j in range(len(Model.interfaceModels)):
+            self.append(to_sup(len(Model.materialModels)+j+1) + "" "" + Model.interfaceModels[j].explanation + ""\n"")
+    def refresh(self):
+        if self.parent.category_choice.currentIndex() == 1:
+            self.setMaximumHeight(150)
+        else:
+            self.setMaximumHeight(200)
+        message = self.controler.message
+        if message:
+            self.append(message)
+            
+class material_parameters(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient0(self)
+        if self.parent.category_choice.currentIndex() == 0:                                     #Material
+            self.setTitle(""Material parameters"")
+        else:
+            self.setTitle(""Metasurface parameters"")
+        self.setFixedWidth(450)
+
+    def refresh(self):
+            
+        nb_param=self.controler.nb_param0
+        
+        material_parameters=material_parameters_scroll(self,self.controler)
+        scroll = QScrollArea(self)
+        scroll.setWidgetResizable(True)
+        scroll.setWidget(material_parameters)
+        
+        self.main_layout=QVBoxLayout()
+        self.main_layout.addWidget(scroll)
+        
+        if nb_param == 0:
+            deleteLayout(self.layout())
+        else:
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+        
+
+class material_parameters_scroll(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient0(self)
+
+        mydescription=self.controler.mydescription0
+        myunits=self.controler.myunits0
+        nb_param=self.controler.nb_param0
+
+        # Path of a file containing the values of the parameters (optional)
+        self.path_param_values=None
+        # Directory and name to save parameters in external file
+        self.dir_save_param=None
+        self.name_save_parameters=None
+
+        label_width=1500
+        text_box_width=100
+        text_box_height=25
+
+
+        # Creation Widgets et Layouts
+        self.label_param=QLabel('Parameter')
+        self.label_param.setMaximumWidth(label_width)
+
+        self.label_value=QLabel('Value')
+        self.label_value.setMaximumWidth(text_box_width)
+
+        self.label_search_path=QLabel('Use file to enter parameters values (optional)')
+        self.label_search_path.setMaximumWidth(label_width)
+
+        self.labels=[]
+
+        self.main_layout=QVBoxLayout()
+        sub_layout_h=QHBoxLayout()
+        sub_layout_h2=QHBoxLayout()
+        sub_layout_h3=QHBoxLayout()
+        sub_layout_h4=QHBoxLayout()
+        sub_layout_h5=QHBoxLayout()
+        layouts=[]
+
+        self.text_boxes_value=[]
+
+        self.search_path_button = QPushButton(""browse"")
+        self.search_path_button.clicked.connect(self.search_path)
+        self.search_path_button.setMaximumWidth(text_box_width)
+        self.search_path_button.setMaximumHeight(text_box_height)
+
+        self.preview_button = QPushButton(""Preview"")
+        self.preview_button.clicked.connect(self.preview)
+        self.preview_button.setMaximumHeight(text_box_height)
+        # Widgets to enter the name of the file to save parameters
+        self.label_name = QLabel('Name of the material/metasurface')
+        self.label_name.setMaximumWidth(label_width)
+        self.enter_name = QLineEdit()
+        self.enter_name.setMaximumWidth(text_box_width)
+        # Widgets to enter file directory
+        self.label_path = QLabel('Path of the parameters file')
+        self.label_path.setMaximumWidth(label_width)
+        self.search_save_path_button = QPushButton(""browse"")
+        self.search_save_path_button.clicked.connect(self.search_save_path)
+        self.search_save_path_button.setMaximumWidth(text_box_width)
+        self.search_save_path_button.setMaximumHeight(text_box_height)
+        # Button to save parameters in the desired file
+        self.save_button = QPushButton(""Save"")
+        self.save_button.clicked.connect(self.save_values)
+        self.save_button.setMaximumHeight(text_box_height)
+        # Error box
+        self.log_box=log_material_param(self,self.controler)
+        self.log_box.setMaximumWidth(275)
+        self.log_box.setMaximumHeight(30)
+
+
+        if nb_param:
+            for i in range(nb_param):
+                self.labels.append(QLabel('{0} ({1})'.format(mydescription[i],myunits[i])))
+                self.labels[i].setMaximumWidth(label_width)
+                self.labels[i].setAlignment(Qt.AlignTop)
+
+                self.text_boxes_value.append(QLineEdit())
+                self.text_boxes_value[i].setMaximumWidth(text_box_width)
+                self.text_boxes_value[i].setMaximumHeight(text_box_height)
+
+                layouts.append(QHBoxLayout())
+                layouts[i].setAlignment(Qt.AlignTop)
+
+        # Organisation layouts
+        sub_layout_h.addWidget(self.label_param)
+        sub_layout_h.addWidget(self.label_value)
+        sub_layout_h.setAlignment(Qt.AlignTop)
+        self.main_layout.addLayout(sub_layout_h)
+
+        if nb_param:
+            for i in range(nb_param):
+                layouts[i].addWidget(self.labels[i])
+                layouts[i].addWidget(self.text_boxes_value[i])
+                self.main_layout.addLayout(layouts[i])
+
+        sub_layout_h2.addWidget(self.label_search_path)
+        sub_layout_h2.addWidget(self.search_path_button)
+        sub_layout_h3.addWidget(self.log_box)
+        sub_layout_h3.addWidget(self.preview_button)
+        sub_layout_h4.addWidget(self.label_name)
+        sub_layout_h4.addWidget(self.enter_name)
+        sub_layout_h5.addWidget(self.label_path)
+        sub_layout_h5.addWidget(self.search_save_path_button)
+        
+        self.main_layout.addLayout(sub_layout_h2)
+        self.main_layout.addLayout(sub_layout_h3)
+        self.main_layout.addLayout(sub_layout_h4)
+        self.main_layout.addLayout(sub_layout_h5)
+        self.main_layout.addWidget(self.save_button)
+
+        self.setLayout(self.main_layout)
+
+    def refresh(self):
+        pass
+
+    def preview(self):
+        global myepsilonmaterial, freq0, graph_option_0
+        nb_param=self.controler.nb_param0
+        mesparam = np.zeros(nb_param)
+        try:
+            for i in range(nb_param):
+                mesparam[i]=float(self.text_boxes_value[i].text())
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""Invalid values."")
+            return(0)
+
+        f=open(os.path.join(""temp"",'temp_file_0.bin'),'rb')      
+        nbTerms = pickle.load(f)
+        f.close()
+        self.controler.material0.change_param(mesparam,self.controler.material0.variableNames())
+        try:
+            myepsilonmaterial = self.controler.material0.epsilon(2*np.pi*freq0)      
+            graph_option_0 ='Real(refractive index)'
+            self.controler.refreshAll0('')
+        except AttributeError:
+            self.log_box.append(""Previews are not implemented for metasurfaces"")
+            
+    def search_path(self):
+        nb_param=self.controler.nb_param0
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"")
+        try:
+            name=os.path.basename(fileName)
+            self.search_path_button.setText(name)
+            self.log_box.append(""Values taken from ""+name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path0()
+            return(0)
+        try:
+            mes_param=np.loadtxt(fileName,dtype = np.float64)
+            if nb_param==len(mes_param):
+                for i in range(nb_param):
+                    self.text_boxes_value[i].setText('{0:.3E}'.format(mes_param[i]))
+            else:
+                self.log_box.append(""The file submitted does not have the same number of parameters as the model chosen."")
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""There is a problem with the file submitted."")
+            return(0)
+            
+    def search_save_path(self):
+        #find path to save parameters
+        DirectoryName = QFileDialog.getExistingDirectory(self,""Select Directory"")
+        try:
+            self.dir_save_param=str(DirectoryName)
+            name=os.path.basename(str(DirectoryName))
+            self.search_save_path_button.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path0()
+            
+    def save_values(self):
+        nb_param=self.controler.nb_param0
+        mesparam = np.zeros(nb_param)
+        try:
+            for i in range(nb_param):
+                mesparam[i]=float(self.text_boxes_value[i].text())
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""Invalid values."")
+            return(0)
+        name = self.enter_name.text()
+        if name == '':
+            self.log_box.append('Please enter a name')
+            return(0)
+        if self.dir_save_param == None:
+                self.log_box.append(""Please enter a valid path"")
+                return(0)
+        if self.parent.parent.category_choice.currentIndex() == 0:
+            f=open(os.path.join(""temp"",'temp_file_0.bin'),'rb')
+            nbTerms = pickle.load(f)
+            f.close()
+            self.controler.material0 = Material(name = name, nbTerms = nbTerms, param = mesparam)
+        else:
+            self.controler.material0 = TDS.Interface(name = name, isMetasurface = 1, param = mesparam)
+        self.controler.save_material_param(self.controler.material0,self.dir_save_param)
+        self.log_box.append(""Values saved"")
+            
+
+class log_material_param(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient0(self)
+        self.setReadOnly(True)
+        self.append('Log')
+    def refresh(self):
+        pass
+    
+class Graphs_Creation(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient0(self)
+        self.setTitle(""Graphs"")
+        # Create objects to plot graphs
+        self.figure = plt.figure()
+        self.canvas = FigureCanvas(self.figure)
+        self.toolbar = NavigationToolbar(self.canvas, self)
+        self.canvas.draw()
+        # Create buttons to chose what to plot
+        # Real part of refractive index
+        self.button_real_index = QPushButton('Real(n)', self)
+        self.button_real_index.clicked.connect(self.real_index_graph)
+        # Imaginary part of refractive index
+        self.button_im_index = QPushButton('Im(n)', self)
+        self.button_im_index.clicked.connect(self.im_index_graph)
+        #Permittivity
+        self.button_Permittivity = QPushButton('Permittivity', self)
+        self.button_Permittivity.clicked.connect(self.Permitivity_graph)
+        
+        # Organisation layout
+        self.vlayoutmain = QVBoxLayout()
+        self.hlayout = QHBoxLayout()
+        self.hlayout.addWidget(self.button_real_index)
+        self.hlayout.addWidget(self.button_im_index)
+        self.hlayout.addWidget(self.button_Permittivity)
+        self.vlayoutmain.addWidget(self.canvas)
+        self.vlayoutmain.addLayout(self.hlayout)
+        self.setLayout(self.vlayoutmain)
+    
+    def draw_graph_material(self,freq,epsilon):
+        global graph_option_0
+        self.figure.clf()
+        ax1 = self.figure.add_subplot(111)
+        if graph_option_0=='Real(refractive index)':
+            ax1.set_title('Real part of refractive index', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Real part of refractive index',color=color)
+            ax1.plot(freq, np.sqrt(epsilon).real, 'b-', label='Re(n)')
+            ax1.legend()
+        elif graph_option_0=='Im(refractive index)':
+            ax1.set_title('Imaginary part of refractive index', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Imaginary part of refractive index',color=color)
+            ax1.plot(freq, np.sqrt(epsilon).imag, 'r-', label='Im(n)')
+            ax1.legend()
+        elif graph_option_0=='Permittivity':
+            ax1.set_title('Permittivity', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Permittivity',color=color)
+            ax1.plot(freq,  np.real(epsilon), 'b-', label='real part')
+            ax1.plot(freq, np.imag(epsilon), 'r-', label='imaginary part')
+            ax1.tick_params(axis='y', labelcolor=color)
+            ax1.legend()
+        self.figure.tight_layout()
+        self.canvas.draw()
+        
+    def Permitivity_graph(self):
+        global graph_option_0
+        graph_option_0='Permittivity'
+        self.controler.ploting_text0('Plotting Permittivity')
+    
+    def real_index_graph(self):
+        global graph_option_0
+        graph_option_0='Real(refractive index)'
+        self.controler.ploting_text0('Plotting real part of refractive index')
+
+    def im_index_graph(self):
+        global graph_option_0
+        graph_option_0='Im(refractive index)'
+        self.controler.ploting_text0('Plotting imaginary part of refractive index')
+    
+    def refresh(self):
+        global myepsilonmaterial,freq0, graph_option_0
+        try:        
+            freq0=self.controler.myglobalparameters.freq
+            if freq0==None:
+                freq0 = np.arange(0,5e12,1e9)
+            self.draw_graph_material(freq0,myepsilonmaterial)
+        except:
+            print(traceback.format_exc())
+            self.figure.clf()
+            ax1 = self.figure.add_subplot(111)
+            ax1.set_title('Graphs', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('')
+            ax1.set_ylabel('',color=color)
+            ax1.plot(0,0,color=color)
+            ax1.tick_params(axis='y', labelcolor=color)
+            self.figure.tight_layout()
+            self.canvas.draw()
+
+###############################################################################
+###############################################################################
+#######################   Initialisation tab   ################################
+###############################################################################
+###############################################################################
+
+class Initialisation_tab(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.setMinimumSize(640, 480)
+        hlayout  = QHBoxLayout()
+        vlayout1 = QVBoxLayout()
+        vlayout2 = QVBoxLayout()
+        
+        self.advanced_choice = QComboBox()
+        self.advanced_choice.addItems(['Do not use advanced options','Use advanced options'])
+        self.advanced_choice.currentIndexChanged.connect(self.refresh)
+        self.advanced_choice.setMaximumWidth(440)
+        
+        self.init_param_widget = InitParam_handler(self, controler)
+        self.init_param_widget.refresh()
+        self.text_box = TextBoxWidget(self, controler)
+        self.layout_materials = layout_materials(self, controler)
+        self.layers = layers(self, controler)
+        
+        vlayout1.addWidget(self.advanced_choice)
+        vlayout1.addWidget(self.init_param_widget, 0)
+        vlayout1.addWidget(self.text_box, 0)
+        vlayout2.addWidget(self.layout_materials, 0)
+        vlayout2.addWidget(self.layers, 0)
+        hlayout.addLayout(vlayout1, 0)
+        hlayout.addLayout(vlayout2, 1)
+        self.setLayout(hlayout)
+        
+    def refresh(self):# /!\ Initialisation_tab is not a client of the controler, 
+    #this is not called by the controler, only when advanced_choice is changed.
+        deleteLayout(self.init_param_widget.layout())
+        self.controler.nlayers = None
+        self.controler.nfixed_material = None
+        self.controler.noptim_material = None
+        self.controler.nfixed_metasurface = None
+        self.controler.noptim_metasurface = None
+        self.init_param_widget.refresh()
+        self.controler.refreshAll('')
+        
+class InitParam_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setMaximumWidth(440)
+
+    def refresh(self):
+        init_instance = InitParamWidget(self.parent,self.controler)
+        try:
+            deleteLayout(self.layout())
+            self.layout().deleteLater()
+        except AttributeError:
+            main_layout = QVBoxLayout()
+            main_layout.addWidget(init_instance)
+            self.setLayout(main_layout)
+
+
+class InitParamWidget(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient(self)
+#        self.setTitle(""Layout"")
+        
+        label_width=1500
+        text_box_width=200
+        text_box_height=25
+        
+        # Number of layers
+        self.label_nlayers = QLabel('Number of layers')
+        self.label_nlayers.setMaximumWidth(label_width)
+        self.nlayers_box=QLineEdit()
+        self.nlayers_box.setMaximumWidth(text_box_width)
+        self.nlayers_box.setMaximumHeight(text_box_height)
+        self.nlayers_box.setText('1')
+        # Number of fixed materials
+        self.label_nfixed_material = QLabel('Number of fixed materials')
+        self.label_nfixed_material.setMaximumWidth(label_width)
+        self.nfixed_material_box=QLineEdit()
+        self.nfixed_material_box.setAlignment(Qt.AlignVCenter)
+        self.nfixed_material_box.setMaximumWidth(text_box_width)
+        self.nfixed_material_box.setMaximumHeight(text_box_height)
+        self.nfixed_material_box.setText('0')
+        # Number of materials to optimize
+        self.label_nmaterial_optim = QLabel('Number of materials to optimize')
+        self.label_nmaterial_optim.setMaximumWidth(label_width)
+        self.nmaterial_optim_box=QLineEdit()
+        self.nmaterial_optim_box.setMaximumWidth(text_box_width)
+        self.nmaterial_optim_box.setMaximumHeight(text_box_height)
+        self.nmaterial_optim_box.setText('1')
+        
+        #modify for metasurfaces:
+        
+        # Number of fixed resonators
+        self.label_nfixed_metasurface = QLabel('Number of fixed metasurfaces ' + to_sup(1))
+        self.label_nfixed_metasurface.setMaximumWidth(label_width)
+        self.nfixed_metasurface_box=QLineEdit()
+        self.nfixed_metasurface_box.setAlignment(Qt.AlignVCenter)
+        self.nfixed_metasurface_box.setMaximumWidth(text_box_width)
+        self.nfixed_metasurface_box.setMaximumHeight(text_box_height)
+        self.nfixed_metasurface_box.setText('0')
+        # Number of resonators to optimize
+        self.label_noptim_metasurface = QLabel('Number of metasurfaces to optimize ' + to_sup(1))
+        self.label_noptim_metasurface.setMaximumWidth(label_width)
+        self.noptim_metasurface_box=QLineEdit()
+        self.noptim_metasurface_box.setMaximumWidth(text_box_width)
+        self.noptim_metasurface_box.setMaximumHeight(text_box_height)
+        self.noptim_metasurface_box.setText('0')
+        
+        
+        # We create the text associated to the text box
+
+        self.label_path_without_sample = QLabel('Select data (without sample) '+to_sup(2))
+        self.label_path_without_sample.setAlignment(Qt.AlignVCenter)
+        self.label_path_without_sample.resize(200, 100);
+        self.label_path_without_sample.resize(self.label_path_without_sample.sizeHint());
+
+        self.label_path_with_sample = QLabel('Select data (with sample) '+to_sup(2))
+        self.label_path_with_sample.setAlignment(Qt.AlignVCenter)
+        self.label_path_with_sample.resize(200, 100);
+        self.label_path_with_sample.resize(self.label_path_with_sample.sizeHint());
+
+        # We create text box for the user to enter values of the sample
+
+        self.button_ask_path_without_sample = QPushButton('browse')
+        self.button_ask_path_without_sample.resize(200, 100);
+        self.button_ask_path_without_sample.resize(self.button_ask_path_without_sample.sizeHint());
+        self.button_ask_path_without_sample.clicked.connect(self.get_path_without_sample)
+
+        self.button_ask_path_with_sample = QPushButton('browse')
+        self.button_ask_path_with_sample.resize(200, 100);
+        self.button_ask_path_with_sample.resize(self.button_ask_path_with_sample.sizeHint());
+        self.button_ask_path_with_sample.clicked.connect(self.get_path_with_sample)
+
+        # We create a button to extract the information from the text boxes
+        self.button = QPushButton('Submit')
+        self.button.clicked.connect(self.on_click)
+        self.button.pressed.connect(self.pressed_loading1)
+        
+        # Filter or not filter
+        self.LFfilter_label = QLabel('Filter low frequencies?')
+        self.LFfilter_choice = QComboBox()
+        self.LFfilter_choice.addItems(['No','Yes'])
+        self.LFfilter_choice.setMaximumWidth(text_box_width)
+        self.LFfilter_choice.setMaximumHeight(text_box_height)
+        
+        self.HFfilter_label = QLabel('Filter high frequencies?')
+        self.HFfilter_choice = QComboBox()
+        self.HFfilter_choice.addItems(['No','Yes'])
+        self.HFfilter_choice.setMaximumWidth(text_box_width)
+        self.HFfilter_choice.setMaximumHeight(text_box_height)
+        
+        self.label_start = QLabel('Start (Hz)')
+        self.label_end   = QLabel('End (Hz)')
+        self.label_sharp = QLabel('Sharpness of frequency filter '+to_sup(3))
+        self.start_box = QLineEdit()
+        self.end_box   = QLineEdit()
+        self.sharp_box = QLineEdit()
+        self.start_box.setMaximumWidth(text_box_width)
+        self.start_box.setMaximumHeight(text_box_height)
+        self.start_box.setText(""0.18e12"")
+        self.end_box.setMaximumWidth(text_box_width)
+        self.end_box.setMaximumHeight(text_box_height)
+        self.end_box.setText(""6e12"")
+        self.sharp_box.setMaximumWidth(text_box_width)
+        self.sharp_box.setMaximumHeight(text_box_height)
+        self.sharp_box.setText(""10"")
+        
+        # remove end of reference pulse
+        self.label_zeros = QLabel('Set end of time trace to zero? '+to_sup(4))
+        self.zeros_choice = QComboBox()
+        self.zeros_choice.addItems(['No','Yes'])
+        self.zeros_choice.setMaximumWidth(text_box_width)
+        self.zeros_choice.setMaximumHeight(text_box_height)
+        
+        #Remove baseline ""dark"" noise
+        self.label_dark = QLabel('Remove dark noise ramp ? '+to_sup(5))
+        self.dark_choice = QComboBox()
+        self.dark_choice.addItems(['No','Yes']) #Use a function to add
+        self.dark_choice.setMaximumWidth(text_box_width-24)
+        
+        self.label_slope = QLabel('Slope of the ramp?')
+        self.slope_box = QLineEdit()
+        self.slope_box.setMaximumWidth(text_box_width)
+        self.slope_box.setMaximumHeight(text_box_height)
+        self.slope_box.setText(""4e-6"")
+        
+        self.label_intercept = QLabel('Intercept of the ramp?')
+        self.intercept_box = QLineEdit()
+        self.intercept_box.setMaximumWidth(text_box_width)
+        self.intercept_box.setMaximumHeight(text_box_height)
+        self.intercept_box.setText(""0.3e-3"")
+        
+        # Delay
+        self.label_delay = QLabel(""Fit delay"")
+        self.label_delay.setMaximumWidth(label_width)
+        self.options_delay = QComboBox()
+        self.options_delay.addItems(['No','Yes'])
+        self.options_delay.setMaximumWidth(text_box_width-24)
+        self.delayvalue_label = QLabel(""Initial guess, maximum delay (s)"")
+        self.delay_guess_box = QLineEdit()
+        self.delay_guess_box.setMaximumWidth(text_box_width-24)
+        self.delay_limit_box = QLineEdit()
+        self.delay_limit_box.setMaximumWidth(text_box_width-24)
+        self.fix_delay_box = QCheckBox(""Fixed"")
+        
+        # Leftover noise
+        self.label_leftover = QLabel(""Fit the amplitude variation and time scale dilation"")
+        self.label_leftover.setMaximumWidth(label_width)
+        self.options_leftover = QComboBox()
+        self.options_leftover.addItems(['No','Yes'])
+        self.options_leftover.setMaximumWidth(text_box_width-24)
+        self.leftovervaluea_label = QLabel(""Amplitude coefficient guess and maximum value"")
+        self.leftovera_guess_box = QLineEdit()
+        self.leftovera_guess_box.setMaximumWidth(text_box_width-24)
+        self.leftovera_limit_box = QLineEdit()
+        self.leftovera_limit_box.setMaximumWidth(text_box_width-24)
+        # self.label_fix_a = QLabel(""Fixed"")
+        # self.label_fix_a.setMaximumWidth(label_width)
+        self.fix_a_box = QCheckBox(""Fixed"")
+        #self.fix_a_box.setMaximumWidth(text_box_width-24)
+        self.leftovervaluec_label = QLabel(""Dilation coefficient guess and maximum value"")
+        self.leftoverc_guess_box = QLineEdit()
+        self.leftoverc_guess_box.setMaximumWidth(text_box_width-24)
+        self.leftoverc_limit_box = QLineEdit()
+        self.leftoverc_limit_box.setMaximumWidth(text_box_width-24)  
+        self.fix_c_box = QCheckBox(""Fixed"")
+        
+        # Super resolution
+        self.label_super = QLabel(""Super resolution"")
+        self.options_super = QComboBox()
+        self.options_super.addItems(['No','Yes'])
+        self.options_super.setMaximumWidth(text_box_width-24)
+        
+
+        # Organisation layout
+        self.hlayout1=QHBoxLayout()
+        self.hlayout2=QHBoxLayout()
+        self.hlayout3=QHBoxLayout()
+        self.hlayout4=QHBoxLayout()
+        self.hlayout5=QHBoxLayout()
+        self.hlayout6=QHBoxLayout()
+        self.hlayout7=QHBoxLayout()
+        self.hlayout8=QHBoxLayout()
+        self.hlayout9=QHBoxLayout()
+        self.hlayout10=QHBoxLayout()
+        self.hlayout11=QHBoxLayout()
+        self.hlayout12=QHBoxLayout()
+        self.hlayout14=QHBoxLayout()
+        self.hlayout17=QHBoxLayout()
+        self.hlayout18=QHBoxLayout()
+        self.hlayout19=QHBoxLayout()
+        self.hlayout20=QHBoxLayout()
+        self.hlayout21=QHBoxLayout()
+        self.hlayout22=QHBoxLayout()
+        self.hlayout23=QHBoxLayout()
+        self.vlayoutmain=QVBoxLayout()
+        
+        self.hlayout1.addWidget(self.label_nlayers,1)
+        self.hlayout1.addWidget(self.nlayers_box,0)
+        
+        self.hlayout2.addWidget(self.label_nfixed_material,1)
+        self.hlayout2.addWidget(self.nfixed_material_box,0)
+        
+        self.hlayout3.addWidget(self.label_nmaterial_optim,1)
+        self.hlayout3.addWidget(self.nmaterial_optim_box,0)
+        
+        self.hlayout4.addWidget(self.label_nfixed_metasurface,1) #modify for metasurfaces
+        self.hlayout4.addWidget(self.nfixed_metasurface_box,0)
+        
+        self.hlayout5.addWidget(self.label_noptim_metasurface,1)
+        self.hlayout5.addWidget(self.noptim_metasurface_box,0)
+        
+        self.hlayout6.addWidget(self.label_path_without_sample,20)
+        self.hlayout6.addWidget(self.button_ask_path_without_sample,17)
+
+        self.hlayout7.addWidget(self.label_path_with_sample,20)
+        self.hlayout7.addWidget(self.button_ask_path_with_sample,17)
+        
+        self.hlayout8.addWidget(self.label_super,0)
+        self.hlayout8.addWidget(self.options_super,1)
+        
+        self.hlayout9.addWidget(self.label_delay,0)
+        self.hlayout9.addWidget(self.options_delay,1)
+
+        self.hlayout10.addWidget(self.delayvalue_label,0)
+        self.hlayout10.addWidget(self.delay_guess_box,1)
+        self.hlayout10.addWidget(self.delay_limit_box,1)
+        self.hlayout10.addWidget(self.fix_delay_box,0)
+        
+        self.hlayout11.addWidget(self.label_leftover,0)
+        self.hlayout11.addWidget(self.options_leftover,1)
+        
+        self.hlayout12.addWidget(self.leftovervaluea_label,0)
+        self.hlayout12.addWidget(self.leftovera_guess_box,1)
+        self.hlayout12.addWidget(self.leftovera_limit_box,1)
+        self.hlayout12.addWidget(self.fix_a_box,0)
+        
+        self.hlayout14.addWidget(self.leftovervaluec_label,0)
+        self.hlayout14.addWidget(self.leftoverc_guess_box,1)
+        self.hlayout14.addWidget(self.leftoverc_limit_box,1)
+        self.hlayout14.addWidget(self.fix_c_box,0)
+        
+        self.hlayout17.addWidget(self.LFfilter_label,1)
+        self.hlayout17.addWidget(self.LFfilter_choice,0)
+        self.hlayout17.addWidget(self.label_start,1)
+        self.hlayout17.addWidget(self.start_box,0)
+        
+        self.hlayout18.addWidget(self.HFfilter_label,1)
+        self.hlayout18.addWidget(self.HFfilter_choice,0)
+        self.hlayout18.addWidget(self.label_end,1)
+        self.hlayout18.addWidget(self.end_box,0)
+
+        self.hlayout19.addWidget(self.label_sharp,1)
+        self.hlayout19.addWidget(self.sharp_box,0)
+
+        self.hlayout20.addWidget(self.label_zeros,1)
+        self.hlayout20.addWidget(self.zeros_choice,0)
+        
+        self.hlayout21.addWidget(self.label_dark,1)
+        self.hlayout21.addWidget(self.dark_choice,0)
+        self.hlayout22.addWidget(self.label_slope,1)
+        self.hlayout22.addWidget(self.slope_box,0)
+        self.hlayout23.addWidget(self.label_intercept,1)
+        self.hlayout23.addWidget(self.intercept_box,0)
+
+        self.vlayoutmain.addLayout(self.hlayout1)
+        self.vlayoutmain.addLayout(self.hlayout2)
+        self.vlayoutmain.addLayout(self.hlayout3)
+        self.vlayoutmain.addLayout(self.hlayout4) #modify for metasurfaces
+        self.vlayoutmain.addLayout(self.hlayout5)
+        self.vlayoutmain.addLayout(self.hlayout6)
+        self.vlayoutmain.addLayout(self.hlayout7)
+        if self.parent.advanced_choice.currentIndex() == 1:
+            self.vlayoutmain.addLayout(self.hlayout8)
+            self.vlayoutmain.addLayout(self.hlayout9)
+            self.vlayoutmain.addLayout(self.hlayout10)
+            self.vlayoutmain.addLayout(self.hlayout11)
+            self.vlayoutmain.addLayout(self.hlayout12)
+            self.vlayoutmain.addLayout(self.hlayout14)
+            self.vlayoutmain.addLayout(self.hlayout21)
+            self.vlayoutmain.addLayout(self.hlayout22)
+            self.vlayoutmain.addLayout(self.hlayout23)
+        sub_layoutv = QVBoxLayout()
+        sub_layoutv.addLayout(self.hlayout17)
+        sub_layoutv.addLayout(self.hlayout18)
+        sub_layoutv.addLayout(self.hlayout19)
+        sub_layoutv.addLayout(self.hlayout20)
+        filter_group = QGroupBox()
+        filter_group.setTitle('Filters')
+        filter_group.setLayout(sub_layoutv)
+        self.vlayoutmain.addWidget(filter_group)
+        self.vlayoutmain.addWidget(self.button)
+        self.setLayout(self.vlayoutmain)
+
+
+    def pressed_loading1(self):
+        self.controler.loading_text()
+
+    def on_click(self):
+        try:
+            nlayers = int(self.nlayers_box.text())
+            nfixed_material = int(self.nfixed_material_box.text())
+            nmaterial_optim = int(self.nmaterial_optim_box.text())
+            try: #modify for metasurfaces
+                nfixed_metasurface = int(self.nfixed_metasurface_box.text())
+                noptim_metasurface = int(self.noptim_metasurface_box.text())
+            except:
+                print(traceback.format_exc())
+                nfixed_metasurface = 0
+                noptim_metasurface = 0
+            Lfiltering_index = self.LFfilter_choice.currentIndex()
+            Hfiltering_index = self.HFfilter_choice.currentIndex()
+            zeros_index = self.zeros_choice.currentIndex()
+            dark_index = self.dark_choice.currentIndex()
+            cutstart = float(self.start_box.text())
+            cutend   = float(self.end_box.text())
+            cutsharp = float(self.sharp_box.text())
+            fitDelay = self.options_delay.currentIndex()
+            delay_guess = 0
+            delay_limit = 0
+            delayfixed = False
+            modesuper = 0
+            fitLeftover = self.options_leftover.currentIndex()
+            leftover_guess = np.zeros(2)
+            leftover_limit = np.zeros(2)
+            leftfixed = [False]*2
+            slope = float(self.slope_box.text())
+            intercept = float(self.intercept_box.text())
+            try:
+                if self.options_delay.currentIndex() == 1:
+                    delay_guess = float(self.delay_guess_box.text())
+                    delayfixed = self.fix_delay_box.isChecked()
+                    if not delayfixed:
+                        delay_limit = float(self.delay_limit_box.text())                    
+                modesuper = self.options_super.currentIndex()
+                if self.options_leftover.currentIndex() == 1:
+                    leftover_guess[0] = float(self.leftovera_guess_box.text())
+                    leftover_guess[1] = float(self.leftoverc_guess_box.text())
+                    leftfixed[0] = self.fix_a_box.isChecked()
+                    leftfixed[1] = self.fix_c_box.isChecked()
+                    if not leftfixed[0]:
+                        leftover_limit[0] = float(self.leftovera_limit_box.text())
+                    if not leftfixed[1]:   
+                        leftover_limit[1] = float(self.leftoverc_limit_box.text())
+            except:
+                print(traceback.format_exc())
+                pass
+#            if (nfixed_metasurface != 0) or (noptim_metasurface !=0):
+#                self.controler.refreshAll(""The software is not able to handle metasurfaces yet. You can add a transfer function, modify optimization and remove this warning."")
+#                return(0)
+            if nlayers<1:
+                self.controler.refreshAll('Number of layers has to be more than 1')
+                return(0)
+            if (nfixed_material<0)|(nmaterial_optim<0)|(nfixed_metasurface<0)|(noptim_metasurface<0):
+                self.controler.refreshAll('Numbers of materials should be positive')
+                return(0)
+            # if nmaterial_optim !=1:
+            #     self.controler.refreshAll(""The software is only able to handle one material to optimize."")
+            #     return(0)
+            #if noptim_metasurface >1: #err corrected
+            #    self.controler.refreshAll(""The software is only able to handle one metasurface to optimize."")
+            #    return(0)
+            try:
+                self.controler.choices_ini(self.path_without_sample,self.path_with_sample,
+                                               nlayers,nfixed_material, nmaterial_optim,
+                                               nfixed_metasurface, noptim_metasurface,
+                                               Lfiltering_index, Hfiltering_index, zeros_index, dark_index, cutstart, 
+                                               cutend, cutsharp, slope, intercept, fitDelay, delay_guess, delay_limit, delayfixed, modesuper, fitLeftover, leftover_guess, leftover_limit, leftfixed)
+                self.controler.refreshAll(""Done."")
+            except:
+                print(traceback.format_exc())
+                self.controler.error_message_path()
+                self.controler.nlayers = 0
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.refreshAll(""Invalid parameters, please enter real values only"")
+
+    def get_path_without_sample(self):
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""Without sample"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""Without sample"")
+        try:
+            self.path_without_sample=fileName
+            name=os.path.basename(fileName)
+            self.button_ask_path_without_sample.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path()
+
+    def get_path_with_sample(self):
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""With sample"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""With sample"")
+        try:
+            self.path_with_sample=fileName
+            Csts.FileName = fileName
+            name=os.path.basename(fileName)
+            self.button_ask_path_with_sample.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path()
+
+    def refresh(self):
+        pass
+
+
+class TextBoxWidget(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient(self)
+        self.setReadOnly(True)
+        self.append(""Log"")
+        self.setMaximumWidth(440)
+        
+        references = [to_sup(1) + ' Metasurfaces are currently only implemented for 1 layer samples',
+                      to_sup(2) + ' Time should be in ps.',
+                      to_sup(3) + ' Sharpness: 100 is almost a step function, 0.1 is really smooth. See graphs in optimization tab.',
+                      to_sup(4) + ' Calculate the delay between the two pulses maximum and set the end of the reference pulse to zero to avoid temporal aliasing.',
+                      to_sup(5) + ' Use a linear function (or custom function) to remove the contribution of the dark noise after 200GHz.']
+        for reference in references:
+            self.append(reference)
+            self.append('')
+
+    def refresh(self):
+        message = self.controler.message
+        if message:
+            self.append(message)
+            
+class layout_materials(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient(self)
+        self.setTitle(""Materials"")
+        self.setMaximumHeight(290)
+
+    def refresh(self):
+        layout_materials=layout_materials_scroll(self,self.controler)
+        scroll = QScrollArea(self)
+        scroll.setWidgetResizable(True)
+        scroll.setWidget(layout_materials)
+
+        self.main_layout=QVBoxLayout()
+        self.main_layout.addWidget(scroll)
+        if self.controler.nlayers:
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+                
+class layout_materials_scroll(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient(self)
+        self.nfixed_material = self.controler.nfixed_material
+        self.nfixed_metasurface = self.controler.nfixed_metasurface
+        if self.nfixed_material:
+            self.materials = [Material() for i in range(self.nfixed_material)]
+            self.hasMaterial = np.zeros(self.nfixed_material)
+        else:
+            self.materials = []
+            self.hasMaterial = [1]
+        self.interfaces = [TDS.Interface(name = 'dioptric interface')]
+        if self.nfixed_metasurface:
+            self.interfaces.append(TDS.Interface(isMetasurface = 1) for i in range(self.nfixed_metasurface))
+            self.hasMetasurface = np.zeros(self.nfixed_metasurface)
+        else:
+            self.hasMetasurface = [1]
+        
+        text_box_width=120
+        text_box_height=25
+        
+        ## Widgets and layouts
+        # Error box
+        self.log_box=log_layers(self,self.controler)
+#        self.log_box.setMaximumWidth(300)
+        # Name text
+        self.label_name = QLabel('Name')
+        self.label_name_2 = QLabel('Name')
+        self.label_name_3 = QLabel('Name')
+        self.label_name_4 = QLabel('Name')
+        # File text
+        self.label_file = QLabel('File')
+        self.label_file_3 = QLabel('File')
+        
+        self.submit_button = QPushButton(""Submit"")
+        self.submit_button.clicked.connect(self.submit_data)
+        self.submit_button.setMaximumHeight(text_box_height)
+        
+        self.text_boxes_fixed_material=  []
+        self.buttons_file_material = []
+        self.text_boxes_optim_material = []
+        self.text_boxes_fixed_metasurface=  []
+        self.buttons_file_metasurface = []
+        self.text_boxes_optim_metasurface = []
+
+        layouts_fixed_material = []
+        layouts_fixed_metasurface = []
+
+        if self.controler.nfixed_material:
+            #boxes
+            for i in range(self.controler.nfixed_material):
+                self.text_boxes_fixed_material.append(QLineEdit())
+                self.text_boxes_fixed_material[i].setMaximumWidth(text_box_width)
+                self.text_boxes_fixed_material[i].setMaximumHeight(text_box_height)
+                
+                self.buttons_file_material.append(QPushButton(""Browse""))
+                self.buttons_file_material[i].setMaximumWidth(text_box_width)
+                self.buttons_file_material[i].setMaximumHeight(text_box_height)
+                self.index=i
+                self.buttons_file_material[i].clicked.connect(self.search_file(i,1))
+                
+                layouts_fixed_material.append(QHBoxLayout())
+                layouts_fixed_material[i].setAlignment(Qt.AlignTop)
+        
+        if self.controler.noptim_material:
+            for i in range(self.controler.noptim_material):
+                self.text_boxes_optim_material.append(QLineEdit())
+                self.text_boxes_optim_material[i].setMaximumWidth(text_box_width)
+                self.text_boxes_optim_material[i].setMaximumHeight(text_box_height)
+        
+        if self.controler.nfixed_metasurface:
+            #boxes
+            for i in range(self.controler.nfixed_metasurface):
+                self.text_boxes_fixed_metasurface.append(QLineEdit())
+                self.text_boxes_fixed_metasurface[i].setMaximumWidth(text_box_width)
+                self.text_boxes_fixed_metasurface[i].setMaximumHeight(text_box_height)
+                
+                self.buttons_file_metasurface.append(QPushButton(""Browse""))
+                self.buttons_file_metasurface[i].setMaximumWidth(text_box_width)
+                self.buttons_file_metasurface[i].setMaximumHeight(text_box_height)
+                self.buttons_file_metasurface[i].clicked.connect(self.search_file(i,0))
+                
+                layouts_fixed_metasurface.append(QHBoxLayout())
+                layouts_fixed_metasurface[i].setAlignment(Qt.AlignTop)
+                
+        if self.controler.noptim_metasurface:
+            for i in range(self.controler.noptim_metasurface):
+                self.text_boxes_optim_metasurface.append(QLineEdit())
+                self.text_boxes_optim_metasurface[i].setMaximumWidth(text_box_width)
+                self.text_boxes_optim_metasurface[i].setMaximumHeight(text_box_height)
+                
+        #default name, may be commented.
+        if (self.controler.noptim_material == 1):
+            self.text_boxes_optim_material[0].setText('sample')
+                
+        # Organisation Layout
+        self.main_layout=QHBoxLayout()
+        sub_layout_g=QGridLayout()
+        sub_layout_g.setAlignment(Qt.AlignTop)
+        
+        if self.controler.nfixed_material:
+            sub_layout_g1 = QGridLayout()
+            sub_layout_g1.setAlignment(Qt.AlignTop)
+            sub_layout_g1.addWidget(self.label_name,0,1)
+            sub_layout_g1.addWidget(self.label_file,0,0)
+            for i in range(self.controler.nfixed_material):
+                sub_layout_g1.addWidget(self.text_boxes_fixed_material[i],i+1,1)
+                sub_layout_g1.addWidget(self.buttons_file_material[i],i+1,0)
+            # Group
+            group_1 = QGroupBox()
+            group_1.setTitle('Fixed materials')
+            group_1.setLayout(sub_layout_g1)
+            sub_layout_g.addWidget(group_1)
+                
+        if self.controler.noptim_material:
+            sub_layout_v = QVBoxLayout()
+            sub_layout_v.addWidget(self.label_name_2) 
+            for i in range(self.controler.noptim_material):
+                sub_layout_v.addWidget(self.text_boxes_optim_material[i])
+            # Group
+            group_2 = QGroupBox()
+            group_2.setTitle('Materials to optimize')
+            group_2.setLayout(sub_layout_v)
+            sub_layout_g.addWidget(group_2)
+            
+        if self.controler.nfixed_metasurface:
+            sub_layout_g3 = QGridLayout()
+            sub_layout_g3.setAlignment(Qt.AlignTop)
+            sub_layout_g3.addWidget(self.label_name_3,0,1)
+            sub_layout_g3.addWidget(self.label_file_3,0,0)
+            for i in range(self.controler.nfixed_metasurface):
+                sub_layout_g3.addWidget(self.text_boxes_fixed_metasurface[i],i+1,1)
+                sub_layout_g3.addWidget(self.buttons_file_metasurface[i],i+1,0)
+            # Group
+            group_3 = QGroupBox()
+            group_3.setTitle('Fixed metasurfaces')
+            group_3.setLayout(sub_layout_g3)
+            sub_layout_g.addWidget(group_3)
+                
+        if self.controler.noptim_metasurface:
+            sub_layout_v4 = QVBoxLayout()
+            sub_layout_v4.addWidget(self.label_name_4) 
+            for i in range(self.controler.noptim_metasurface):
+                sub_layout_v4.addWidget(self.text_boxes_optim_metasurface[i])
+            # Group
+            group_4 = QGroupBox()
+            group_4.setTitle('Metasurfaces to optimize')
+            group_4.setLayout(sub_layout_v4)
+            sub_layout_g.addWidget(group_4)
+        
+        if (self.controler.nlayers!=None):
+            sub_layout_g.addWidget(self.submit_button)
+            self.main_layout.addLayout(sub_layout_g)
+            self.main_layout.addWidget(self.log_box)
+            self.setLayout(self.main_layout)
+        
+    def refresh(self):
+        pass
+    
+    def search_file(self,index,isMaterial):
+        def search_file():
+            options = QFileDialog.Options()
+            # options |= QFileDialog.DontUseNativeDialog
+            # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"","""",""All Files (*);;Python Files (*.py)"", options=options)
+            fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"","""",""All Files (*);;Python Files (*.py)"")
+            try:
+                name=os.path.basename(fileName)
+                if name =='':
+                    self.log_box.append('Please enter a valid path')
+                    return(0)
+            except:
+                print(traceback.format_exc())
+                self.log_box.append('Please enter a valid path')
+                return(0)
+            try:
+                if isMaterial:
+                    self.materials[index] = Material(file = fileName)
+                    self.buttons_file_material[index].setText(name)
+                    self.text_boxes_fixed_material[index].setText(name.split('.')[0])
+                    self.hasMaterial[index]=1
+                else:
+                    self.interfaces[index+1] = TDS.Interface(file = fileName)
+                    self.buttons_file_metasurface[index].setText(name)
+                    self.text_boxes_fixed_metasurface[index].setText(name.split('.')[0])
+                    self.hasMetasurface[index]=1
+            except:
+                print(traceback.format_exc())
+                self.log_box.append('There is an error with the file submitted')
+        return search_file
+    
+    def submit_data(self):
+        materialsNames = []
+        metasurfacesNames = ['dioptric interface']
+
+        for i in range(self.nfixed_material):
+            if self.hasMaterial[i]==0:
+                self.controler.refreshAll(""Please enter all material files."")
+                return(0)
+        for i in range(self.nfixed_material):
+            name = self.text_boxes_fixed_material[i].text()
+            if name !='':
+                materialsNames.append(name)
+                # materials have been added to the list in search_path
+            else:
+                self.controler.refreshAll('Please give all the materials a name')
+                return(0)
+        for i in range(self.controler.noptim_material):
+            name = self.text_boxes_optim_material[i].text()
+            if name !='':
+                materialsNames.append(name)
+                self.materials.append(Material(name = name, fit_material = 1))
+            else:
+                self.controler.refreshAll('Please give all the materials a name')
+                return(0)
+        # Metasurfaces
+        for i in range(self.nfixed_metasurface):
+            if self.hasMetasurface[i]==0:
+                self.controler.refreshAll(""Please enter all metasurfaces files."")
+                return(0)
+            name = self.text_boxes_fixed_metasurface[i].text()
+            if name !='':
+                metasurfacesNames.append(name)
+                # metasurfaces have been added to the list in search_path
+            else:
+                self.controler.refreshAll('Please give all the metasurfaces a name')
+                return(0)
+        for i in range(self.controler.noptim_metasurface):
+            name = self.text_boxes_optim_metasurface[i].text()
+            if name !='':
+                metasurfacesNames.append(name)
+                self.interfaces.append(TDS.Interface(name = name, isMetasurface = 1, fit_metasurface = 1))
+            else:
+                self.controler.refreshAll('Please give all the metasurfaces a name')
+                return(0)
+        
+        if len(materialsNames)>0:
+            materialsNames.append('air')
+            self.materials.append(Material(name = 'air'))
+            self.controler.materialList = self.materials
+            self.controler.materialNames = materialsNames
+            # Metasurfaces
+            self.controler.distinctInterfaceList = self.interfaces
+            self.controler.interfaceNames = metasurfacesNames
+        else:
+            self.controler.refreshAll('No materials given')
+            return(0)
+        self.controler.refreshAll('')
+            
+class log_layers(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient(self)
+        self.setReadOnly(True)
+        self.append('Log')
+    def refresh(self):
+        pass
+
+class layers(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient(self)
+        self.setTitle(""Layers"")
+        self.setMaximumHeight(400)
+
+    def refresh(self):
+        layers=layers_scroll(self,self.controler)
+        scroll = QScrollArea(self)
+        scroll.setWidgetResizable(True)
+        scroll.setWidget(layers)
+
+        self.main_layout=QVBoxLayout()
+        self.main_layout.addWidget(scroll)
+        if self.controler.nlayers:
+            if len(self.controler.materialList):
+                try:
+                    deleteLayout(self.layout())
+                    self.layout().deleteLater()
+                except AttributeError:
+                    self.setLayout(self.main_layout)
+
+class layers_scroll(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.controler.addClient(self)
+        self.nlayers = self.controler.nlayers
+        
+        label_width=240
+        text_box_width=120
+        text_box_height=25
+        
+        ## Creation Widgets and Layouts
+        # Error box
+        self.log_box=log_layers(self,self.controler)
+        self.log_box.setMaximumHeight(100)
+        # labels
+        self.label_number=QLabel('Layer')
+        self.label_number.setMaximumWidth(label_width)
+        
+        self.label_thickness=QLabel('Thickness of the sample (m)')
+        self.label_thickness.setMaximumWidth(label_width)
+        
+        self.label_uncertainty=QLabel('Uncertainty of the thickness (%)')
+        self.label_uncertainty.setMaximumWidth(label_width)
+        
+        self.label_fit=QLabel('Fit thickness? '+to_sup(1))
+        self.label_fit.setMaximumWidth(label_width)
+        
+        self.label_material=QLabel('Material')
+        self.label_material.setMaximumWidth(label_width)
+        
+        self.label_interface=QLabel('Interface before layer')
+        self.label_interface.setMaximumWidth(label_width)
+        
+        self.label_air=QLabel('air')
+        self.label_air.setMaximumWidth(label_width)
+        self.label_air2=QLabel('air')
+        self.label_air.setMaximumWidth(label_width)
+        
+        self.label_1=QLabel('-')
+        self.label_1.setMaximumWidth(label_width)
+        self.label_2=QLabel('-')
+        self.label_2.setMaximumWidth(label_width)
+        self.label_3=QLabel('-')
+        self.label_3.setMaximumWidth(label_width)
+
+        self.label_FP=QLabel('Fabry Perot effect?')
+        self.label_FP.setMaximumWidth(label_width)
+
+        # Submit button
+        self.submit_button = QPushButton(""Submit"")
+        self.submit_button.clicked.connect(self.submit_data)
+        self.submit_button.pressed.connect(self.pressed_loading)
+        self.submit_button.setMaximumWidth(text_box_width)
+        self.submit_button.setMaximumHeight(text_box_height)
+        
+        self.labels = []
+        self.thickness_boxes = []
+        self.uncertainty_boxes = []
+        self.fit_options = []
+        self.materials_options = []
+        self.interface_options = []
+        self.FP_choice = []
+        
+        if len(self.controler.materialList):
+            if (self.nlayers):
+                # create widgets
+                for i in range(self.nlayers):
+                    self.labels.append(QLabel('{}'.format(i)))
+                    self.labels[i].setMaximumWidth(text_box_width)
+                    self.labels[i].setMaximumHeight(text_box_height)
+                    
+                    self.thickness_boxes.append(QLineEdit())
+                    self.thickness_boxes[i].setMaximumWidth(text_box_width)
+                    self.thickness_boxes[i].setMaximumHeight(text_box_height)
+                    
+                    self.uncertainty_boxes.append(QLineEdit())
+                    self.uncertainty_boxes[i].setMaximumWidth(text_box_width)
+                    self.uncertainty_boxes[i].setMaximumHeight(text_box_height)
+                    
+                    self.fit_options.append(QComboBox())
+                    self.fit_options[i].addItems(['Yes', 'No'])
+                    self.fit_options[i].setMaximumWidth(text_box_width)
+                    self.fit_options[i].setMaximumHeight(text_box_height)
+                    
+                    self.materials_options.append(QComboBox())
+                    self.materials_options[i].addItems(self.controler.materialNames)
+                    self.materials_options[i].setMaximumWidth(text_box_width)
+                    self.materials_options[i].setMaximumHeight(text_box_height)
+
+                    self.FP_choice = QComboBox()
+                    self.FP_choice.addItems(['Yes', 'No'])
+                    self.FP_choice.setMaximumWidth(text_box_width)
+                    self.FP_choice.setMaximumHeight(text_box_height)
+                    
+                for i in range(self.nlayers+1):
+                    self.interface_options.append(QComboBox())
+                    self.interface_options[i].addItems(self.controler.interfaceNames)
+                    self.interface_options[i].setMaximumWidth(text_box_width)
+                    self.interface_options[i].setMaximumHeight(text_box_height)
+                
+                if (self.nlayers == 3)&(self.controler.noptim_material ==1):
+                    if (self.controler.nfixed_material == 1)|(self.controler.nfixed_material == 0):
+                        self.materials_options[1].setCurrentIndex(1)
+            
+                self.main_layout = QVBoxLayout()
+                sub_layout_g = QGridLayout()
+                sub_layout_h = QHBoxLayout()
+                sub_layout_h.addWidget(self.log_box)
+                sub_layout_h.addWidget(self.submit_button)
+                
+                sub_layout_g.addWidget(self.label_number,     0,0)
+                sub_layout_g.addWidget(self.label_thickness,  1,0)
+                sub_layout_g.addWidget(self.label_uncertainty,2,0)
+                sub_layout_g.addWidget(self.label_fit,        3,0)
+                sub_layout_g.addWidget(self.label_FP,        4,0)
+                sub_layout_g.addWidget(self.label_material,   5,0)
+                sub_layout_g.addWidget(self.label_interface,  6,0)
+
+                for i in range(self.nlayers):
+                    sub_layout_g.addWidget(self.labels[i],           0,i+1)
+                    sub_layout_g.addWidget(self.thickness_boxes[i],  1,i+1)
+                    sub_layout_g.addWidget(self.uncertainty_boxes[i],2,i+1)
+                    sub_layout_g.addWidget(self.fit_options[i],      3,i+1)
+                    sub_layout_g.addWidget(self.FP_choice,      4,i+1)
+                    sub_layout_g.addWidget(self.materials_options[i],5,i+1)
+                for i in range(self.nlayers+1):
+                    sub_layout_g.addWidget(self.interface_options[i],6,i+1)
+                sub_layout_g.addWidget(self.label_air, 0,self.nlayers+1)
+                sub_layout_g.addWidget(self.label_1,   1,self.nlayers+1)
+                sub_layout_g.addWidget(self.label_2,   2,self.nlayers+1)
+                sub_layout_g.addWidget(self.label_3,   3,self.nlayers+1)
+                sub_layout_g.addWidget(self.label_air2,4,self.nlayers+1)
+                
+                
+                self.main_layout.addLayout(sub_layout_g)
+                self.main_layout.addLayout(sub_layout_h)
+                self.setLayout(self.main_layout)
+                self.log_box.append(to_sup(1) + ' Set the thickness of the layer as an optimization parameter.')
+                
+                
+    def refresh(self):
+        pass
+    
+    def pressed_loading(self):
+        self.parent.parent.text_box.append(""\n Processing...\n"") # using refreshAll caused data to disappear
+    
+    def submit_data(self):
+        layerlist = []
+        position_optim_material = []
+        position_optim_interface = []
+        position_optim_thickness = []
+        id_FP = []
+        nbpi= 0
+        # Get the layers
+        id_FP = self.FP_choice.currentIndex()
+        for i in range(self.controler.nlayers):
+            try:
+                thickness=float(self.thickness_boxes[i].text())
+                uncertainty=float(self.uncertainty_boxes[i].text())
+                if thickness<=0:
+                    self.log_box.append(""Warning: thickness should be positive"")
+                    return(0)
+                if uncertainty<0 or uncertainty>100:
+                    self.log_box.append(""Warning: The uncertainty you entered is not between 0 an 100%."")
+                    return(0)
+            except:
+                print(traceback.format_exc())
+                self.log_box.append(""Error: Please enter real numbers."")
+                return(0)
+            
+            material = self.controler.materialList[self.materials_options[i].currentIndex()]
+            if material.fit_material:
+                position_optim_material.append(i)
+            layer = Layer(thickness, material, uncertainty = uncertainty, 
+                          fit_index = self.fit_options[i].currentIndex())
+            layerlist.append(layer)
+            if layer.fit_thickness:
+                position_optim_thickness.append(i)
+        #Get the interfaces
+        for i in range(self.controler.nlayers+1):
+            interface = self.controler.distinctInterfaceList[self.interface_options[i].currentIndex()]
+            if interface.fit_metasurface:
+                position_optim_interface.append(i)
+            self.controler.interfaceList.append(interface)
+        
+        self.controler.param_ini(layerlist,position_optim_material,position_optim_thickness,position_optim_interface,id_FP, nbpi)
+        self.controler.initialised=1
+        if self.controler.nlayers ==1: #change param_ini to not need that? Get message from controler instead?
+            self.controler.refreshAll(""Data submitted"")
+        else:
+            self.log_box.append(""Data submitted"") #data still written in the boxes.
+
+
+
+###############################################################################
+###############################################################################
+######################  Model parameters tab   ################################
+###############################################################################
+###############################################################################
+
+class Model_parameters_tab(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.setMinimumSize(640, 480)
+
+        # Creation widgets
+        self.model_choices=model_choices(self,controler)
+        self.references_widget=references_widget(self,controler)
+        self.param_values=parameters_values(self,controler)
+
+        # Creation Layouts
+        main_layout=QHBoxLayout()
+        sub_layout_v1=QVBoxLayout()
+        sub_layout_v2=QVBoxLayout()
+
+        # Organisation layouts
+        sub_layout_v1.addWidget(self.model_choices)
+        sub_layout_v1.addWidget(self.references_widget)
+
+        sub_layout_v2.addWidget(self.param_values)
+
+        main_layout.addLayout(sub_layout_v1)
+        main_layout.addLayout(sub_layout_v2)
+        self.setLayout(main_layout)
+
+class model_choices(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+        self.setTitle(""Model choices"")
+        self.setMaximumWidth(400)
+
+    def refresh(self):
+        nb_param=self.controler.noptim_material + self.controler.noptim_metasurface
+
+        if nb_param == 0:
+            deleteLayout(self.layout())
+        if nb_param:
+            mod_choices=model_choices_scroll(self,self.controler)
+            scroll = QScrollArea(self)
+            scroll.setWidgetResizable(True)
+            scroll.setWidget(mod_choices)
+    
+            self.main_layout=QVBoxLayout()
+            self.main_layout.addWidget(scroll)
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+
+
+
+
+class model_choices_scroll(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+        self.setMaximumWidth(400)
+
+        # Creation widgets
+        label_width=200
+        action_widget_width=200
+        corrective_width_factor=-16
+
+#        # Photonic structre #remove
+#        self.label_struct = QLabel(""Photonic structure \u00b9"")
+#        self.label_struct.setMaximumWidth(label_width)
+#        self.options_struct = QComboBox()
+#        self.options_struct.addItems(['Transmission Fabry-Perot',
+#                                      'Transmission Fabry-Perot \n with a resonator (TDCMT)'])
+#        self.options_struct.setMaximumWidth(action_widget_width +
+#                                            corrective_width_factor)
+#        self.options_struct.setMaximumHeight(30)
+
+        self.label_name = []
+        
+        self.label_model = []
+        self.enter_model = []
+
+
+       
+        main_layout=QVBoxLayout()
+        sub_layout_h0 = []
+        sub_layout_h = []
+        
+        i=0 
+        for material in self.controler.materialList:
+            if material.fit_material == 1:
+                # Name of the material
+                self.label_name.append(QLabel(""{} :"".format(material.name)))
+                self.label_name[i].setMaximumWidth(label_width)
+                sub_layout_h0.append(QHBoxLayout())
+                sub_layout_h0[i].addWidget(self.label_name[i],0)
+                main_layout.addLayout(sub_layout_h0[i])
+
+                self.label_model.append([])
+                self.enter_model.append([])
+                sub_layout_h.append([])
+                for j in range(len(Model.materialModels)):
+                    self.label_model[i].append(QLabel(Model.materialModels[j].label + to_sup(j+1)))
+                    self.label_model[i][j].setMaximumWidth(label_width)
+                    if Model.materialModels[j].isCumulative:
+                        self.enter_model[i].append(QLineEdit())
+                        self.enter_model[i][j].setMaximumWidth(action_widget_width + corrective_width_factor)
+                        self.enter_model[i][j].setMaximumHeight(30)
+                        self.enter_model[i][j].setText(""0"")
+                    else:
+                        self.enter_model[i].append(QComboBox())
+                        self.enter_model[i][j].addItems(['No', 'Yes'])
+                        self.enter_model[i][j].setMaximumWidth(action_widget_width)
+                # Creation layouts
+                    sub_layout_h[i].append(QHBoxLayout())
+
+                # Organisation Layouts
+                    sub_layout_h[i][j].addWidget(self.label_model[i][j],0)
+                    sub_layout_h[i][j].addWidget(self.enter_model[i][j],0)
+
+                    main_layout.addLayout(sub_layout_h[i][j])
+                
+                i+=1
+        for interface in self.controler.interfaceList:
+            if interface.fit_metasurface == 1:
+                self.label_name.append(QLabel(""{} :"".format(interface.name)))
+                self.label_name[i].setMaximumWidth(label_width)
+                sub_layout_h0.append(QHBoxLayout())
+                sub_layout_h0[i].addWidget(self.label_name[i],0)
+                main_layout.addLayout(sub_layout_h0[i])
+                
+                self.label_model.append([])
+                self.enter_model.append([])
+                sub_layout_h.append([])
+                for j in range(len(Model.interfaceModels)):
+                    self.label_model[i].append(QLabel(Model.interfaceModels[j].label + to_sup(len(Model.materialModels)+j+1)))
+                    self.label_model[i][j].setMaximumWidth(label_width)
+                    self.enter_model[i].append(QLineEdit())
+                    self.enter_model[i][j].setMaximumWidth(action_widget_width +corrective_width_factor)
+                    self.enter_model[i][j].setMaximumHeight(30)
+                    self.enter_model[i][j].setText(""0"")
+                # Creation layouts
+                    sub_layout_h[i].append(QHBoxLayout())
+
+                # Organisation Layouts
+                    sub_layout_h[i][j].addWidget(self.label_model[i][j],0)
+                    sub_layout_h[i][j].addWidget(self.enter_model[i][j],0)
+
+                    main_layout.addLayout(sub_layout_h[i][j])
+                
+                i+=1
+                
+        # OK button
+        self.button_submit = QPushButton(""Submit"")
+        self.button_submit.clicked.connect(self.submit_model_param)
+        
+        main_layout.addWidget(self.button_submit)
+
+        self.setLayout(main_layout)
+
+    def submit_model_param(self):
+        nbTerms = [] 
+
+        for i in range(self.controler.noptim_material):
+            nbTerms.append([])
+            for j in range(len(Model.materialModels)):
+                try:
+                    if Model.materialModels[j].isCumulative:
+                        nbTerms[i].append(int(self.enter_model[i][j].text()))
+                    else:
+                        nbTerms[i].append(self.enter_model[i][j].currentIndex())
+                    if nbTerms[i][j]<0:
+                        self.controler.invalid_n_model(Model.materialModels[j].invalidNumberMessage)
+                        return(0)
+                except:
+                    print(traceback.format_exc())
+                    self.controler.invalid_n_model(Model.materialModels[j].invalidNumberMessage)
+                    return(0)
+        for i in range(self.controler.noptim_material,self.controler.noptim_material+self.controler.noptim_metasurface):    #we add the numbers of interface models after the material models
+            nbTerms.append([])
+            for j in range(len(Model.interfaceModels)):
+                try:
+                    nbTerms[i].append(int(self.enter_model[i][j].text()))
+                    if nbTerms[i][j]<0:
+                        self.controler.invalid_n_model(Model.interfaceModels[j].invalidNumberMessage)
+                        return(0)
+                except:
+                    print(traceback.format_exc())
+                    self.controler.invalid_n_model(Model.interfaceModels[j].invalidNumberMessage)
+                    return(0)
+        try:
+            if self.controler.initialised == 1:
+                self.controler.reset_values()
+                self.controler.parameters_values(nbTerms)
+            else:
+                self.controler.invalid_tun_opti_first()
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.invalid_param()
+    def refresh(self):
+        pass
+
+
+class references_widget(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+        #self.setFixedHeight(200)
+        self.setFixedWidth(400)
+        # self.setReadOnly(True)
+        self.append(""References"")
+        references=[to_sup(j+1) + "" "" + Model.materialModels[j].explanation + ""\n"" for j in range(len(Model.materialModels))]
+        references = references + [to_sup(len(Model.materialModels)+j+1) + "" "" + Model.interfaceModels[j].explanation + ""\n"" for j in range(len(Model.interfaceModels))]
+        for i in references:
+            self.append(i)
+
+    def refresh(self):
+        message = self.controler.message
+        if message:
+            self.append(message)
+
+
+
+
+class parameters_values(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+        self.setTitle(""Parameters Values"")
+
+    def refresh(self):
+        nb_param=self.controler.nb_param
+
+        if nb_param == 0:
+            deleteLayout(self.layout())
+        if nb_param:
+            parameters_values=parameters_values_scroll(self,self.controler)
+            scroll = QScrollArea(self)
+            scroll.setWidgetResizable(True)
+            scroll.setWidget(parameters_values)
+    
+            self.main_layout=QVBoxLayout()
+            self.main_layout.addWidget(scroll)
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+
+class parameters_values_scroll(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+
+        mydescription=self.controler.mydescription
+        myvariables=self.controler.myvariables
+        myunits=self.controler.myunits
+        nb_param=self.controler.nb_param
+
+        # Path of a file containing the values of the parameters (optional)
+        self.path_param_values=None
+        # Directory and name to save parameters in external file
+        self.dir_save_param=None
+        self.name_save_parameters=None
+
+        label_width=1500
+        text_box_width=100
+        text_box_height=25
+
+
+        # Creation Widgets and Layouts
+        self.label_param=QLabel('Parameter')
+        self.label_param.setMaximumWidth(label_width)
+
+        self.label_value=QLabel('Value')
+        self.label_value.setMaximumWidth(text_box_width)
+
+        self.label_min=QLabel('Min')
+        self.label_min.setMaximumWidth(text_box_width)
+
+        self.label_max=QLabel('Max')
+        self.label_max.setMaximumWidth(text_box_width)
+
+        self.label_search_path=QLabel('Use file to enter parameters values (optional)')
+        self.label_search_path.setMaximumWidth(label_width)
+
+        self.labels=[]
+
+        self.main_layout=QVBoxLayout()
+        sub_layout_h=QHBoxLayout()
+        sub_layout_h2=QHBoxLayout()
+        sub_layout_h3=QHBoxLayout()
+        sub_layout_h4=QHBoxLayout()
+        sub_layout_h5=QHBoxLayout()
+        sub_layout_h6=QHBoxLayout()
+        sub_layout_h7=QHBoxLayout()
+        layouts=[]
+
+        self.text_boxes_value=[]
+        self.text_boxes_min=[]
+        self.text_boxes_max=[]
+
+        self.search_path_button = QPushButton(""browse"")
+        self.search_path_button.clicked.connect(self.search_path)
+        self.search_path_button.setMaximumWidth(text_box_width)
+        self.search_path_button.setMaximumHeight(text_box_height)
+
+        self.submit_button = QPushButton(""Submit"")
+        self.submit_button.clicked.connect(self.submit_values)
+        self.submit_button.setMaximumWidth(text_box_width)
+        self.submit_button.setMaximumHeight(text_box_height)
+        
+        # Widgets to enter the name of the file to save parameters
+        self.label_name = QLabel('Name of the file')
+        self.label_name.setMaximumWidth(label_width)
+        self.enter_name = QLineEdit()
+        self.enter_name.setMaximumWidth(text_box_width)
+        
+        # Widgets to enter file directory
+        self.label_path = QLabel('Path of the file')
+        self.label_path.setMaximumWidth(label_width)
+        self.search_save_path_button = QPushButton(""browse"")
+        self.search_save_path_button.clicked.connect(self.search_save_path)
+        self.search_save_path_button.setMaximumWidth(text_box_width)
+        self.search_save_path_button.setMaximumHeight(text_box_height)
+        
+        # Button to save parameters in the desired file
+        self.save_button = QPushButton(""Save parameters in file"")
+        self.save_button.clicked.connect(self.save_values)
+        self.save_button.setMaximumHeight(text_box_height)
+
+        self.log_box=log_param_values(self,self.controler)
+        self.log_box.setMaximumWidth(400)
+        self.log_box.setMaximumHeight(50)
+
+
+        if nb_param:
+            for i in range(nb_param):
+                self.labels.append(QLabel('{0} ({1})'.format(mydescription[i],myunits[i])))
+                self.labels[i].setMaximumWidth(label_width)
+                self.labels[i].setAlignment(Qt.AlignTop)
+
+                self.text_boxes_value.append(QLineEdit())
+                self.text_boxes_value[i].setMaximumWidth(text_box_width)
+                self.text_boxes_value[i].setMaximumHeight(text_box_height)
+
+                self.text_boxes_min.append(QLineEdit())
+                self.text_boxes_min[i].setMaximumWidth(text_box_width)
+                self.text_boxes_min[i].setMaximumHeight(text_box_height)
+
+                self.text_boxes_max.append(QLineEdit())
+                self.text_boxes_max[i].setMaximumWidth(text_box_width)
+                self.text_boxes_max[i].setMaximumHeight(text_box_height)
+
+                layouts.append(QHBoxLayout())
+                layouts[i].setAlignment(Qt.AlignTop)
+
+        # Organisation layouts
+        sub_layout_h.addWidget(self.label_param)
+        sub_layout_h.addWidget(self.label_value)
+        sub_layout_h.addWidget(self.label_min)
+        sub_layout_h.addWidget(self.label_max)
+        sub_layout_h.setAlignment(Qt.AlignTop)
+        self.main_layout.addLayout(sub_layout_h)
+
+        if nb_param:
+            for i in range(nb_param):
+                layouts[i].addWidget(self.labels[i])
+                layouts[i].addWidget(self.text_boxes_value[i])
+                layouts[i].addWidget(self.text_boxes_min[i])
+                layouts[i].addWidget(self.text_boxes_max[i])
+                self.main_layout.addLayout(layouts[i])
+
+        sub_layout_h2.addWidget(self.label_search_path)
+        sub_layout_h2.addWidget(self.search_path_button)
+
+        sub_layout_h3.addWidget(self.log_box)
+        sub_layout_h3.addWidget(self.submit_button)
+        
+        sub_layout_h5.addWidget(self.label_name)
+        sub_layout_h5.addWidget(self.enter_name)
+        
+        sub_layout_h6.addWidget(self.label_path)
+        sub_layout_h6.addWidget(self.search_save_path_button)
+        
+        sub_layout_h7.addWidget(self.save_button)
+        
+        self.main_layout.addLayout(sub_layout_h2)
+        self.main_layout.addLayout(sub_layout_h3)
+        self.main_layout.addLayout(sub_layout_h4)
+        sub_layoutv = QVBoxLayout()
+        sub_layoutv.addLayout(sub_layout_h5)
+        sub_layoutv.addLayout(sub_layout_h6)
+        sub_layoutv.addLayout(sub_layout_h7)
+        files_group = QGroupBox()
+        files_group.setTitle('Save in external file (optional)')
+        files_group.setLayout(sub_layoutv)
+        self.main_layout.addWidget(files_group)
+
+        self.setLayout(self.main_layout)
+
+    def refresh(self):
+        pass
+
+    def submit_values(self):
+        nb_param=self.controler.nb_param
+        mesparam = np.zeros([nb_param,3])
+        error_interval = 0
+        try:
+            for i in range(nb_param):
+                if float(self.text_boxes_value[i].text())<float(self.text_boxes_min[i].text()):
+                    self.log_box.append(""The value in line {0} has to be superior to the minimum !"".format(i+1))
+                    error_interval = 1
+                if float(self.text_boxes_value[i].text())>float(self.text_boxes_max[i].text()):
+                    self.log_box.append(""The value in line {0} has to be inferior to the maximum !"".format(i+1))
+                    error_interval = 1
+                mesparam[i,0]=float(self.text_boxes_value[i].text())
+                mesparam[i,1]=float(self.text_boxes_min[i].text())
+                mesparam[i,2]=float(self.text_boxes_max[i].text())
+            if error_interval:
+                self.log_box.append(""Values not submitted ! There is a problem with the values and intervals of the parameters."")
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""Invalid values."")
+            return(0)
+        for i,layer in enumerate(self.controler.layerlist):                            #Here, all the variables are send to every fitted layers and interfaces, this could be improved
+            if layer.material.fit_material == 1:
+                self.controler.layerlist[i].material.change_param(mesparam[:,0],self.controler.myvariables,mesparam[:,1],mesparam[:,2])
+        for i,interface in enumerate(self.controler.interfaceList):
+            if interface.fit_metasurface == 1:
+                self.controler.interfaceList[i].change_param(mesparam[:,0],self.controler.myvariables,mesparam[:,1],mesparam[:,2])
+        self.controler.save_optimisation_param(mesparam)
+        self.log_box.append(""Values submitted"")
+        
+    def save_values(self):
+        nb_param=self.controler.nb_param
+        mesparam = np.zeros([nb_param,3])
+        error_interval = 0
+        try:
+            for i in range(nb_param):
+                if float(self.text_boxes_value[i].text())<float(self.text_boxes_min[i].text()):
+                    self.log_box.append(""The value in line {0} has to be superior to the minimum !"".format(i+1))
+                    error_interval = 1
+                if float(self.text_boxes_value[i].text())>float(self.text_boxes_max[i].text()):
+                    self.log_box.append(""The value in line {0} has to be inferior to the maximum !"".format(i+1))
+                    error_interval = 1
+                mesparam[i,0]=float(self.text_boxes_value[i].text())
+                mesparam[i,1]=float(self.text_boxes_min[i].text())
+                mesparam[i,2]=float(self.text_boxes_max[i].text())
+            if error_interval:
+                self.log_box.append(""Values not saved ! There is a problem with the values and intervals of the parameters."")
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""Invalid values."")
+            return(0)
+        name = self.enter_name.text()
+        if name == '':
+            self.log_box.append('Please enter a name')
+            return(0)
+        try:
+            self.controler.save_optimisation_param_outside(mesparam,self.dir_save_param,name)
+            self.log_box.append(""Values saved"")
+        except:
+            print(traceback.format_exc())
+            self.log_box.append(""Please enter a valid path"")
+            return(0)
+
+    def search_path(self):
+        if self.controler.initialised == 1:
+            nb_param=self.controler.nb_param
+            options = QFileDialog.Options()
+            # options |= QFileDialog.DontUseNativeDialog
+            # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"", options=options)
+            fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"")
+            try:
+                self.path_param_values=fileName
+                name=os.path.basename(fileName)
+                self.search_path_button.setText(name)
+                self.log_box.append(""Values taken from ""+name)
+            except:
+                print(traceback.format_exc())
+                self.controler.error_message_path()
+                return(0)
+
+            try:
+                self.controler.mesparam=np.loadtxt(self.path_param_values)
+                mes_param=self.controler.mesparam
+                if nb_param==len(mes_param[:,0]):
+                    for i in range(nb_param):
+                        self.text_boxes_value[i].setText('{0:.3E}'.format(mes_param[i,0]))
+                        self.text_boxes_min[i].setText('{0:.3E}'.format(mes_param[i,1]))
+                        self.text_boxes_max[i].setText('{0:.3E}'.format(mes_param[i,2]))
+                else:
+                    self.log_box.append(""The file submitted does not have the same number of parameters as the model chosen."")
+                    return(0)
+            except:
+                print(traceback.format_exc())
+                self.log_box.append(""There is a problem with the file submitted."")
+                return(0)
+        else:
+            self.log_box.append(""Please run the initialisation window first."")
+    
+    def search_save_path(self):
+        #find path to save parameters
+        DirectoryName = QFileDialog.getExistingDirectory(self,""Select Directory"")
+        try:
+            self.dir_save_param=str(DirectoryName)
+            name=os.path.basename(str(DirectoryName))
+            self.search_save_path_button.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path2()
+
+class log_param_values(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient2(self)
+#        self.setFixedHeight(150)
+        self.setReadOnly(True)
+        self.append(""Optimisation"")
+    def refresh(self):
+        pass
+
+###############################################################################
+###############################################################################
+#########################   Optimisation tab   ################################
+###############################################################################
+###############################################################################
+
+class Optimization_tab(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.setMinimumSize(640, 480)
+        self.controler=controler
+
+        # Creation Widgets
+        self.action_choice = QComboBox()
+        self.action_choice.addItems(['Optimize','Create fictional sample'])
+        self.action_choice.currentIndexChanged.connect(self.refresh)
+        self.action_choice.setMaximumWidth(380)
+        self.action_choice.setMaximumHeight(20)
+        self.action_widget = Action_handler(self,controler)
+        self.action_widget.refresh()
+        self.error_bars = Error_bars_handler(self,controler)
+        self.error_bars.refresh()
+        self.phase_correction = phase_correction_handler(self,controler)
+        self.phase_correction.refresh()
+        self.optim_param = Optimization_parameters(self,controler)
+        self.optim_param.refresh()
+        self.log_widget = log_optimisation(self,controler)
+        self.graph_widget = Graphs_optimisation(self,controler)
+    
+        # Preview button
+        self.preview_button = QPushButton(""Preview"")
+        self.preview_button.clicked.connect(self.preview)
+        self.preview_button.pressed.connect(self.pressed_loading)
+        self.preview_button.setMaximumWidth(380)
+        self.preview_button.setMaximumHeight(20)
+        
+        sub_layout_v = QVBoxLayout()
+        sub_layout_v.addWidget(self.action_choice,0)
+        sub_layout_v.addWidget(self.preview_button,0)
+        sub_layout_v.addWidget(self.phase_correction,0)
+        sub_layout_v.addWidget(self.action_widget,0)
+        sub_layout_v.addWidget(self.error_bars,0)
+        sub_layout_v.addWidget(self.optim_param,0)
+        sub_layout_v.addWidget(self.log_widget,0)
+        sub_layout_v.setAlignment(Qt.AlignTop)
+        
+        main_layout = QHBoxLayout()
+        main_layout.addLayout(sub_layout_v,0)
+        main_layout.addWidget(self.graph_widget,1)
+        self.setLayout(main_layout)
+        
+    def preview(self):
+        self.controler.preview()
+    def pressed_loading(self):
+        self.controler.loading_text3()
+        
+    def refresh(self):# /!\ Optimization_tab is not a client of the controler, 
+    #this is not called by the controler, only when action_choice is changed.
+        deleteLayout(self.action_widget.layout())
+        self.action_widget.refresh()
+        self.optim_param.refresh()
+        self.controler.refreshAll3('')
+
+class Action_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setMaximumWidth(440)
+
+    def refresh(self):
+        if self.parent.action_choice.currentIndex() == 0:
+            self.action_widget = Optimization_choices(self,self.controler)
+        else:
+            self.action_widget = FictionalSample_choices(self,self.controler)
+        try:
+            deleteLayout(self.layout())
+            self.layout().deleteLater()
+        except AttributeError:
+            main_layout = QVBoxLayout()
+            main_layout.addWidget(self.action_widget)
+            self.setLayout(main_layout)
+
+class Optimization_choices(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler=controler
+        self.parent = parent
+        self.emptycellfile = None
+        # Corrective factors
+        label_width=200
+        action_widget_width=150
+        corrective_width_factor=-12
+        
+        # Widget to see output directory
+        self.label_outputdir = QLabel('Output directory: ')
+        self.label_outputdir.setMaximumWidth(label_width)
+        self.button_outputdir = QPushButton('browse', self)
+        self.button_outputdir.clicked.connect(self.get_outputdir)
+        self.button_outputdir.setMaximumWidth(action_widget_width + corrective_width_factor)
+        self.button_outputdir.setMaximumHeight(30)
+
+
+        # Widget to name time domain output
+        self.label_time_domain = QLabel('Time domain output filename: ')
+        self.label_time_domain.setMaximumWidth(label_width)
+        self.enter_time_domain = QLineEdit()
+        self.enter_time_domain.setMaximumWidth(action_widget_width + corrective_width_factor)
+        self.enter_time_domain.setMaximumHeight(30)
+
+
+        # Widget to name frequency domain output
+        self.label_frequency_domain = QLabel('Frequency domain output filename: ')
+        self.label_frequency_domain.setMaximumWidth(label_width)
+        self.enter_frequency_domain = QLineEdit()
+        self.enter_frequency_domain.setMaximumWidth(action_widget_width + corrective_width_factor)
+        self.enter_frequency_domain.setMaximumHeight(30)
+
+
+        # Widget to name optimization output
+        self.label_out_opt = QLabel('Output optimization: ')
+        self.label_out_opt.setMaximumWidth(label_width)
+        self.enter_out_opt = QLineEdit()
+        self.enter_out_opt.setMaximumWidth(action_widget_width + corrective_width_factor)
+        self.enter_out_opt.setMaximumHeight(30)
+        
+        
+        # Algorithm choice
+        self.label_algo = QLabel(""Algorithm"")
+        self.label_algo.setMaximumWidth(label_width)
+        self.options_algo = QComboBox()
+        self.options_algo.addItems(['NumPy optimize swarm particle',
+                                    'ALPSO without parallelization',
+                                    'ALPSO with parallelization',
+                                    'SLSQP (pyOpt)',
+                                    'SLSQP (pyOpt with parallelization)',
+                                    'L-BFGS-B',
+                                    'SLSQP (scipy)',
+                                    'Dual annealing'])
+        self.options_algo.setMaximumWidth(action_widget_width+40)
+        self.options_algo.currentIndexChanged.connect(self.refresh_param)
+        
+        
+        # Target permittivity
+        self.label_eps = QLabel(""Compute the permittivity: "")
+        self.reference_label = QLabel('Empty cell (if 3 layers)')
+        self.reference_button = QPushButton(""browse"")
+        self.reference_button.setMaximumHeight(20)
+        self.reference_button.setMaximumWidth(action_widget_width)
+        self.reference_button.clicked.connect(self.search_file)
+        
+        self.eps_init_button = QPushButton(""with initial length"")
+        self.eps_init_button.setMaximumHeight(20)
+        self.eps_init_button.clicked.connect(self.compute_eps_ini)
+        self.eps_init_button.pressed.connect(self.pressed_loading)
+        self.eps_opti_button = QPushButton(""with the last optimum length"")
+        self.eps_opti_button.setMaximumHeight(20)
+        self.eps_opti_button.clicked.connect(self.compute_eps_opti)
+        self.eps_opti_button.pressed.connect(self.pressed_loading)
+        
+        # Error choice
+        self.label_error = QLabel(""Error function weighting ""+to_sup(1))
+        self.label_error.setMaximumWidth(label_width)
+        self.options_error = QComboBox()
+        self.options_error.addItems(['Constant','Custom weight', 'Custom noise', 'Noise matrix'])
+        self.options_error.setMaximumWidth(action_widget_width-12)
+        self.options_error.currentIndexChanged.connect(self.refresh_param)
+        
+        # # save error bars buttons #NOUREDDIN
+        # self.save_error_bars_button = QPushButton(""Save Error bars"") 
+        # self.save_error_bars_button.setMaximumHeight(20)
+        # self.save_error_bars_button.setMaximumWidth(action_widget_width)
+        # self.save_error_bars_button.clicked.connect(lambda: self.get_std_files_dialog.exec_()) #execute and show the dialog
+        
+        # # save error bars for models #NOUREDDIN
+        # self.model_errors_checkbox = QCheckBox(""Model errors"")
+        
+        # self.get_std_files_dialog = QDialog()
+        # self.get_std_files_dialog.setWindowTitle(f""Load Normalization files"")
+        # self.get_std_files_dialog.resize(750,200)
+        
+        
+        # std_ref_group = QGroupBox(f""Reference std file"")
+        # std_ref_group.setMaximumSize(800,100)
+        # std_ref_group.setMinimumSize(800,100)
+        
+        # std_sample_group = QGroupBox(f""Sample std file"")
+        # std_sample_group.setMaximumSize(800,100)
+        # std_sample_group.setMinimumSize(800,100)
+        
+        # saving_dir_group = QGroupBox(f""Saving directory"")
+        # saving_dir_group.setMaximumSize(800,100)
+        # saving_dir_group.setMinimumSize(800,100)
+        
+        # std_ref_layout = QHBoxLayout()
+        # std_sample_layout = QHBoxLayout()
+        # saving_dir_layout = QHBoxLayout()
+        # dialog_main_layout = QVBoxLayout()
+        
+        
+        # self.dialog_ref_textedit =  QTextEdit()
+        # self.dialog_ref_textedit.setMaximumHeight(30)
+        # self.dialog_ref_textedit.setMaximumWidth(750)
+        # self.dialog_ref_textedit.setEnabled(False)
+        
+        # self.dialog_sample_textedit =  QTextEdit()
+        # self.dialog_sample_textedit.setMaximumHeight(30)
+        # self.dialog_sample_textedit.setMaximumWidth(750)
+        # self.dialog_sample_textedit.setEnabled(False)
+        
+        # self.saving_dir_textedit =  QTextEdit()
+        # self.saving_dir_textedit.setMaximumHeight(30)
+        # self.saving_dir_textedit.setMaximumWidth(750)
+        # self.saving_dir_textedit.setEnabled(False)
+        
+        # self.browse_ref = QPushButton(f""browse"")
+        # self.browse_ref.setMaximumHeight(30)
+        # self.browse_ref.setMaximumWidth(75)
+        
+        # self.browse_sample = QPushButton(f""browse"")
+        # self.browse_sample.setMaximumHeight(30)
+        # self.browse_sample.setMaximumWidth(75)
+        
+        # self.save_dir_button = QPushButton(f""browse"")
+        # self.save_dir_button.setMaximumHeight(30)
+        # self.save_dir_button.setMaximumWidth(75)
+        
+        
+        # # self.ref_label = QLabel(f""Reference std file"")
+        # # self.sample_label = QLabel(f""Sample std file"")
+        
+        # self.ok_button = QPushButton(f""OK"")
+        # self.ok_button.setMaximumHeight(30)
+        # self.ok_button.setMaximumWidth(75)
+        
+        # std_ref_layout.addWidget(self.dialog_ref_textedit)
+        # std_ref_layout.addWidget(self.browse_ref)
+        
+        # std_sample_layout.addWidget(self.dialog_sample_textedit)
+        # std_sample_layout.addWidget(self.browse_sample)
+        
+        # saving_dir_layout.addWidget(self.saving_dir_textedit)
+        # saving_dir_layout.addWidget(self.save_dir_button)
+        
+        
+        # std_ref_group.setLayout(std_ref_layout)
+        # std_sample_group.setLayout(std_sample_layout)
+        # saving_dir_group.setLayout(saving_dir_layout)
+        
+        
+        # dialog_main_layout.addWidget(std_ref_group)
+        # dialog_main_layout.addWidget(std_sample_group)
+        # dialog_main_layout.addWidget(saving_dir_group)
+        # dialog_main_layout.addWidget(self.ok_button)
+        
+        # self.get_std_files_dialog.setLayout(dialog_main_layout)
+        
+        
+
+        # Creation layouts
+        sub_layout_h=QHBoxLayout()
+        sub_layout_h_2=QHBoxLayout()
+        sub_layout_h_3=QHBoxLayout()
+        sub_layout_h_4=QHBoxLayout()
+        sub_layout_h_5=QHBoxLayout()
+        sub_layout_h_6=QHBoxLayout()
+        sub_layout_h_7=QHBoxLayout()
+        sub_layout_h_8=QHBoxLayout()
+        # sub_layout_h_9=QHBoxLayout() # save error bars buttons #NOUREDDIN
+        # sub_layout_h_10=QHBoxLayout() # save error bars for models #NOUREDDIN
+        main_layout=QVBoxLayout()
+        
+
+        # Organisation layouts
+        
+        # Permittivity
+        sub_layout_h.addWidget(self.reference_label)
+        sub_layout_h.addWidget(self.reference_button)
+        sub_layout_h_2.addWidget(self.eps_init_button)
+        sub_layout_h_2.addWidget(self.eps_opti_button)
+        
+        sub_layout_h_3.addWidget(self.label_outputdir)
+        sub_layout_h_3.addWidget(self.button_outputdir)
+        sub_layout_h_4.addWidget(self.label_time_domain)
+        sub_layout_h_4.addWidget(self.enter_time_domain)
+        sub_layout_h_5.addWidget(self.label_frequency_domain)
+        sub_layout_h_5.addWidget(self.enter_frequency_domain)
+        sub_layout_h_6.addWidget(self.label_out_opt)
+        sub_layout_h_6.addWidget(self.enter_out_opt)
+        
+        # Organisation layouts for optimisation
+        sub_layout_h_7.addWidget(self.label_algo)
+        sub_layout_h_7.addWidget(self.options_algo)
+        
+        sub_layout_h_8.addWidget(self.label_error)
+        sub_layout_h_8.addWidget(self.options_error)
+        
+        # sub_layout_h_9.addWidget(self.save_error_bars_button) # save error bars buttons #NOUREDDIN
+
+        # Vertical layout        
+        main_layout.addWidget(self.label_eps)
+        #if (self.controler.nlayers == 3)&(self.controler.nfixed_material == 1):
+        main_layout.addLayout(sub_layout_h)
+        main_layout.addLayout(sub_layout_h_2)
+        main_layout.addLayout(sub_layout_h_3)
+        main_layout.addLayout(sub_layout_h_4)
+        main_layout.addLayout(sub_layout_h_5)
+        main_layout.addLayout(sub_layout_h_6)
+        # optimize
+        main_layout.addLayout(sub_layout_h_7)
+        main_layout.addLayout(sub_layout_h_8)
+        
+        # # main_layout.addLayout(sub_layout_h_9) # save error bars buttons #NOUREDDIN
+        
+        self.setLayout(main_layout)
+
+    def get_outputdir(self):
+        DirectoryName = QFileDialog.getExistingDirectory(self,""Select Directory"")
+        try:
+            self.outputdir=str(DirectoryName)
+            name=os.path.basename(str(DirectoryName))
+            self.button_outputdir.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path3()
+
+    def pressed_loading(self):
+        self.controler.loading_text3()
+
+    
+    def search_file(self):
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"")
+        try:
+            name=os.path.basename(fileName)
+            if name =='':
+                self.controler.refreshAll3('Please enter a valid path')
+                return(0)
+            self.reference_button.setText(name)
+            self.emptycellfile = fileName
+        except:
+            print(traceback.format_exc())
+            self.controler.refreshAll3('Please enter a valid path')
+            return(0)
+    
+    def compute_eps_ini(self, nbpi):
+        self.nbpi=0
+        self.controler.compute_eps_init(self.emptycellfile, self.nbpi)
+    
+    def compute_eps_opti(self):
+        self.controler.compute_eps_opti(self.emptycellfile)
+    
+    def refresh_param(self):
+        self.parent.parent.optim_param.refresh()
+        self.controler.errorIndex = self.options_error.currentIndex() # for graph
+
+class Error_bars_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setMaximumWidth(350)
+
+    def refresh(self):
+        self.error_widget = Error_bars(self,self.controler)
+        try:
+            deleteLayout(self.layout())
+            self.layout().deleteLater()
+        except AttributeError:
+            main_layout = QVBoxLayout()
+            main_layout.addWidget(self.error_widget)
+            self.setLayout(main_layout)
+
+class Error_bars(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setTitle(f""Error bars"")
+    
+    # def refresh(self):
+        self.ref_added = False
+        self.sample_added = False
+        
+        
+        
+        label_width = 75
+        textedit_width = 260
+        height = 20
+        button_width = 25
+        
+        sub_layout_h_1 = QHBoxLayout()
+        sub_layout_h_2 = QHBoxLayout()
+        sub_layout_h_3 = QHBoxLayout()
+        sub_layout_h_4 = QHBoxLayout()
+        sub_layout_h_5 = QHBoxLayout()
+        main_layout = QVBoxLayout()
+        
+        self.index_error_check = QCheckBox(f""Index error"")
+        sub_layout_h_1.addWidget(self.index_error_check)
+        
+        self.model_error_check = QCheckBox(f""Model error"")
+        sub_layout_h_1.addWidget(self.model_error_check)
+        # sub_layout_h_2.addWidget(self.model_error_check)
+        
+        self.stdref_label = QLabel(f""std ref"")
+        self.stdref_label.setMaximumSize(label_width,height)
+        
+        self.stdref_textedit = QLineEdit()
+        self.stdref_textedit.setMaximumSize(textedit_width,height)
+        self.stdref_textedit.setEnabled(False)
+        
+        self.stdref_browse = QPushButton(""..."") 
+        self.stdref_browse.setMaximumSize(button_width,height)
+        self.stdref_browse.setEnabled(False)
+        
+        
+        sub_layout_h_3.addWidget(self.stdref_label)
+        sub_layout_h_3.addWidget(self.stdref_textedit)
+        sub_layout_h_3.addWidget(self.stdref_browse)
+
+
+        self.stdsample_label = QLabel(f""std sample"")
+        self.stdsample_label.setMaximumSize(label_width,height)
+        
+        self.stdsample_textedit = QLineEdit()
+        self.stdsample_textedit.setMaximumSize(textedit_width,height)
+        self.stdsample_textedit.setEnabled(False)
+        
+        self.stdsample_browse = QPushButton(""..."") 
+        self.stdsample_browse.setMaximumSize(button_width,height)
+        self.stdsample_browse.setEnabled(False)
+        
+        
+        sub_layout_h_4.addWidget(self.stdsample_label)
+        sub_layout_h_4.addWidget(self.stdsample_textedit)
+        sub_layout_h_4.addWidget(self.stdsample_browse)
+        
+        self.submit_choices_button = QPushButton(f""Submit"")
+        self.submit_choices_button.setMaximumSize(75,height)
+        self.submit_choices_button.setEnabled(False)
+        
+        # sub_layout_h_5.addWidget(self.submit_choices_button)
+        sub_layout_h_1.addWidget(self.submit_choices_button)
+        
+        
+        main_layout.addLayout(sub_layout_h_1)
+        main_layout.addLayout(sub_layout_h_2)
+        main_layout.addLayout(sub_layout_h_3)
+        main_layout.addLayout(sub_layout_h_4)
+        main_layout.addLayout(sub_layout_h_5)
+        
+        
+        self.setLayout(main_layout)
+        # self.setMaximumHeight(350)
+        
+        self.index_error_check.stateChanged.connect(self.err_change_choice)
+        self.model_error_check.stateChanged.connect(self.err_change_choice)
+        self.stdref_browse.clicked.connect(self.browse_std_ref_file)
+        self.stdsample_browse.clicked.connect(self.browse_std_sample_file)
+        self.submit_choices_button.clicked.connect(self.submit_err_params)
+        
+        self.err_change_choice()
+        
+    def submit_err_params(self):
+        temp_file_dir = path_(ROOT_DIR).joinpath(f""temp"")
+        if not temp_file_dir.is_dir():
+            path_(temp_file_dir).mkdir()
+        
+        temp_file = temp_file_dir.joinpath(f""temp_err_bool.bin"")
+        index_errors_bool = self.index_error_check.isChecked()
+        model_errors_bool = self.model_error_check.isChecked()
+        bools = [index_errors_bool, model_errors_bool]
+        with open(temp_file, 'wb') as f:
+            # pickle.dump(index_errors_bool,f,pickle.HIGHEST_PROTOCOL)
+            pickle.dump(bools,f,pickle.HIGHEST_PROTOCOL)
+        
+        
+        err_params = temp_file_dir.joinpath(f""temp_err_files.bin"")
+        filepaths = [self.stdref_textedit.text(), self.stdsample_textedit.text()]
+        with open(err_params, ""wb"") as file:
+            pickle.dump(filepaths,file,pickle.HIGHEST_PROTOCOL)
+        
+    
+    def browse_std_ref_file(self):
+        ref_name, _ = QFileDialog.getOpenFileName(parent=self, caption= f""Select the correct@tds std_freq file for the reference"", directory=f""{ROOT_DIR}"",filter=""text file (*.txt)"")
+        self.stdref_textedit.setText(f""{ref_name}"")
+        self.stdref_textedit.setEnabled(True)
+        self.ref_added = True
+        if self.ref_added and  self.sample_added:
+            self.submit_choices_button.setEnabled(True)
+    
+    def browse_std_sample_file(self):
+        sample_name, _ = QFileDialog.getOpenFileName(parent=self, caption= f""Select the correct@tds std_freq file for the sample"", directory=f""{ROOT_DIR}"",filter=""text file (*.txt)"")
+        self.stdsample_textedit.setText(f""{sample_name}"")
+        self.stdsample_textedit.setEnabled(True)
+        self.sample_added = True
+        if self.sample_added and self.ref_added :
+            self.submit_choices_button.setEnabled(True)
+            
+    
+    def err_change_choice(self):
+
+        index_errors_bool = self.index_error_check.isChecked()
+        model_errors_bool = self.model_error_check.isChecked()
+        
+        if index_errors_bool:
+            self.stdref_browse.setEnabled(True)
+            self.stdsample_browse.setEnabled(True)
+        else:
+            self.stdref_browse.setEnabled(False)
+            self.stdsample_browse.setEnabled(False)
+        
+        if model_errors_bool:
+            self.submit_choices_button.setEnabled(True)
+        elif not model_errors_bool and not self.ref_added and not self.sample_added:
+            self.submit_choices_button.setEnabled(False)
+            
+    
+    # def model_err_change_choice(self):
+        # 
+        # index_errors_bool = self.index_error_check.isChecked()
+        # temp_mod_file = path_(ROOT_DIR).joinpath(f""temp"").joinpath(f""temp_model_err.bin"")
+        # model_errors_bool = self.model_error_check.isChecked()
+        # 
+        # with open(temp_mod_file, 'wb') as f:
+            # pickle.dump(model_errors_bool,f,pickle.HIGHEST_PROTOCOL)
+
+
+
+
+class phase_correction_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setMaximumWidth(380)
+
+    def refresh(self):
+        self.phase_widget = phase_correction(self,self.controler)
+        try:
+            deleteLayout(self.layout())
+            self.layout().deleteLater()
+        except AttributeError:
+            main_layout = QVBoxLayout()
+            main_layout.addWidget(self.phase_widget)
+            self.setLayout(main_layout)
+
+class phase_correction(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.emptycellfile = None
+        self.setTitle(f""Phase correction"")
+
+        self.label_phase = QLabel(""Number of pi correction"")
+        self.enter_phase = QLineEdit()
+        self.enter_phase.setMaximumWidth(50)
+        self.enter_phase.setMaximumHeight(30)
+        self.submit_phase = QPushButton(""Submit"")
+        self.submit_phase.clicked.connect(self.compute_eps_phase_correction)
+        self.submit_phase.pressed.connect(self.pressed_loading)
+        self.submit_phase.setMaximumWidth(50)
+        self.submit_phase.setMaximumHeight(20)
+
+        # Creation layouts
+        sub_layout_h=QHBoxLayout()
+        main_layout=QVBoxLayout()
+        sub_layout_h.addWidget(self.label_phase)
+        sub_layout_h.addWidget(self.enter_phase)
+        sub_layout_h.addWidget(self.submit_phase)
+        main_layout.addLayout(sub_layout_h)
+
+        self.setLayout(main_layout)
+
+    def compute_eps_phase_correction(self):
+        self.nbpi=float(self.enter_phase.text())
+        nbpi=self.nbpi
+        f=open(os.path.join(""temp"",'temp_file_phase.bin'),'wb')
+        pickle.dump(nbpi,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.controler.compute_eps_phase_corraction(self.emptycellfile)
+
+    def pressed_loading(self):
+        self.controler.loading_text3()
+    
+        
+class Error_bars_handler(QWidget):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setMaximumWidth(350)
+
+    def refresh(self):
+        self.error_widget = Error_bars(self,self.controler)
+        try:
+            deleteLayout(self.layout())
+            self.layout().deleteLater()
+        except AttributeError:
+            main_layout = QVBoxLayout()
+            main_layout.addWidget(self.error_widget)
+            self.setLayout(main_layout)
+
+class Error_bars(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setTitle(f""Error bars"")
+    
+    # def refresh(self):
+        self.ref_added = False
+        self.sample_added = False
+        
+        
+        
+        label_width = 75
+        textedit_width = 260
+        height = 20
+        button_width = 25
+        
+        sub_layout_h_1 = QHBoxLayout()
+        sub_layout_h_2 = QHBoxLayout()
+        sub_layout_h_3 = QHBoxLayout()
+        sub_layout_h_4 = QHBoxLayout()
+        sub_layout_h_5 = QHBoxLayout()
+        main_layout = QVBoxLayout()
+        
+        self.index_error_check = QCheckBox(f""Index error"")
+        sub_layout_h_1.addWidget(self.index_error_check)
+        
+        self.model_error_check = QCheckBox(f""Model error"")
+        sub_layout_h_1.addWidget(self.model_error_check)
+        # sub_layout_h_2.addWidget(self.model_error_check)
+        
+        self.stdref_label = QLabel(f""std ref"")
+        self.stdref_label.setMaximumSize(label_width,height)
+        
+        self.stdref_textedit = QLineEdit()
+        self.stdref_textedit.setMaximumSize(textedit_width,height)
+        self.stdref_textedit.setEnabled(False)
+        
+        self.stdref_browse = QPushButton(""..."") 
+        self.stdref_browse.setMaximumSize(button_width,height)
+        self.stdref_browse.setEnabled(False)
+        
+        
+        sub_layout_h_3.addWidget(self.stdref_label)
+        sub_layout_h_3.addWidget(self.stdref_textedit)
+        sub_layout_h_3.addWidget(self.stdref_browse)
+
+
+        self.stdsample_label = QLabel(f""std sample"")
+        self.stdsample_label.setMaximumSize(label_width,height)
+        
+        self.stdsample_textedit = QLineEdit()
+        self.stdsample_textedit.setMaximumSize(textedit_width,height)
+        self.stdsample_textedit.setEnabled(False)
+        
+        self.stdsample_browse = QPushButton(""..."") 
+        self.stdsample_browse.setMaximumSize(button_width,height)
+        self.stdsample_browse.setEnabled(False)
+        
+        
+        sub_layout_h_4.addWidget(self.stdsample_label)
+        sub_layout_h_4.addWidget(self.stdsample_textedit)
+        sub_layout_h_4.addWidget(self.stdsample_browse)
+        
+        self.submit_choices_button = QPushButton(f""Submit"")
+        self.submit_choices_button.setMaximumSize(75,height)
+        self.submit_choices_button.setEnabled(False)
+        
+        # sub_layout_h_5.addWidget(self.submit_choices_button)
+        sub_layout_h_1.addWidget(self.submit_choices_button)
+        
+        
+        main_layout.addLayout(sub_layout_h_1)
+        main_layout.addLayout(sub_layout_h_2)
+        main_layout.addLayout(sub_layout_h_3)
+        main_layout.addLayout(sub_layout_h_4)
+        main_layout.addLayout(sub_layout_h_5)
+        
+        
+        self.setLayout(main_layout)
+        # self.setMaximumHeight(350)
+        
+        self.index_error_check.stateChanged.connect(self.err_change_choice)
+        self.model_error_check.stateChanged.connect(self.err_change_choice)
+        self.stdref_browse.clicked.connect(self.browse_std_ref_file)
+        self.stdsample_browse.clicked.connect(self.browse_std_sample_file)
+        self.submit_choices_button.clicked.connect(self.submit_err_params)
+        
+        self.err_change_choice()
+        
+    def submit_err_params(self):
+        temp_file_dir = path_(ROOT_DIR).joinpath(f""temp"")
+        if not temp_file_dir.is_dir():
+            path_(temp_file_dir).mkdir()
+        
+        temp_file = temp_file_dir.joinpath(f""temp_err_bool.bin"")
+        index_errors_bool = self.index_error_check.isChecked()
+        model_errors_bool = self.model_error_check.isChecked()
+        bools = [index_errors_bool, model_errors_bool]
+        with open(temp_file, 'wb') as f:
+            # pickle.dump(index_errors_bool,f,pickle.HIGHEST_PROTOCOL)
+            pickle.dump(bools,f,pickle.HIGHEST_PROTOCOL)
+        
+        
+        err_params = temp_file_dir.joinpath(f""temp_err_files.bin"")
+        filepaths = [self.stdref_textedit.text(), self.stdsample_textedit.text()]
+        with open(err_params, ""wb"") as file:
+            pickle.dump(filepaths,file,pickle.HIGHEST_PROTOCOL)
+        
+    
+    def browse_std_ref_file(self):
+        ref_name, _ = QFileDialog.getOpenFileName(parent=self, caption= f""Select the correct@tds std_freq file for the reference"", directory=f""{ROOT_DIR}"",filter=""text file (*.txt)"")
+        self.stdref_textedit.setText(f""{ref_name}"")
+        self.stdref_textedit.setEnabled(True)
+        self.ref_added = True
+        if self.ref_added and  self.sample_added:
+            self.submit_choices_button.setEnabled(True)
+    
+    def browse_std_sample_file(self):
+        sample_name, _ = QFileDialog.getOpenFileName(parent=self, caption= f""Select the correct@tds std_freq file for the sample"", directory=f""{ROOT_DIR}"",filter=""text file (*.txt)"")
+        self.stdsample_textedit.setText(f""{sample_name}"")
+        self.stdsample_textedit.setEnabled(True)
+        self.sample_added = True
+        if self.sample_added and self.ref_added :
+            self.submit_choices_button.setEnabled(True)
+            
+    
+    def err_change_choice(self):
+
+        index_errors_bool = self.index_error_check.isChecked()
+        model_errors_bool = self.model_error_check.isChecked()
+        
+        if index_errors_bool:
+            self.stdref_browse.setEnabled(True)
+            self.stdsample_browse.setEnabled(True)
+        else:
+            self.stdref_browse.setEnabled(False)
+            self.stdsample_browse.setEnabled(False)
+        
+        if model_errors_bool:
+            self.submit_choices_button.setEnabled(True)
+        elif not model_errors_bool and not self.ref_added and not self.sample_added:
+            self.submit_choices_button.setEnabled(False)
+            
+    
+    # def model_err_change_choice(self):
+        # 
+        # index_errors_bool = self.index_error_check.isChecked()
+        # temp_mod_file = path_(ROOT_DIR).joinpath(f""temp"").joinpath(f""temp_model_err.bin"")
+        # model_errors_bool = self.model_error_check.isChecked()
+        # 
+        # with open(temp_mod_file, 'wb') as f:
+            # pickle.dump(model_errors_bool,f,pickle.HIGHEST_PROTOCOL)
+
+
+
+
+class Optimization_parameters(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.parent = parent
+        self.controler = controler
+        self.setTitle(""Optimization parameters"")
+
+    def refresh(self):
+        self.optim_index = self.parent.action_choice.currentIndex()
+        if self.optim_index == 0:
+            self.errorFile = None
+            self.choices = self.parent.action_widget.action_widget
+            self.algo_index = self.choices.options_algo.currentIndex()
+            self.errorweight_index = self.choices.options_error.currentIndex()
+            # Corrective factors
+            action_widget_width=150
+            corrective_width_factor=-12
+            # SwarmSize
+            self.label_swarmsize = QLabel(""Swarmsize"")
+            self.enter_swarmsize = QLineEdit()
+            self.enter_swarmsize.setMaximumWidth(action_widget_width +corrective_width_factor)
+            self.enter_swarmsize.setMaximumHeight(30)
+    
+            # Number of iterations
+            self.label_niter = QLabel(""Number of iterations"")
+            self.enter_niter = QLineEdit()
+            self.enter_niter.setMaximumWidth(action_widget_width +corrective_width_factor)
+            self.enter_niter.setMaximumHeight(30)
+    
+            # Wiget to see how many process are going to be used for the omptimization
+            self.label_nb_proc = QLabel(""How many process do you want to use?"")
+            self.enter_nb_proc = QLineEdit()
+            self.enter_nb_proc.setText('1')
+            self.enter_nb_proc.setMaximumWidth(50)
+            self.enter_nb_proc.setMaximumHeight(25)
+            
+            # Files needed for error function
+            if self.errorweight_index == 1:
+                self.label_customweight = QLabel('Weight')
+                self.button_errorFile = QPushButton('browse', self)
+                self.button_errorFile.clicked.connect(self.get_weight)
+                self.button_errorFile.setMaximumWidth(action_widget_width + corrective_width_factor)
+                self.button_errorFile.setMaximumHeight(30)
+                
+            elif self.errorweight_index == 2:
+                self.label_customweight = QLabel('Noise')
+                self.button_errorFile = QPushButton('browse', self)
+                self.button_errorFile.clicked.connect(self.get_weight)
+                self.button_errorFile.setMaximumWidth(action_widget_width + corrective_width_factor)
+                self.button_errorFile.setMaximumHeight(30)
+                
+            elif self.errorweight_index == 3:
+                self.label_customweight = QLabel('Noise matrix')
+                self.button_errorFile = QPushButton('browse', self)
+                self.button_errorFile.clicked.connect(self.get_weight)
+                self.button_errorFile.setMaximumWidth(action_widget_width + corrective_width_factor)
+                self.button_errorFile.setMaximumHeight(30)
+            
+            # Button to launch optimization
+            self.begin_button = QPushButton(""Begin"")
+            self.begin_button.clicked.connect(self.begin_optimization)
+            self.begin_button.pressed.connect(self.pressed_loading)
+            #self.begin_button.setMaximumWidth(50)
+            self.begin_button.setMaximumHeight(20)
+            
+            #button to test error guess
+#            self.testguess_button = QPushButton(""Test Guess"")
+#            self.testguess_button.clicked.connect(self.optim.guesstest())
+#            self.testguess_button.pressed.connect(self.pressed_loading)
+#            self.testguess_button.setMaximumHeight(20)
+            
+            sub_layout_h1=QHBoxLayout()
+            sub_layout_h2=QHBoxLayout()
+            sub_layout_h3=QHBoxLayout()
+            sub_layout_h4=QHBoxLayout()
+            
+            sub_layout_h1.addWidget(self.label_nb_proc,0)
+            sub_layout_h1.addWidget(self.enter_nb_proc,0)
+            sub_layout_h2.addWidget(self.label_swarmsize,0)
+            sub_layout_h2.addWidget(self.enter_swarmsize,0)
+            sub_layout_h3.addWidget(self.label_niter,0)
+            sub_layout_h3.addWidget(self.enter_niter,0)
+            if self.errorweight_index == 1:
+                sub_layout_h4.addWidget(self.label_customweight)
+                sub_layout_h4.addWidget(self.button_errorFile)
+            elif self.errorweight_index ==2:
+                sub_layout_h4.addWidget(self.label_customweight)
+                sub_layout_h4.addWidget(self.button_errorFile)
+            elif self.errorweight_index ==3:
+                sub_layout_h4.addWidget(self.label_customweight)
+                sub_layout_h4.addWidget(self.button_errorFile)
+            self.main_layout=QVBoxLayout()
+            self.main_layout.addLayout(sub_layout_h1,0)
+            if self.algo_index < 3:
+                self.main_layout.addLayout(sub_layout_h2)
+            self.main_layout.addLayout(sub_layout_h3)
+            if self.errorweight_index == 1:
+                self.main_layout.addLayout(sub_layout_h4)
+            elif self.errorweight_index == 2:
+                self.main_layout.addLayout(sub_layout_h4)
+            elif self.errorweight_index == 3:
+                self.main_layout.addLayout(sub_layout_h4)
+            self.main_layout.addWidget(self.begin_button)
+#            self.main_layout.addWidget(self.testguess_button)
+            try:
+                deleteLayout(self.layout())
+                self.layout().deleteLater()
+            except AttributeError:
+                self.setLayout(self.main_layout)
+            self.setMaximumHeight(450)
+        
+        else:
+            try:
+                deleteLayout(self.layout())
+                self.setMaximumHeight(0)
+            except:
+                print(traceback.format_exc())
+                pass
+    
+    def pressed_loading(self):
+        self.controler.loading_text3()
+        
+
+        
+    def get_weight(self):
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"")
+        #print(fileName)
+        if self.controler.initialised == 0:
+            self.controler.refreshAll3(""Please run initialisation first"")
+            return(0)
+        if self.controler.errorIndex != 3:
+            try:
+                name=os.path.basename(fileName)
+                if name =='':
+                    self.controler.refreshAll3('Please enter a valid path')
+                    return(0)
+                errorweight = np.loadtxt(fileName)
+                if (len(errorweight) != self.controler.nsample):
+                    self.controler.refreshAll3('Please enter a valid path. The file should have {} lines'.format(self.controler.nsample))
+                    return(0)
+                self.button_errorFile.setText(name)
+                self.errorFile = fileName
+                self.controler.errorFile = fileName # for graphs
+            except:
+                print(traceback.format_exc())
+                self.controler.refreshAll3('Please enter a valid path')
+                return(0)
+        else:
+            #errorweightdata = h5py.File(fileName, 'r')
+            #nameerr = list(errorweightdata.keys())[0]
+            #errorweight = list(errorweightdata[nameerr])
+            name=os.path.basename(fileName)
+            self.button_errorFile.setText(name)
+            self.errorFile = fileName
+            self.controler.errorFile = fileName # for graphs
+            try:
+                errorweightdata = h5py.File(fileName, 'r')
+                nameerr = list(errorweightdata.keys())[0]
+                errorweight = list(errorweightdata[nameerr])
+                if np.shape(errorweight)[0] != self.controler.nsample or np.shape(errorweight)[1] != self.controler.nsample:
+                    self.refreshAll3('Please enter a valid path. The file should be a {} square matrix'.format(self.controler.nsample))
+                    return 0
+            except:
+                print(traceback.format_exc())
+                self.controler.refreshAll3('Please enter a valid path')
+
+            
+    def submit_algo_param(self):
+        choix_algo=self.algo_index
+        if (choix_algo == 3 or choix_algo == 4):
+            try:
+                from pyOpt import SLSQP
+            except:
+                print(traceback.format_exc())
+                self.controler.refreshAll3(""SLSQP was not imported successfully and can't be used"")
+                return(0)
+        swarmsize = 0
+        if choix_algo <3: #particle swarm
+            try:
+                swarmsize=int(self.enter_swarmsize.text())
+                if swarmsize<0:
+                    self.controler.invalid_swarmsize()
+                    return(0)
+            except:
+                print(traceback.format_exc())
+                self.controler.invalid_swarmsize()
+                return(0)
+        try:
+            niter=int(self.enter_niter.text())
+            if niter<0:
+                self.controler.invalid_niter()
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.invalid_niter()
+            return(0)
+        self.controler.algo_parameters(choix_algo,swarmsize,niter,self.errorweight_index,self.errorFile)
+        
+        if self.controler.is_temp_file_1 == 0:
+            self.controler.no_temp_file_1()
+            return(0)
+        if self.controler.is_temp_file_2 == 0:
+            self.controler.no_temp_file_2()
+            return(0)
+        try:
+            self.time_domain  = str(self.choices.enter_time_domain.text())
+            if self.time_domain=="""":
+                self.controler.error_message_output_filename()
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_output_filename()
+            return(0)
+        try:
+            self.frequency_domain = str(self.choices.enter_frequency_domain.text())
+            if self.frequency_domain=="""":
+                self.controler.error_message_output_filename()
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_output_filename()
+            return(0)
+        try:
+            self.out_opt_filename  = str(self.choices.enter_out_opt.text())
+            if self.out_opt_filename=="""":
+                self.controler.error_message_output_filename()
+                return(0)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_output_filename()
+            return(0)
+        try:
+            self.controler.get_output_paths(self.choices.outputdir,self.time_domain,
+                                            self.frequency_domain, self.out_opt_filename)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_output_paths()
+            return(0)
+        return(1)
+                
+    def begin_optimization(self):
+        global graph_option_2
+        submitted = self.submit_algo_param()    #get values from optimisation widget
+        if submitted == 1:
+            try:
+                from mpi4py import MPI
+                nb_proc=int(self.enter_nb_proc.text())
+            except:
+                print(traceback.format_exc())
+                self.controler.message_log_tab3(""You don't have MPI for parallelization, we'll use only 1 process"")
+                nb_proc=1
+            if self.controler.is_temp_file_5 == 1:
+                self.controler.begin_optimization(nb_proc)
+                graph_option_2='Pulse (E_field)'
+            else:
+                self.controler.no_temp_file_5()
+    
+class FictionalSample_choices(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        
+        label_width=200
+        action_widget_width=150
+        corrective_width_factor=-12
+        
+        # Widget to see output directory
+        self.label_outputdir = QLabel('Directory: ')
+        self.label_outputdir.setMaximumWidth(label_width)
+        self.button_outputdir = QPushButton('browse', self)
+        self.button_outputdir.clicked.connect(self.get_outputdir)
+        self.button_outputdir.setMaximumWidth(action_widget_width +
+                                           corrective_width_factor)
+        self.button_outputdir.setMaximumHeight(30)
+        
+        # Widget to name file
+        self.label_name = QLabel('File name: ')
+        self.label_name.setMaximumWidth(label_width)
+        self.enter_name = QLineEdit()
+        self.enter_name.setMaximumWidth(action_widget_width +
+                                           corrective_width_factor)
+        self.enter_name.setMaximumHeight(30)
+        
+        # Widget to eneter temporal noise standard deviation
+        self.label_tempnoise = QLabel('Temporal noise standard deviation (s): ')
+        self.label_tempnoise.setMaximumWidth(label_width)
+        self.enter_tempnoise = QLineEdit()
+        self.enter_tempnoise.setText('0')
+        self.enter_tempnoise.setMaximumWidth(action_widget_width +
+                                           corrective_width_factor)
+        self.enter_tempnoise.setMaximumHeight(30)
+        
+        # Widget to eneter amplitude noise standard deviation
+        self.label_ampnoise = QLabel('Amplitude noise standard deviation: ')
+        self.label_ampnoise.setMaximumWidth(label_width)
+        self.enter_ampnoise = QLineEdit()
+        self.enter_ampnoise.setText('0')
+        self.enter_ampnoise.setMaximumWidth(action_widget_width +
+                                           corrective_width_factor)
+        self.enter_ampnoise.setMaximumHeight(30)
+        
+        # Button to create fake sample
+        self.fiction_button = QPushButton(""Create fictional sample"")
+        self.fiction_button.clicked.connect(self.generateFictionalSample)
+        self.fiction_button.pressed.connect(self.pressed_loading)
+        self.fiction_button.setMaximumHeight(20)
+        
+        # Target permittivity
+        self.reference_label = QLabel('Empty cell')
+        self.reference_button = QPushButton(""browse"")
+        self.reference_button.setMaximumHeight(20)
+        self.reference_button.setMaximumWidth(action_widget_width)
+        self.reference_button.clicked.connect(self.search_file)
+        
+        self.eps_init_button = QPushButton(""Compute the permittivity"")
+        self.eps_init_button.setMaximumHeight(20)
+        self.eps_init_button.clicked.connect(self.compute_eps_ini)
+        self.eps_init_button.pressed.connect(self.pressed_loading)
+        
+        # Creation layouts
+        main_layout = QVBoxLayout()
+        sub_layout_h1=QHBoxLayout()
+        sub_layout_h2=QHBoxLayout()
+        sub_layout_h3=QHBoxLayout()
+        sub_layout_h4=QHBoxLayout()
+        sub_layout_h5=QHBoxLayout()
+        
+        # Organisation layouts for fictional sample
+        sub_layout_h1.addWidget(self.label_outputdir)
+        sub_layout_h1.addWidget(self.button_outputdir)
+        sub_layout_h2.addWidget(self.label_name)
+        sub_layout_h2.addWidget(self.enter_name)
+        sub_layout_h3.addWidget(self.label_tempnoise)
+        sub_layout_h3.addWidget(self.enter_tempnoise)
+        sub_layout_h4.addWidget(self.label_ampnoise)
+        sub_layout_h4.addWidget(self.enter_ampnoise)
+        # Permittivity
+        sub_layout_h5.addWidget(self.reference_label)
+        sub_layout_h5.addWidget(self.reference_button)
+        
+        if (self.controler.nlayers == 3)&(self.controler.nfixed_material ==1):
+            main_layout.addLayout(sub_layout_h5)
+        main_layout.addWidget(self.eps_init_button)
+        main_layout.addLayout(sub_layout_h1)
+        main_layout.addLayout(sub_layout_h2)
+        main_layout.addLayout(sub_layout_h3)
+        main_layout.addLayout(sub_layout_h4)
+        main_layout.addWidget(self.fiction_button)
+        
+        self.setLayout(main_layout)
+        
+    def pressed_loading(self):
+        self.controler.loading_text3()
+    def compute_eps_ini(self, nbpi):
+        self.nbpi=0
+        self.controler.compute_eps_init(self.emptycellfile, self.nbpi)
+
+    def generateFictionalSample(self): #files
+        name = self.enter_name.text()
+        try:
+            name  = str(self.enter_name.text())
+            if name=="""":
+                self.controler.refreshAll3(""Please enter a valid name"")
+        except:
+            print(traceback.format_exc())
+            self.controler.refreshAll3(""Please enter a valid name"")
+        try:
+            directory = self.outputdir
+        except AttributeError:
+            directory = None
+        tempstd = float(self.enter_tempnoise.text())
+        ampstd  = float(self.enter_ampnoise.text())
+        self.controler.generateFictionalSample(tempstd,ampstd,name,directory)
+    
+    
+    def get_outputdir(self):
+        DirectoryName = QFileDialog.getExistingDirectory(self,""Select Directory"")
+        try:
+            self.outputdir=str(DirectoryName)
+            name=os.path.basename(str(DirectoryName))
+            self.button_outputdir.setText(name)
+        except:
+            print(traceback.format_exc())
+            self.controler.error_message_path3()
+    def search_file(self):
+        options = QFileDialog.Options()
+        # options |= QFileDialog.DontUseNativeDialog
+        # fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"", options=options)
+        fileName, _ = QFileDialog.getOpenFileName(self,""QFileDialog.getOpenFileName()"", """",""All Files (*);;Python Files (*.py)"")
+        try:
+            name=os.path.basename(fileName)
+            if name =='':
+                self.controler.refreshAll3('Please enter a valid path')
+                return(0)
+            self.reference_button.setText(name)
+            self.emptycellfile = fileName
+        except:
+            print(traceback.format_exc())
+            self.controler.refreshAll3('Please enter a valid path')
+            return(0)
+
+
+class log_optimisation(QTextEdit):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient3(self)
+        self.setReadOnly(True)
+
+        references=[to_sup(1)+"" Error options:"",
+                    ""Constant weight: error = \u2016Efit(w)-E(w)\u2016 / \u2016E(w)\u2016"",
+                    ""or error = \u2016Efit(t)-E(t)\u2016 / \u2016E(t)\u2016 in super resolution"",
+                    ""Custom weight: error \u03B1 \u2016(Efit(t)-E(t))*weight \u2016 / \u2016E(t)\u2016 "",
+                    ""Custom noise: error \u03B1 \u2016(Efit(t)-E(t))/noise \u2016 / \u2016E(t)\u2016 "",
+                    ""Custom noise matrix: error ...""]
+        for i in references:
+            self.append(i)
+
+    def refresh(self):
+        message = self.controler.message
+        if type(message)==list:
+            for i in message:
+                self.append(i)
+        else:
+            self.append(message)
+
+class Graphs_optimisation(QGroupBox):
+    def __init__(self, parent, controler):
+        super().__init__(parent)
+        self.controler = controler
+        self.controler.addClient3(self)
+        self.setTitle(""Graphs"")
+        # Create objects to plot graphs
+        self.figure = plt.figure()
+        self.canvas = FigureCanvas(self.figure)
+        self.toolbar = NavigationToolbar(self.canvas, self)
+        self.canvas.draw()
+        # Create buttons to chose what to plot
+        # Real part of refractive index
+        self.button_real_index = QPushButton('Real(n)', self)
+        self.button_real_index.clicked.connect(self.real_index_graph)
+        # Imaginary part of refractive index
+        self.button_im_index = QPushButton('Im(n)', self)
+        self.button_im_index.clicked.connect(self.im_index_graph)
+        # E field
+        self.button_E_field = QPushButton('E field', self)
+        self.button_E_field.clicked.connect(self.E_field_graph)
+        # E field [dB]
+        self.button_E_field_dB = QPushButton('E field  [dB]', self)
+        self.button_E_field_dB.clicked.connect(self.E_field_dB_graph)
+        # Pulse (E field)
+        self.button_Pulse_E_field = QPushButton('Pulse E field', self)
+        self.button_Pulse_E_field.clicked.connect(self.Pulse_E_field_graph)
+        # Pulse (E field) [dB]
+        self.button_Pulse_E_field_dB = QPushButton('Pulse E field [dB]', self)
+        self.button_Pulse_E_field_dB.clicked.connect(self.Pulse_E_field_dB_graph)
+        # frequency filter
+        self.button_freq_filter = QPushButton('Frequency filter')
+        self.button_freq_filter.clicked.connect(self.freq_filter_graph)
+        # temporal filter
+        self.button_temp_filter = QPushButton('Temporal filter')
+        self.button_temp_filter.clicked.connect(self.temp_filter_graph)
+        # E field residue
+        self.button_E_field_residue = QPushButton('E field residue', self)
+        self.button_E_field_residue.clicked.connect(self.E_field_residue_graph)
+        # E field residue [dB]
+        self.button_E_field_residue_dB = QPushButton('E field residue [dB]', self)
+        self.button_E_field_residue_dB.clicked.connect(self.E_field_residue_dB_graph)
+        # Pulse residue (E field)
+        self.button_Pulse_E_field_residue = QPushButton('E(t) residue', self)
+        self.button_Pulse_E_field_residue.clicked.connect(self.Pulse_E_field_residue_graph)
+        # Pulse residue (E field) [dB]
+        self.button_Pulse_E_field_residue_dB = QPushButton('E(t) residue [dB]', self)
+        self.button_Pulse_E_field_residue_dB.clicked.connect(self.Pulse_E_field_residue_dB_graph)
+
+
+        # Organisation layout
+        self.vlayoutmain = QVBoxLayout()
+        self.hlayout = QHBoxLayout()
+        self.hlayout2 = QHBoxLayout()
+        self.hlayout.addWidget(self.button_real_index)
+        self.hlayout.addWidget(self.button_im_index)
+        self.hlayout.addWidget(self.button_E_field)
+        self.hlayout.addWidget(self.button_E_field_dB)
+        self.hlayout.addWidget(self.button_Pulse_E_field)
+        self.hlayout.addWidget(self.button_Pulse_E_field_dB)
+        self.hlayout2.addWidget(self.button_freq_filter)
+        self.hlayout2.addWidget(self.button_temp_filter)
+        self.hlayout2.addWidget(self.button_E_field_residue)
+        self.hlayout2.addWidget(self.button_E_field_residue_dB)
+        self.hlayout2.addWidget(self.button_Pulse_E_field_residue)
+        self.hlayout2.addWidget(self.button_Pulse_E_field_residue_dB)
+        self.vlayoutmain.addWidget(self.toolbar)
+        self.vlayoutmain.addWidget(self.canvas)
+        self.vlayoutmain.addLayout(self.hlayout)
+        self.vlayoutmain.addLayout(self.hlayout2)
+        self.setLayout(self.vlayoutmain)
+#        self.drawgraph()
+
+
+    def draw_graph_init(self,myinputdata, myreferencedata, myfitteddata, epsilonTarget,
+                        myglobalparameters, freqWindow, timeWindow, weight, noise, noisematrix, errorweight_index):
+        global graph_option_2
+        self.figure.clf()
+        ax1 = self.figure.add_subplot(111)
+        if graph_option_2=='Real(refractive index)':
+            ax1.set_title('Real part of refractive index', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Real part of refractive index',color=color)
+            if epsilonTarget is not None:
+                ax1.plot(myglobalparameters.freq, np.sqrt(epsilonTarget).real, 'b-', label='target')
+            ax1.plot(myglobalparameters.freq, np.sqrt(myfitteddata.epsilon[0]).real, 'g-', label='fited')
+            ax1.legend()
+
+        elif graph_option_2=='Im(refractive index)':
+            ax1.set_title('Imaginary part of refractive index', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Imaginary part of refractive index',color=color)
+            if epsilonTarget is not None:
+                ax1.plot(myglobalparameters.freq, np.sqrt(epsilonTarget).imag, 'r-', label='target')
+            ax1.plot(myglobalparameters.freq, -np.sqrt(myfitteddata.epsilon[0]).imag, 'g-', label='fited')
+            ax1.legend()
+
+        elif graph_option_2=='E_field':
+            ax1.set_title('E_field', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('E_field',color=color)
+            ax1.plot(myglobalparameters.freq,(abs(myreferencedata.Spulseinit)), 'g-', label='reference spectre (log)')
+            ax1.plot(myglobalparameters.freq,(abs(myinputdata.Spulse)), 'b-', label='spectre after sample (log)')
+            ax1.plot(myglobalparameters.freq,(abs(myfitteddata.Spulse)), 'r-', label='fited spectre (log)')
+            ax1.legend()
+
+        elif graph_option_2=='E_field [dB]':
+            ax1.set_title('E_field [dB]', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('E_field [dB]',color=color)
+            ax1.plot(myglobalparameters.freq,20*np.log(abs(myreferencedata.Spulseinit))/np.log(10), 'g-', label='reference spectre (log)')
+            ax1.plot(myglobalparameters.freq,20*np.log(abs(myinputdata.Spulse))/np.log(10), 'b-', label='spectre after (log)')
+            ax1.plot(myglobalparameters.freq,20*np.log(abs(myfitteddata.Spulse))/np.log(10), 'r-', label='fited spectre (log)')
+
+            ax1.legend()
+
+        elif graph_option_2=='Pulse (E_field)':
+            ax1.set_title('Pulse (E_field)', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Time [s]')
+            ax1.set_ylabel('Pulse (E_field)',color=color)
+            ax1.plot(myglobalparameters.t, myreferencedata.Pulseinit, 'g-', label='reference pulse')
+            ax1.plot(myglobalparameters.t, myinputdata.pulse, 'b-', label='pulse after sample')
+            ax1.plot(myglobalparameters.t, myfitteddata.pulse, 'r-', label='fited pulse')
+            if weight is not None:
+                ax1.plot(myglobalparameters.t, weight, 'c-', label='normalised weight')
+                ax1.plot(myglobalparameters.t, -weight, 'c-')
+            elif noise is not None:
+                ax1.plot(myglobalparameters.t, noise, 'c-', label='normalised noise')
+                ax1.plot(myglobalparameters.t, -noise, 'c-')
+            ax1.legend()
+
+        elif graph_option_2 == 'Pulse (E_field) [dB]':
+            ax1.set_title('Pulse (E_field)', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Time [s]')
+            ax1.set_ylabel('Pulse (E_field)',color=color)
+            ax1.plot(myglobalparameters.t, 10*np.log(myreferencedata.Pulseinit**2)/np.log(10), 'g-', label='reference pulse')
+            ax1.plot(myglobalparameters.t, 10*np.log(myinputdata.pulse**2)/np.log(10), 'b-', label='pulse after sample')
+            ax1.plot(myglobalparameters.t, 10*np.log(myfitteddata.pulse**2)/np.log(10), 'r-', label='pulse fited')
+            ax1.legend()
+
+        elif graph_option_2=='freq_filter':
+            ax1.set_title('Frequency filter', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('Frequency filter',color=color)
+            ax1.plot(myglobalparameters.freq, freqWindow, 'g-', label='filter')
+            ax1.legend()
+        
+        elif graph_option_2=='temp_filter':
+            ax1.set_title('Temporal filter', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Time [s]')
+            ax1.set_ylabel('Temporal filter',color=color)
+            ax1.plot(myglobalparameters.t, timeWindow, 'g-', label='filter')
+            ax1.legend()
+
+        elif graph_option_2=='E_field residue':
+            ax1.set_title('E_field residue', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('E_field residue',color=color)
+            if errorweight_index==3:
+                ax1.plot(myglobalparameters.freq,(abs(np.fft.rfft(np.dot(noisematrix,(myinputdata.pulse-myfitteddata.pulse))))), 'r-', label='[spectre after sample] - [fited spectre] normalized by noisematrix')
+            else:
+                ax1.plot(myglobalparameters.freq,(abs(myinputdata.Spulse-myfitteddata.Spulse)), 'r-', label='[spectre after sample] - [fited spectre]')
+            ax1.legend()
+
+        elif graph_option_2=='E_field residue [dB]':
+            ax1.set_title('E_field residue [dB]', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Frequency [Hz]')
+            ax1.set_ylabel('E_field residue [dB]',color=color)
+            if errorweight_index==3:
+                ax1.plot(myglobalparameters.freq,(20*np.log(abs(np.fft.rfft(np.dot(noisematrix,(myinputdata.pulse-myfitteddata.pulse))))))/np.log(10), 'r-', label='[spectre after sample] - [fited spectre] normalized by noisematrix (log)')
+            else:
+                ax1.plot(myglobalparameters.freq,20*np.log(abs(myinputdata.Spulse-myfitteddata.Spulse))/np.log(10), 'r-', label='[spectre after sample] - [fited spectre (log)]')
+
+            ax1.legend()
+
+        elif graph_option_2=='Pulse (E_field) residue':
+            ax1.set_title('Pulse (E_field) residue', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Time [s]')
+            ax1.set_ylabel('Pulse (E_field) residue',color=color)
+            if errorweight_index == 3:
+                ax1.plot(myglobalparameters.t, np.dot(noisematrix,(myinputdata.pulse-myfitteddata.pulse)), 'b-', label='[pulse after sample] - [fited pulse] normalized by noisematrix')
+            else:
+                ax1.plot(myglobalparameters.t, myinputdata.pulse-myfitteddata.pulse, 'b-', label='[pulse after sample] - [fited pulse]')
+            ax1.legend()
+
+        else:
+            ax1.set_title('Pulse (E_field) residue [dB]', fontsize=10)
+            color = 'tab:red'
+            ax1.set_xlabel('Time [s]')
+            ax1.set_ylabel('Pulse (E_field) residue [dB]',color=color)
+            if errorweight_index==3:
+                ax1.plot(myglobalparameters.t, 20*np.log(np.dot(noisematrix,(myinputdata.pulse-myfitteddata.pulse)))/np.log(10), 'b-', label='[pulse after sample] - [fited pulse] normalized by noisematrix (log)')
+            else:
+                ax1.plot(myglobalparameters.t, 20*np.log(myinputdata.pulse-myfitteddata.pulse)/np.log(10), 'b-', label='[pulse after sample] - [fited pulse] (log)')
+            ax1.legend()
+
+        self.figure.tight_layout()
+        self.canvas.draw()
+
+    def real_index_graph(self):
+        global graph_option_2
+        graph_option_2='Real(refractive index)'
+        self.controler.ploting_text3('Ploting real part of refractive index')
+
+    def im_index_graph(self):
+        global graph_option_2
+        graph_option_2='Im(refractive index)'
+        self.controler.ploting_text3('Ploting imaginary part of refractive index')
+
+    def E_field_graph(self):
+        global graph_option_2
+        graph_option_2='E_field'
+        self.controler.ploting_text3('Ploting E_field')
+
+    def E_field_dB_graph(self):
+        global graph_option_2
+        graph_option_2='E_field [dB]'
+        self.controler.ploting_text3('Ploting E_field [dB]')
+
+    def Pulse_E_field_graph(self):
+        global graph_option_2
+        graph_option_2='Pulse (E_field)'
+        self.controler.ploting_text3('Ploting pulse E_field')
+
+    def Pulse_E_field_dB_graph(self):
+        global graph_option_2
+        graph_option_2='Pulse (E_field) [dB]'
+        self.controler.ploting_text3('Ploting pulse E_field [dB]')
+        
+    def freq_filter_graph(self):
+        global graph_option_2
+        graph_option_2='freq_filter'
+        self.controler.ploting_text3('Ploting frequency filter')
+
+    def temp_filter_graph(self):
+        global graph_option_2
+        graph_option_2='temp_filter'
+        self.controler.ploting_text3('Ploting temporal filter')
+
+    def E_field_residue_graph(self):
+        global graph_option_2
+        graph_option_2='E_field residue'
+        self.controler.ploting_text3('Ploting E_field residue')
+
+    def E_field_residue_dB_graph(self):
+        global graph_option_2
+        graph_option_2='E_field residue [dB]'
+        self.controler.ploting_text3('Ploting E_field residue [dB]')
+
+    def Pulse_E_field_residue_graph(self):
+        global graph_option_2
+        graph_option_2='Pulse (E_field) residue'
+        self.controler.ploting_text3('Ploting pulse E_field residue')
+
+    def Pulse_E_field_residue_dB_graph(self):
+        global graph_option_2
+        graph_option_2='Pulse (E_field) residue [dB]'
+        self.controler.ploting_text3('Ploting pulse E_field residue [dB]')
+
+    def refresh(self):
+        try:
+            epsilonTarget=self.controler.epsilonTarget
+            myinputdata=self.controler.myinputdata
+            myreferencedata=self.controler.myreferencedata
+            if self.controler.myfitteddata != None: 
+                myfitteddata=self.controler.myfitteddata
+            else:
+                myfitteddata=self.controler.previewdata
+            myglobalparameters=self.controler.myglobalparameters
+            freqWindow = self.controler.Freqwindow
+            timeWindow = self.controler.timeWindow
+            weight = self.controler.normalisedWeight
+            noise = self.controler.normalisedNoise
+            noisematrix = self.controler.noisematrix
+            errorweight_index = self.controler.errorIndex
+            self.draw_graph_init(myinputdata,myreferencedata,myfitteddata,
+                                 epsilonTarget,myglobalparameters,freqWindow,
+                                 timeWindow, weight, noise, noisematrix, errorweight_index)
+        except:
+            print(traceback.format_exc())
+            pass
+
+
+
+###############################################################################
+###############################################################################
+###############################################################################
+###############################################################################
+###############################################################################
+
+class MainWindow(QMainWindow):
+    def __init__(self, controler):
+        super().__init__()
+        self.setWindowTitle(""Fit@TDS"")
+        self.mainwidget = MyTableWidget(self,controler)
+        self.setCentralWidget(self.mainwidget)
+
+    def closeEvent(self,event):
+        try:
+            shutil.rmtree(""temp"")
+        except:
+            print(traceback.format_exc())
+            pass
+
+def main():
+    app = QApplication([])
+    controler = Controler()
+    win = MainWindow(controler)
+    controler.init()
+    # win.show()
+    win.showMaximized()
+    app.exec()
+
+if __name__ == '__main__':
+    main()
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/optimization.py",".py","35508","783","# -*- coding: utf-8 -*-
+""""""
+Created on Sun Dec 15 12:34:04 2019
+
+@author: nayab, juliettevl
+""""""
+
+# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math
+import pickle
+from pyswarm import pso   ## Library for optimization
+import random
+import numpy as np   ## Library to simplify the linear algebra calculations
+import scipy.optimize as optimize  ## Library for optimization
+import matplotlib.pyplot as plt ## Library for plotting results
+from scipy.optimize import curve_fit ## Library for optimization
+from epsillon3 import dielcal ## Library for resolving the inverse problem in our case (see the assumptions necessary to use this library)
+import fit_TDSf as TDS
+import h5py #Library to import the noise matrix
+from collections import Counter
+from pathlib import  Path as path_
+import numdifftools as nd
+
+
+import warnings
+#warnings.filterwarnings(""ignore"") #this is just to remove the 'devided by zero' runtime worning for low frequency
+#we stricly advise to comment the above line as soon as you modify the code!
+
+
+# =============================================================================
+j = 1j
+c = 2.998e8
+
+# =============================================================================
+# External Python modules (serves for optimization algo #3)
+# =============================================================================
+## Parallelization that requieres mpi4py to be installed, if mpi4py was not installed successfully comment frome line 32 to line 40 (included)
+try:
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    myrank = comm.Get_rank()
+    size = comm.Get_size()
+except:
+    print('mpi4py is required for parallelization')
+    myrank=0
+
+
+#end
+# =============================================================================
+# Extension modules
+# =============================================================================
+
+try:
+    from pyOpt import Optimization   ## Library for optimization
+    from pyOpt import ALPSO  ## Library for optimization
+except:
+    if myrank==0:
+        print(""Error importing pyopt"")
+    
+try:
+    from pyOpt import SLSQP  ## Library for optimization
+except:
+    if myrank==0:
+        print(""Error importing pyopt SLSQP"")
+
+
+# =============================================================================
+# classes we will use
+# =============================================================================
+
+class globalparameters:
+   def __init__(self, t, freq,w):
+      self.t = t
+      self.freq = freq
+      self.w = w
+
+# =============================================================================
+
+class inputdatafromfile:
+   def __init__(self, path):
+      self.timeAndPulse = np.loadtxt(path) ## We load the data of the measured pulse
+      self.Pulseinit = self.timeAndPulse[:,1]
+      self.Spulseinit = (np.fft.rfft((self.Pulseinit)))  ## We compute the spectrum of the measured pulse
+
+# =============================================================================
+
+class mydata:
+   def __init__(self, pulse,refSpulse):
+      self.pulse= pulse
+      self.Spulse= np.fft.rfft((pulse))
+      self.mytransferfunction = np.fft.rfft((pulse))/refSpulse
+#      self.epsilon= dielcal(np.fft.rfft((pulse))/refSpulse,z,myglobalparameters) #pas possible a n couches
+#      self.Spulse= fft_gpu((pulse))
+#      self.mytransferfunction = fft_gpu((pulse))/refSpulse
+      self.mynorm= np.linalg.norm(refSpulse)
+
+# =============================================================================
+
+class myfitdata:
+    def __init__(self, layers, delay_guess = 0, leftover_guess = np.zeros(2)):
+        self.mytransferfunction = layers.transferfunction(myglobalparameters.w,delay_guess,leftover_guess)
+        self.pulse = self.calculedpulse(layers,delay_guess,leftover_guess)
+        self.Spulse = (np.fft.rfft((self.calculedpulse(layers,delay_guess,leftover_guess))))
+        self.epsilon = []
+        optim_materials = []
+        for layer in layers.layers:
+            if layer.material.fit_material: #changer pour plrs fois meme mat
+                optim_materials.append(layer.material)
+        for mat in optim_materials:
+            self.epsilon.append(mat.epsilon(myglobalparameters.w))
+            
+        # =============================================================================
+    # function that returns the convolved pulse to the transfer function, it does it by different Drude model with one oscillator, n oscillators, etc
+    # =============================================================================
+    def calculedpulse(self,layers,delay_guess,leftover_guess):
+        global myinputdata,myreferencedata, myglobalparameters
+        Z = layers.transferfunction(myglobalparameters.w,delay_guess=delay_guess,leftover_guess=leftover_guess)
+        Spectrumtot=Z*myreferencedata.Spulseinit
+        Pdata=(np.fft.irfft((np.array(Spectrumtot)), n = len(myreferencedata.Pulseinit)))
+        return Pdata
+
+# =============================================================================
+class Callback_bfgs(object):
+    def __init__(self):
+        self.nit = 0
+        
+    def __call__(self, par, convergence=0):
+        self.nit += 1
+        with open('algo_bfgs_out.txt', 'a+') as filehandle:
+            filehandle.write('\n iteration number %d ; error %s ; parameters %s \r\n' % (self.nit, monerreur(par), par))
+            
+class Callback_slsqp(object):
+    def __init__(self):
+        self.nit = 0
+        
+    def __call__(self, par, convergence=0):
+        self.nit += 1
+        with open('algo_slsqp_out.txt', 'a+') as filehandle:
+            filehandle.write('\n iteration number %d ; error %s ; parameters %s \r\n' % (self.nit, monerreur(par), par))
+
+
+class Callback_annealing(object):
+    def __init__(self):
+        self.nit = 0
+        
+    def __call__(self, par, f, context):
+        self.nit += 1
+        with open('algo_dualannealing_out.txt', 'a+') as filehandle:
+            filehandle.write('\n iteration number %d ; error %s ; parameters %s \r\n' % (self.nit, monerreur(par), par))
+
+           
+
+
+
+# =============================================================================
+def errorchoice(i):
+    global myinputdata, input_reduced, normalisedWeight, normalisedNoise, nsample, spulsenorm, pulsenorm, mode, normalisednoisemat
+    if mode == ""basic"":
+        if i == 0: # constant weight. Maybe we we should only keep this one in the normal resolution.
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                fit_pulse = np.fft.irfft(Z*myreferencedata.Spulseinit)
+                erreur=np.linalg.norm((fit_pulse-myinputdata.pulse))/pulsenorm
+                return erreur
+        elif i == 1: # custom weighting
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                fit_pulse = np.fft.irfft(Z*myreferencedata.Spulseinit, n = len(myreferencedata.Pulseinit))
+                erreur=np.linalg.norm((fit_pulse-myinputdata.pulse)*normalisedWeight)/pulsenorm#spulsenorm#/myinputdata.mynorm
+                return erreur
+        elif i == 3:
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                fit_pulse = np.fft.irfft(Z*myreferencedata.Spulseinit, n = len(myreferencedata.Pulseinit))
+                Rtls = np.dot(normalisednoisemat,(myinputdata.pulse-fit_pulse))
+                #Rtls = np.diag(normalisednoisemat)*(myinputdata.pulse-fit_pulse)
+                #Stls = np.dot(np.transpose(Rtls),Rtls)
+                erreur = np.sqrt(np.dot(np.transpose(Rtls),Rtls))/pulsenorm #virer pulsenorm ?
+                return erreur
+        else:
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                fit_pulse = np.fft.irfft(Z*myreferencedata.Spulseinit, n = len(myreferencedata.Pulseinit))
+                erreur=np.linalg.norm((fit_pulse-myinputdata.pulse)/normalisedNoise)/pulsenorm#spulsenorm#/myinputdata.mynorm
+                return erreur
+    elif mode == ""superresolution"":
+        if i == 0: # constant weight:
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                Spectrumtot=Z*myreferencedata.Spulseinit
+                pulse_theo=(np.fft.irfft((np.array(Spectrumtot)), n = len(myreferencedata.Pulseinit))) # calcul from calculedpulse. In fact it is the same calcul as in the basic mode for i!=0
+                pulse_theo_reduced = pulse_theo[:nsample]
+                
+                erreur=np.linalg.norm(input_reduced-pulse_theo_reduced)/pulsenorm
+                return erreur
+        elif i == 1: # custom weighting
+            def monerreur(x):
+                Z = fit_transfer_function(x)
+                Spectrumtot=Z*myreferencedata.Spulseinit
+                pulse_theo=np.fft.irfft((np.array(Spectrumtot)),n = len(myreferencedata.Pulseinit))
+
+                pulse_theo_reduced = pulse_theo[:nsample]
+		
+                erreur=np.linalg.norm((input_reduced-pulse_theo_reduced)*normalisedWeight)/pulsenorm
+                return erreur
+                return erreur
+    return monerreur
+
+#def guesstest():
+#    global myinputdata, normalisedWeight, normalisedNoise, nsample, spulsenorm, pulsenorm, mode, normalisednoisemat
+#    minval = np.array(list(minDict.values()))
+#    maxval = np.array(list(maxDict.values()))
+#    xguess = np.array(list(myVariablesDictionary.values()))
+#    print('xguess')
+#    print(xguess)
+#    x = (xguess-minval)/(maxval-minval)
+#    monerreur = errorchoice(error_index)
+#    f = monerreur(x)
+#    return f
+
+# =============================================================================
+
+def fit_transfer_function(x):
+    global mylayerlist, myinterfacelist, myglobalparameters, weightferf, position_optim_thickness,nb_param, position_optim_material, fitDelay, delaymax_guess, delay_limit, delayfixed, fitLeftover, leftcoef_guess, leftcoef_limit, leftfixed
+    x1 = x*(maxval-minval)+minval
+    delay_guess = 0
+    leftover_guess = np.zeros(2)
+    if fitLeftover:
+        count=-1
+        for i in range (0,len(leftcoef_guess)):                    
+            count = count+1
+            leftover_guess[i] = x1[-len(leftcoef_guess)+count]
+    if fitDelay:
+        if fitLeftover:
+            delay_guess = x1[-len(leftcoef_guess)-1]
+        else:
+            delay_guess = x1[-1]
+    for i in position_optim_material:
+        mylayerlist[i].material.change_param(x1[0:nb_param],myvariables)
+    for i in position_optim_interface:
+        myinterfacelist[i].change_param(x1[0:nb_param],myvariables)
+    for i, pos in enumerate(position_optim_thickness):
+        mylayerlist[pos].thickness = x1[nb_param+i]
+    mylayers = TDS.Layers(mylayerlist, myinterfacelist)
+    return mylayers.transferfunction(myglobalparameters.w, delay_guess, leftover_guess)
+
+# =============================================================================
+def errorchoice_pyOpt(i):
+    def objfunc(x):  ## Function used in the Optimization function from pyOpt. For more details see http://www.pyopt.org/quickguide/quickguide.html
+        monerreur = errorchoice(i)
+        f = monerreur(x)
+        fail = 0
+        return f, 1, fail
+    return objfunc
+
+
+# =============================================================================
+
+def optimALPSO(opt_prob, swarmsize, maxiter,algo,out_opt_full_info_filename):
+    if algo == 2:
+        alpso_none = ALPSO(pll_type='SPM')
+    else:
+        alpso_none = ALPSO()
+    alpso_none.setOption('fileout',1)
+    alpso_none.setOption('filename',out_opt_full_info_filename)
+    alpso_none.setOption('SwarmSize',swarmsize)
+    alpso_none.setOption('maxInnerIter',6)
+    alpso_none.setOption('etol',1e-5)
+    alpso_none.setOption('rtol',1e-10)
+    alpso_none.setOption('atol',1e-10)
+    alpso_none.setOption('vcrazy',1e-4)
+    alpso_none.setOption('dt',1e0)
+    alpso_none.setOption('maxOuterIter',maxiter)
+    alpso_none.setOption('stopCriteria',0)#Stopping Criteria Flag (0 - maxIters, 1 - convergence)
+    alpso_none.setOption('printInnerIters',1)
+    alpso_none.setOption('printOuterIters',1)
+    alpso_none.setOption('HoodSize',int(swarmsize/100))
+    return(alpso_none(opt_prob))
+    
+def optimSLSQP(opt_prob,maxiter,swarmsize):
+    slsqp_none = SLSQP()
+    slsqp_none.setOption('IPRINT',1)
+    slsqp_none.setOption('IFILE',out_opt_full_info_filename)
+    slsqp_none.setOption('MAXIT',maxiter)
+    slsqp_none.setOption('IOUT',15) 
+    slsqp_none.setOption('ACC',1e-20)
+    return(slsqp_none(opt_prob))
+
+def optimSLSQPpar(opt_prob,maxiter,swarmsize): # arecopierdansdoublet
+          
+    slsqp_none = SLSQP() # arecopierdansdoublet
+
+    slsqp_none.setOption('IPRINT',1)
+    slsqp_none.setOption('IFILE',out_opt_full_info_filename)
+    slsqp_none.setOption('MAXIT',maxiter)
+    slsqp_none.setOption('IOUT',12) 
+    slsqp_none.setOption('ACC',1e-24)
+    return(slsqp_none(opt_prob,sens_mode='pgc')) 
+
+            
+
+# =============================================================================
+# Change that if the GPU is needed
+# =============================================================================
+def fft_gpu(y):
+#    global using_gpu
+#    if using_gpu==1:
+#        ygpu = cp.array(y)
+#        outgpu=cp.fft.rfft(ygpu)  # implied host->device
+#        out=outgpu.get()
+#    else:
+    out = np.fft.rfft(y)
+    return(out)
+
+def ifft_gpu(y):
+#    global using_gpu
+#    if using_gpu==1:  ## Only works if the number of elements before doing the fft is pair
+#        ygpu = cp.array(y)
+#        outgpu=cp.fft.irfft(ygpu)  # implied host->device
+#        out=outgpu.get()                            
+#    else:
+    out = np.fft.irfft(y)
+    return(out)
+
+# =============================================================================
+# We load the model choices
+# =============================================================================
+f=open(os.path.join(""temp"",'temp_file_1_ini.bin'),'rb')
+[pathwithoutsample,pathwithsample, freqWindow, timeWindow, fitDelay, delaymax_guess, delay_limit, delayfixed, mode, fitLeftover, leftcoef_guess, leftcoef_limit, leftfixed]=pickle.load(f)
+#[myinputdata, myreferencedata, myglobalparameters, nsample, delaymax, mode] = pickle.load(f)
+f.close()
+
+f=open(os.path.join(""temp"",'temp_file_1.bin'),'rb')
+[myvariables, epsilonTarget]=pickle.load(f)
+f.close()
+
+f=open(os.path.join(""temp"",'temp_file_4.bin'),'rb')
+[out_dir,time_domain_filename,frequency_domain_filename,out_opt_filename]=pickle.load(f)
+f.close()
+
+f=open(os.path.join(""temp"",'temp_file_5.bin'),'rb')
+[algo,swarmsize,maxiter,error_index,error_file]=pickle.load(f)
+f.close()
+
+#using_gpu = 0
+
+
+
+# Load fields data
+
+frequency_domain_filename = os.path.join(out_dir,frequency_domain_filename)
+time_domain_filename = os.path.join(out_dir,time_domain_filename)
+out_opt_full_info_filename=os.path.join(out_dir,'{0}_full_info.out'.format(out_opt_filename.split('.')[0]))
+out_opt_filename = os.path.join(out_dir,out_opt_filename)
+out_rtls_filename = os.path.join(out_dir,out_opt_filename+'_rtls')
+    
+datawithsample=np.loadtxt(pathwithsample)    ## We load the signal of the measured pulse with sample
+
+myreferencedata=inputdatafromfile(pathwithoutsample) # champs
+    
+myglobalparameters=globalparameters   # t freq w
+myglobalparameters.t=myreferencedata.timeAndPulse[:,0]*1e-12 #this assumes input files are in ps ## We load the list with the time of the experiment
+nsample=len(myglobalparameters.t)
+dt=myglobalparameters.t.item(2)-myglobalparameters.t.item(1)   ## Sample rate
+myglobalparameters.freq = np.fft.rfftfreq(nsample, dt)        ## We create a list with the frequencies for the spectrum
+myglobalparameters.w=myglobalparameters.freq*2*np.pi
+    
+myinputdata=mydata(datawithsample[:,1],myreferencedata.Spulseinit)    ## We create a variable containing the data related to the measured pulse with sample
+
+if mode == ""superresolution"":
+    frep=99.991499600e6 # repetition frequency of the pulse laser used in the tds measurments in Hz, 99
+    nsampleZP=np.round(1/(frep*dt)) #number of time sample betwen two pulses. IT has to be noted that it could be better to have an integer number there then the rounding does not change much
+    nsamplenotreal=nsampleZP.astype(int)
+    myglobalparameters.t=np.arange(nsampleZP)*dt  # 0001 #
+    myglobalparameters.freq = np.fft.rfftfreq(nsamplenotreal, dt)
+    myglobalparameters.w = 2*np.pi*myglobalparameters.freq
+    
+    myreferencedata.Pulseinit=np.pad(myreferencedata.timeAndPulse[:,1],(0,nsamplenotreal-nsample),'constant',constant_values=(0))
+    myreferencedata.Spulseinit=(fft_gpu((myreferencedata.Pulseinit)))    # fft computed with GPU
+    
+    myinputdata=mydata(np.pad(datawithsample[:,1],(0,nsamplenotreal-nsample),'constant',constant_values=(0)),myreferencedata.Spulseinit)
+#    monepsilon=dielcal(fft_gpu((np.pad(datawithsample[:,1],(0,nsamplenotreal-nsample),'constant',constant_values=(0))))/myreferencedata.Spulseinit,z,myglobalparameters)
+#print(myglobalparameters.w)
+#print(len(myglobalparameters.w))
+# Filter data
+myreferencedata.Spulseinit = myreferencedata.Spulseinit*freqWindow
+myinputdata.Spulse         = myinputdata.Spulse        *freqWindow
+myreferencedata.Pulseinit  = np.fft.irfft(myreferencedata.Spulseinit, n = len(myreferencedata.Pulseinit))
+myinputdata.pulse          = np.fft.irfft(myinputdata.Spulse, n = len(myinputdata.pulse))
+
+myreferencedata.Pulseinit = myreferencedata.Pulseinit*timeWindow
+myreferencedata.Spulseinit = (np.fft.rfft((myreferencedata.Pulseinit)))
+
+myinputdata=mydata(myinputdata.pulse,myreferencedata.Spulseinit)
+#for superresolution
+input_reduced = myinputdata.pulse[:nsample] #input_reduced norm is equal to pulsenorm
+
+# error weight
+#print(error_file)
+pulsenorm = np.linalg.norm(myinputdata.pulse)
+if error_file is not None:
+    if error_index == 1:
+        weight = np.loadtxt(error_file)
+        try:
+            if len(weight[0]) == 2: #in case there is time
+                weight = weight[:,1]
+        except:
+            pass
+        spulsenorm = np.linalg.norm(myinputdata.Spulse)
+        pulsenorm = np.linalg.norm(myinputdata.pulse)
+        weightnorm = np.linalg.norm(weight)/np.linalg.norm(np.ones(nsample))
+        normalisedWeight = weight/weightnorm
+    elif error_index == 2:
+        noise = np.loadtxt(error_file)
+        try:
+            if len(noise[0]) == 2: #in case there is time
+                noise = noise[:,1]
+        except:
+            pass
+        spulsenorm = np.linalg.norm(myinputdata.Spulse)
+        pulsenorm = np.linalg.norm(myinputdata.pulse)
+        noisenorm = np.linalg.norm(noise)/np.linalg.norm(np.ones(nsample))
+        normalisedNoise = noise/noisenorm
+    elif error_index == 3:
+        noisedata = h5py.File(error_file, 'r')
+        name = list(noisedata.keys())[0]
+        noisematrix = np.array(list(noisedata[name]))
+        #noisematrix = np.diagflat(np.ones(np.size(myinputdata.pulse)))
+        #noisematnorm = np.linalg.norm(noisematrix)/np.linalg.norm(np.ones(nsample))
+        noisematnorm = np.linalg.norm(noisematrix)
+        #print(noisematnorm)
+        normalisednoisemat = noisematrix/noisematnorm
+        pulsenorm = np.linalg.norm(myinputdata.pulse)
+#        try:
+#            if len(noise[0]) == 2: #in case there is time
+#                noise = noise[:,1]
+#        except:
+#            pass
+#        spulsenorm = np.linalg.norm(myinputdata.Spulse)
+#        pulsenorm = np.linalg.norm(myinputdata.pulse)
+#        noisenorm = np.linalg.norm(noise)/np.linalg.norm(np.ones(nsample))
+#        normalisedNoise = noise/noisenorm
+monerreur = errorchoice(error_index)
+objfunc = errorchoice_pyOpt(error_index)
+
+# =============================================================================
+
+# We load the parameters values
+f=open(os.path.join(""temp"",'temp_file_2.bin'),'rb')
+[position_optim_thickness, position_optim_material, position_optim_interface, mylayers, mylayerlist, myinterfacelist, mesparam]=pickle.load(f)
+f.close()
+
+
+# =============================================================================
+# change to optimize several materials/metamaterials. make list of myvariables/mesparam
+
+nb_param = len(myvariables)
+
+myVariablesDictionary = dict(zip(myvariables,mesparam[:,0]))
+minDict = dict(zip(myvariables,mesparam[:,1]))
+maxDict = dict(zip(myvariables,mesparam[:,2]))
+
+myglobalparameters
+myinputdata
+myreferencedata
+mylayers
+totVariablesName = myvariables
+
+# =============================================================================
+
+for pos in position_optim_thickness:
+    layer = mylayerlist[pos]
+    z = layer.thickness
+    deltaz = layer.uncertainty
+    myVariablesDictionary['thickness_{}'.format(pos)]=z
+    minDict['thickness_{}'.format(pos)] = z*(1-deltaz)
+    maxDict['thickness_{}'.format(pos)] = z*(1+deltaz)
+    totVariablesName = np.append(myvariables,'thickness_{}'.format(pos))
+if fitDelay:
+    if not delayfixed:
+        myVariablesDictionary['delay']=delaymax_guess
+        minDict['delay'] = -delay_limit
+        maxDict['delay'] =  delay_limit
+        totVariablesName = np.append(totVariablesName,'delay')
+    else:
+        myVariablesDictionary['delay']=delaymax_guess
+        minDict['delay'] = delaymax_guess
+        maxDict['delay'] = delaymax_guess+delaymax_guess/1e6 if delaymax_guess[i]!=0 else 1e-50
+        totVariablesName = np.append(totVariablesName,'delay')
+if fitLeftover:
+    tab=[]
+    for i in range (0,len(leftcoef_guess)):                    
+        if not leftfixed[i]:
+            myVariablesDictionary['leftover '+str(i)]=leftcoef_guess[i]#leftcoef[count-1]
+            minDict['leftover '+str(i)] = -leftcoef_limit[i]
+            maxDict['leftover '+str(i)] = leftcoef_limit[i]
+            tab = np.append(tab,'leftover '+str(i))
+        else:
+            myVariablesDictionary['leftover '+str(i)]=leftcoef_guess[i]#leftcoef[count-1]
+            minDict['leftover '+str(i)] = leftcoef_guess[i]
+            maxDict['leftover '+str(i)] = leftcoef_guess[i]+leftcoef_guess[i]/1e6 if leftcoef_guess[i]!=0 else 1e-50
+            tab = np.append(tab,'leftover '+str(i))
+    totVariablesName = np.append(totVariablesName,tab)
+## We take into account the thicknesses and delay as optimization parameters
+# so we put the values and their uncertainty in the corresponding lists
+
+
+""""""parameters for the optimization algorithm""""""
+
+
+#=============================================================================#
+# Instantiate Optimization Problem
+#=============================================================================#
+
+# Normalisation
+minval = np.array(list(minDict.values()))
+maxval = np.array(list(maxDict.values()))
+guess = np.array(list(myVariablesDictionary.values()))
+print('guess')
+print(guess)
+
+#if guess>=0:
+x0=np.array((guess-minval)/(maxval-minval))
+#else:
+#    x0=-(guess-minval)/(maxval-minval)
+print('x0')
+print(x0)
+print('errorguess')
+print(monerreur(x0))
+lb=np.zeros(len(guess))
+up=np.ones(len(guess))
+
+
+## Optimization dans le cas PyOpt
+if algo in [1,2,3,4]:
+    opt_prob = Optimization('Dielectric modeling based on TDS pulse fitting',objfunc)
+    for nom,varvalue in myVariablesDictionary.items():
+        #if varvalue>=0:
+        opt_prob.addVar(nom,'c',lower = 0,upper = 1,
+                    value = (varvalue-minDict.get(nom))/(maxDict.get(nom)-minDict.get(nom)) #normalisation
+                    )
+        #else:
+        #    opt_prob.addVar(nom,'c',lower = 0,upper = 1,
+        #                value = -(varvalue-minDict.get(nom))/(maxDict.get(nom)-minDict.get(nom)) #normalisation
+         #               )    
+    opt_prob.addObj('f')
+    #opt_prob.addCon('g1','i') #possibility to add constraints
+    #opt_prob.addCon('g2','i')
+
+
+# =============================================================================
+# solving the problem with the function in scipy.optimize
+# =============================================================================
+
+
+if  algo==0: ## xopt is a list we the drudeinput's parameters that minimize 'monerreur', fopt is a list with the optimals objective values
+    start = time.process_time()
+    xopt,fopt=pso(monerreur,lb,up,swarmsize=swarmsize,minfunc=1e-18,minstep=1e-8,debug=1,phip=0.5,phig=0.5,maxiter=maxiter) ## 'monerreur' function that we want to minimize, 'lb' and 'up' bounds of the problem
+    elapsed_time = time.process_time()-start
+    print(""Time taken by the optimization:"",elapsed_time)
+    
+if algo == 5:
+    start = time.process_time()
+    cback=Callback_bfgs()
+    res = optimize.minimize(monerreur,x0,method='L-BFGS-B',bounds=list(zip(lb, up)),callback=cback,options={'maxiter':maxiter})
+    elapsed_time = time.process_time()-start
+    hess = nd.hessian(monerreur,step=1e-6)(res.x)
+    xopt = res.x
+    fopt = res.fun
+    print(res.message,""\nTime taken by the optimization:"",elapsed_time)
+    print(f""hess_bfgs : {res['hess_inv']}"")
+    print(f""hess_nd : {hess}"")
+    
+if algo == 6:
+    start = time.process_time()
+    cback=Callback_slsqp()
+    print('error x0 algo')
+    print(monerreur(x0))
+    res = optimize.minimize(monerreur,x0,method='SLSQP',bounds=list(zip(lb, up)),callback=cback,options={'maxiter':maxiter, 'ftol': 1e-20})
+    elapsed_time = time.process_time()-start
+    xopt = res.x
+    fopt = res.fun
+    print(res.message,""\nTime taken by the optimization:"",elapsed_time)
+    
+if algo==7:
+    start = time.process_time()
+    cback=Callback_annealing()
+    res = optimize.dual_annealing(monerreur, bounds=list(zip(lb, up)),callback=cback,maxiter=maxiter)
+    elapsed_time = time.process_time()-start
+    xopt = res.x
+    fopt = res.fun
+    print(res.message,""\nTime taken by the optimization:"",elapsed_time)
+
+
+
+# =============================================================================
+# solving the problem with pyOpt
+# =============================================================================
+
+
+if  (algo==1)|(algo == 2):
+    start = time.process_time()
+    [fopt, xopt, inform] = optimALPSO(opt_prob, swarmsize, maxiter,algo,out_opt_full_info_filename)
+    elapsed_time = time.process_time()-start
+    print(inform,""\nTime taken by the optimization:"",elapsed_time)
+    
+if algo ==3:
+        try:
+            start = time.process_time()
+            [fopt, xopt, inform] = optimSLSQP(opt_prob,maxiter,swarmsize)
+            elapsed_time = time.process_time()-start
+            print(inform,""\nTime taken by the optimization:"",elapsed_time)
+        except Exception as e:
+            print(e)
+
+if algo ==4:
+        try:
+            start = time.process_time()
+            [fopt, xopt, inform] = optimSLSQPpar(opt_prob,maxiter,swarmsize)
+            elapsed_time = time.process_time()-start
+            print(inform,""\nTime taken by the optimization:"",elapsed_time)
+        except Exception as e:
+            print(e)
+            
+
+# =============================================================================
+    
+if myrank == 0:
+    xopt = xopt*(maxval-minval)+minval
+    text_result=[]
+    text_result.append('The best error was: \t{}\n'.format(fopt))
+    text_result.append('the best parameters were: \t{}\n'.format(xopt))
+    
+    # =========================================================================
+    delay_guess = 0
+    leftover_guess = np.zeros(2)
+    if fitLeftover:
+        count=-1
+        for i in range (0,len(leftcoef_guess)):                    
+            count=count+1
+            leftover_guess[i] = xopt[-len(leftcoef_guess)+count]
+    if fitDelay:
+        if fitLeftover:
+            delay_guess = xopt[-len(leftcoef_guess)-1]
+        else:
+            delay_guess = xopt[-1]
+    for i in position_optim_material:
+        mylayerlist[i].material.change_param(xopt,myvariables)
+    for i in position_optim_interface:
+        myinterfacelist[i].change_param(xopt,myvariables)
+    for i, pos in enumerate(position_optim_thickness):
+        mylayerlist[pos].thickness = xopt[nb_param+i]
+    mylayers = TDS.Layers(mylayerlist, myinterfacelist)
+    myfitteddata=myfitdata(mylayers,delay_guess=delay_guess,leftover_guess=leftover_guess)
+
+
+    # =========================================================================
+    # saving the results
+    # =========================================================================
+
+
+    outputtime=np.column_stack((myglobalparameters.t,myfitteddata.pulse))
+
+    text_result.append(""\n Please cite this paper in any communication about any use of fit@tds :"")
+    text_result.append(""\n THz-TDS time-trace analysis for the extraction of material and metamaterial parameters"")
+    text_result.append(""\n Romain Peretti, Sergey Mitryukovskiy, Kevin Froberger, Aniss Mebarki, Sophie Eliet, Mathias Vanwolleghem, Jean-Francois Lampin, Melanie Lavancier and Nabil Vindas"")
+    text_result.append(""\n IEEE Transactions on Terahertz Science and Technology, Volume 9, Issue 2"")
+    text_result.append(""\n DOI: 10.1109/TTHZ.2018.2889227 \n"")
+
+    result_optimization=[xopt,text_result]
+    f=open(os.path.join(""temp"",'temp_file_3.bin'),'wb')
+    pickle.dump(result_optimization,f,pickle.HIGHEST_PROTOCOL)
+    f.close()
+
+    ## Save the data obtained via this program
+    # Save optimization parameters
+    Rtls_opt=0
+    if error_index == 3:
+        Rtls_opt = np.dot(noisematrix,(myinputdata.pulse-myfitteddata.pulse))
+        Stls = np.dot(np.transpose(Rtls_opt),Rtls_opt)
+        Qaic = Stls + 2*np.size(xopt)
+        print('Akaike criterion')
+        print(Qaic)
+    outputoptim = fopt
+    if error_index == 3:
+        outputoptim = np.append(outputoptim,Qaic)
+        np.savetxt(out_rtls_filename,Rtls_opt)
+    outputoptim = np.append(outputoptim,xopt)
+    
+    out_opt_h5 = h5py.File(out_opt_filename+'.h5', 'w')
+    dset = out_opt_h5.create_dataset(""output"", (len(outputoptim),),dtype='float64')
+    dset[:]=outputoptim
+
+    out_opt_h5.close()
+    
+    np.savetxt(out_opt_filename,outputoptim) #np.linalg.norm(myinputdata.pulse)
+    
+    # Save time domain results
+    np.savetxt(time_domain_filename,outputtime,header=""Please cite this paper in any communication about any use of fit@tds : \n THz-TDS time-trace analysis for the extraction of material and metamaterial parameters \n Romain Peretti, Sergey Mitryukovskiy, Kevin Froberger, Aniss Mebarki, Sophie Eliet, Mathias Vanwolleghem Jean-Francois Lampin, Melanie Lavancier and Nabil Vindas \n IEEE Transactions on Terahertz Science and Technology, Volume 9, Issue 2 \n DOI: 10.1109/TTHZ.2018.2889227 \n \n time \t E-field"")
+    
+    #print(np.real(epsilonTarget), np.imag(epsilonTarget), np.real(np.sqrt(epsilonTarget)),np.imag(np.sqrt(epsilonTarget)))
+#    if (epsilonTarget is not None)&(len(mylayerlist) == 1):
+    try:
+        outputfreq=abs(np.column_stack((myglobalparameters.freq,myfitteddata.Spulse,np.real(myfitteddata.epsilon[0]),np.imag(myfitteddata.epsilon[0]),
+                                        np.real(np.sqrt(myfitteddata.epsilon[0])),np.imag(np.sqrt(myfitteddata.epsilon[0])),np.real(epsilonTarget) ,
+                                        np.imag(epsilonTarget), np.real(np.sqrt(epsilonTarget)),np.imag(np.sqrt(epsilonTarget)) )))
+#    else:
+    except:
+        outputfreq=abs(np.column_stack((myglobalparameters.freq,myfitteddata.Spulse,np.real(myfitteddata.epsilon[0]),np.imag(myfitteddata.epsilon[0]),
+                                        np.real(np.sqrt(myfitteddata.epsilon[0])),np.imag(np.sqrt(myfitteddata.epsilon[0])) )))
+
+    # Save frequency domain results
+    np.savetxt(frequency_domain_filename,outputfreq,header=""Please cite this paper in any communication about any use of fit@tds : \n THz-TDS time-trace analysis for the extraction of material and metamaterial parameters \n Romain Peretti, Sergey Mitryukovskiy, Kevin Froberger, Aniss Mebarki, Sophie Eliet, Mathias Vanwolleghem, Jean-Francois Lampin, Melanie Lavancier and Nabil Vindas \n IEEE Transactions on Terahertz Science and Technology, Volume 9, Issue 2 \n DOI: 10.1109/TTHZ.2018.2889227 \n \n Freq \t E-field \t real part of fitted epsilon \t imaginary part of fitted epsilon \t real part of fitted n \t imaginary part of fitted n \t real part of initial epsilon \t imaginary part of initial epsilon \t real part of initial n\t imaginary part of initial n"")
+
+
+    # =========================================================================
+    # History and convergence
+    # =========================================================================
+    out_opt_full_info_filename_dir = path_(out_opt_full_info_filename).parent
+    out_opt_full_info_filename = path_(out_opt_full_info_filename).stem
+    out_opt_full_info_filename = path_(out_opt_full_info_filename_dir).joinpath(out_opt_full_info_filename)
+    
+    if  (algo==1)|(algo == 2):
+        # f = open(""{0}_print.out"".format(out_opt_full_info_filename.split('.')[0]),'r')
+        f = open(f""{out_opt_full_info_filename}_print.out"",'r')
+    
+        # Find maxiter
+        line = f.readline()
+        while (line !=''):
+            line = f.readline()
+            lineSplit = line.split()
+            if len(lineSplit)>2:
+                if (lineSplit[0:3]==['NUMBER','OF','ITERATIONS:']):
+                    maxiter = int(lineSplit[3])
+            
+        f.close()
+        # f = open(""{0}_print.out"".format(out_opt_full_info_filename.split('.')[0]),'r') # Go back to the beginning
+        f = open(f""{out_opt_full_info_filename}_print.out"",'r')# Go back to the beginning
+        
+        # To find number of parameters:
+        j = 0
+        while (f.readline()!= 'OBJECTIVE FUNCTION VALUE:\n') & (j<50):
+            j = j+1
+        P = [(f.readline())[4:]]
+        
+        while (f.readline()!= 'BEST POSITION:\n') & (j<100):
+            j =  j+1
+        line = (f.readline())
+        line = (line.split())
+        nLines = 0
+        while(len(line)>0):#(line[0][0] == 'P'):
+            P.extend(line[2::3])
+            line = (f.readline())
+            line = (line.split())
+            nLines = nLines+1
+        bestPositions = np.zeros((maxiter,len(P)))
+        bestPositions[0]=P
+        
+        
+        for i in range(1,maxiter):
+            j = 0
+            while (f.readline()!= 'OBJECTIVE FUNCTION VALUE:\n') & (j<100):
+                j = j+1 # to avoid infinite loop
+                # One could use pass instead
+            P = [(f.readline())[4:]]
+            while (f.readline()!= 'BEST POSITION:\n') & (j<200):
+                j =  j+1
+            for nLine in range(nLines):
+                line = (f.readline())
+                line = (line.split())
+                P.extend(line[2::3])
+            bestPositions[i]=P
+        f.close()
+        
+        # Write and save file
+        historyHeaderFile = os.path.join(out_dir,""convergence.txt"")
+        historyHeader = 'objective function value'
+        for name in myVariablesDictionary:
+            historyHeader = '{}{}\t'.format(historyHeader, name)
+        np.savetxt(historyHeaderFile, bestPositions, header = historyHeader)
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/epsillon3.py",".py","22660","545","# =============================================================================
+# Aditya_code
+# =============================================================================
+import sympy as sp
+import numdifftools as nd
+import pandas as pd
+import pyarrow as pa
+import pyarrow.csv  as csv_
+from pathlib import Path as path_
+
+ROOT_DIR = path_(__file__).parent
+# filename = path_(ROOT_DIR).joinpath('errors.csv')
+filename = path_(ROOT_DIR)
+
+# ============================================================================ #
+#                        sympy error function definition                       #
+# ============================================================================ #
+n, k, w, d, C1, C2 = sp.symbols('n k w d C1 C2', real=True)
+
+# # Define complex-valued function
+c = 2.998e8
+A1 = (4*n**3+4*n*k**2+8*n**2+8*k**2+4*n)/((k**2+(1+n)**2)**2)
+B1 = (4*k*(-n**2-k**2+1))/((k**2+(1+n)**2)**2)
+D1 = w*d*k/c
+D2 = w*d*(n-1)/c
+f =  C1**2 + C2**2 - 2*(C1*A1 - C2*B1)*sp.exp(-D1)*sp.cos(D2) + 2*(C1*B1 + A1*C2)*sp.exp(-D1)*sp.sin (D2) + A1**2*sp.exp(-2*D1) + B1**2*sp.exp(-2*D1)
+Hessian = sp.hessian(f,(n,k))
+# ---------------------------------------------------------------------------- #
+
+
+# =============================================================================
+# Aditya_code
+# =============================================================================
+
+###############################################################################
+###############################################################################
+# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math, cmath
+
+# from __future__ import print_function
+from pyswarm import pso
+import random
+import numpy as np
+import scipy.optimize as optimize
+import matplotlib.pyplot as plt
+from numpy import *
+from pylab import *
+from scipy.io.matlab import mio
+
+# from scipy import optimize
+from scipy.optimize import minimize
+import csts as Csts
+
+###############################################################################
+###############################################################################
+j = 1j
+c = 2.998e8
+
+name = Csts.FileName
+###############################################################################
+
+# defining the Error function
+def errorFct(n, k, d, frequency, measAmp, measAngle, FP, eqs = ""log"",print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+    
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    # print(f""z :  {z}"")
+    # print(f""measAmp :  {measAmp}"")
+    
+    #à utiliser en premier lieu
+    if eqs == ""exp"":
+        ThZ = abs(z)*abs(exp(j*(angleTaf+angleTfa+angleTfe+angleFP)))
+        measZ = measAmp*abs(exp(j*measAngle))
+        erreur = abs((ThZ-measZ))**2
+    
+    #à utiliser si la version en exp ne marche pas (partie réelle de l'indice de réfraction inversée)
+    elif eqs == ""log"":
+        ThZ = (np.log(abs(z))+1j*(angleTaf+angleTfa+angleTfe+angleFP))
+        measZ = ((log(measAmp))+1j*measAngle)
+        erreur = abs((ThZ-measZ))**2
+    
+    # print_bool = True
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err = {erreur}"")
+
+    #version antérieure, pas bon car séparait la formule en deux
+    #errorAngle = (angleTaf + angleTfa +temp_ angleTfe + angleFP - measAngle) ** 2
+    #errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    #erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+
+# ############## try to filter wrong n and k values for hessian ############## #
+def errorFct_hess(n, k, n_ini, k_ini, d, frequency, measAmp, measAngle, FP, print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    
+    # if n < n_ini or n < 0:
+    # if  n < 0 :
+        # # n = n_ini
+        # n = abs(n)
+    # if k < 0 :
+        # # k = k_ini
+        # k = abs(k)
+    
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    
+    
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    #à utiliser en premier lieu
+    # ThZ = abs(z)*abs(exp(j*(angleTaf+angleTfa+angleTfe+angleFP)))
+    # measZ = measAmp*abs(exp(j*measAngle))
+    # erreur = abs((ThZ-measZ))**2
+    
+    # erreur = np.square(np.abs(z-measAmp))
+    
+    #à utiliser si la version en exp ne marche pas (partie réelle de l'indice de réfraction inversée)
+    ThZ = (np.log(abs(z))+1j*(angleTaf+angleTfa+angleTfe+angleFP))
+    measZ = ((log(measAmp))+1j*measAngle)
+    erreur = abs((ThZ-measZ))**2
+    
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err = {erreur}"")
+        
+        # print(f""refIndex : {refInd}"")
+        # print(f""THz={ThZ}"")
+        # print(f""measz = {measZ}"")
+
+    #version antérieure, pas bon car séparait la formule en deux
+    #errorAngle = (angleTaf + angleTfa + angleTfe + angleFP - measAngle) ** 2
+    #errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    #erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+    
+
+def errorFct_TF(n, k, d, frequency, ftrans_meas, FP, print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * np.exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    
+    erreur = np.square(np.abs(z-ftrans_meas))
+    
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err_trans = {erreur}"")
+    
+
+    return erreur
+
+##Aditya_code
+
+# def errorFct2(n, k, d, f, c1, c2):
+    # 
+    # w = 2*np.pi*f
+    # a1 = (4*n**3+4*n*k**2+8*n**2+8*k**2+4*n)/((k**2+(1+n)**2)**2)
+    # b1 = (4*k*(-n**2-k**2+1))/((k**2+(1+n)**2)**2)
+    # d1 = w*d*k/c
+    # d2 = w*d*(n-1)/c
+    # erreur2 =  c1**2 + c2**2 - 2*(c1*a1 - c2*b1)*np.exp(-d1)*np.cos(d2) + 2*(c1*b1 + a1*c2)*np.exp(-d1)*np.sin(d2) + a1**2*np.exp(-2*d1) + b1**2*np.exp(-2*d1)
+    # 
+    # print(f""n_r : {n}"")
+    # print(f""k_r : {k}"")
+    # print(f""erreur_r : {erreur2}"")
+
+    # return erreur2
+
+
+##Aditya_code
+
+# defining the 'so-called' inverse problem to get the refractive index
+def inverseProblem(freq, fctTrans, thick, FP, init, limitUp, phse):
+    """""" Calculates n and k over frequency.
+        freq : Frequency in Hz, must be POSITIVE;
+        fctTrans : Transfer function, must be same size as freq and correspond to positive frequencies;
+        thick : thickness of the sample in meters [m];
+        FP : If equal to 1 takes into account the Farby-Perot effect;
+        init : initial guess for the inverse problem. init should be an array : init = [3, 0.1], for example;
+        limitUp : High frequency limit of the study in Hz;
+    """"""
+    init = np.transpose([init.real, init.imag])
+    # nnn = np.ones(init.shape)
+    # init = np.transpose([nnn, init.imag])
+    
+    f = freq[(freq <= limitUp)]
+
+    ## The measurement
+    meas = fctTrans[(freq <= limitUp)]
+
+    ## Inverse problem
+    nInv = zeros(freq.shape[0])
+    kInv = zeros(freq.shape[0])
+    # =============================== Hessian matix ============================== #
+    Hessian_nd_c = np.empty((freq.shape[0]),dtype=matrix)
+    Gradient_nd_c = np.empty((freq.shape[0]),dtype=matrix)
+    # Hessian_nd_r = np.empty((freq.shape[0]),dtype=matrix)
+    # Hessian_sp_r = np.empty((freq.shape[0]),dtype=matrix)
+    
+    err_n  = np.zeros((freq.shape[0]))
+    err_k  = np.zeros((freq.shape[0]))
+    
+    diag_n = np.zeros((freq.shape[0]))
+    diag_k = np.zeros((freq.shape[0]))
+    
+    inv_n = np.zeros((freq.shape[0]))
+    inv_k = np.zeros((freq.shape[0]))
+    
+    hess_n = np.zeros((freq.shape[0]))
+    hess_k = np.zeros((freq.shape[0]))
+    
+    err_n_bfgs = np.zeros((freq.shape[0]))
+    err_k_bfgs = np.zeros((freq.shape[0]))
+    
+    # err2_n  = np.empty((freq.shape[0]))
+    # err2_k  = np.empty((freq.shape[0]))
+    # err3_n  = np.empty((freq.shape[0]))
+    # err3_k  = np.empty((freq.shape[0]))
+    # ---------------------------------------------------------------------------- #
+    
+    measAngleTh = unwrap(angle(meas)) 
+    # measAngleTh = unwrap(angle(meas)) + phse * np.pi
+    measAbsTh = abs(meas)
+    
+    for i in range(0, f.shape[0] - 1):
+        # pouet = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP)
+        # res = minimize(pouet, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # res = minimize(pouet, init[i], method=""SLSQP"")
+        
+        # # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        
+        # err_function_tf = lambda x: errorFct_TF(x[0], x[1], thick, f[i], fctTrans[i], FP)
+        # res = minimize(err_function_tf, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # res = minimize(err_function_tf, init[i], method=""BFGS"", tol=1e-6)
+        
+        err_function_log = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP, ""log"")
+        res = minimize(err_function_log, init[i], method=""Nelder-Mead"", tol=1e-6)
+        
+        
+        # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        # res = minimize(err_function_tf, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # if i==0:
+            # res = minimize(err_function_tf, init[i], method=""Nelder-Mead"", tol=1e-6)
+            # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        # else:
+            # res = minimize(err_function_tf, init[i], method=""Nelder-Mead"", tol=1e-6)
+            # res = minimize(err_function_log, [nInv[i-1],kInv[i-1]], method=""BFGS"", tol=1e-6)
+        
+        err_n_bfgs[i] = 0
+        err_k_bfgs[i] = 0
+        # err_n_bfgs[i] = np.sqrt(res[""hess_inv""][0][0])
+        # err_k_bfgs[i] = np.sqrt(res[""hess_inv""][1][1])
+        
+        # err_function_trans = lambda x: errorFct_TF(x[0], x[1], thick, f[i], fctTrans[i], FP)
+        # # res = minimize(err_function_trans, init[i], method=""BFGS"", tol=1e-6)
+        # res = minimize(err_function_trans, init[i], method=""BFGS"", tol=1e-6)
+        
+        print_bool_res = False
+        
+        if print_bool_res:
+            print(f""###################"") 
+            print(f""freq : {f[i]/1e12}"")
+            print(f""init : {init[i]}"")
+            print(f""res : {res}"")
+            print(f""minimizing..."")
+            print(f""Status : {res['message']}"")
+            # print(f"" err_n_bfgs : {err_n_bfgs[i]}"")
+            # print(f"" err_k_bfgs : {err_k_bfgs[i]}"")
+            print(f""Total Evaluations: {res['nfev']}"")
+            print(f""n : {res['x'][0]}"")
+            print(f""k : {res['x'][1]}"")
+            # print(f""erreur : {err_funtion_log_minimzied}"")
+            # print(f""erreur : {err_fctrans}"")
+            print(f""===================="") 
+        
+        # cf https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize
+        # for other methods
+        nInv[i] = res.x[0]
+        kInv[i] = res.x[1]
+    
+    #TOVERIFY
+    for i in range(0, f.shape[0] - 1):
+        # puick = errorFct(res.x[0],res.x[1],thick,f[i],measAbsTh[i], measAngleTh[i], FP)
+        # puick = errorFct(nInv[i],kInv[i],thick,f[i],measAbsTh[i], measAngleTh[i], FP)
+        
+        ##Aditya_code
+
+        # ========================== numdifftools in C space ========================= #
+        # G_numdiff_C = nd.Gradient(pouet)([res.x[0], res.x[1]])
+        
+        # H_Numdiff_C = nd.Hessian(pouet, method = ""central"", order =2)([res.x[0], res.x[1]])
+        # H_Numdiff_C = nd.Hessian(pouet)([res.x[0], res.x[1]])
+        
+        
+        # ############################################################################ #
+        if print_bool_res:
+            print(f""###################"")
+            print(f""replacing"")
+            print(f""freq: {f[i]/1e12}"")
+            print(f""n : {nInv[i]}"")
+            print(f""k : {kInv[i]}"")
+        
+        try:
+            step_val = 1e-6 * (1e-3 / thick)
+            # print(f""step : {step_val}"")
+            # # pouetpouet = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP,print_bool=True)
+            # pouetpouet = lambda x: errorFct_hess(x[0], x[1], nInv[i], kInv[i],  thick, f[i], measAbsTh[i], measAngleTh[i],FP,print_bool=True)
+            # H_Numdiff_C = nd.Hessian(pouetpouet, step=1e-6)([nInv[i] , kInv[i]])
+            err_fct_hess = lambda x: errorFct_TF(x[0], x[1],  thick, f[i], fctTrans[i],FP,print_bool=False)
+            H_Numdiff_C = nd.Hessian(err_fct_hess, step=step_val)([nInv[i] , kInv[i]])
+            Hessian_nd_c[i] = H_Numdiff_C
+            inv_hess = np.linalg.inv(H_Numdiff_C)
+            diag_inv_hess = np.diag(inv_hess)
+            err = np.sqrt(diag_inv_hess)
+            # print(f""hess_c = {H_Numdiff_C}"")
+            # print(f""inv_hess_c = {inv_hess}"")
+            if print_bool_res:
+                print(f""diag_inv_hess_c = {diag_inv_hess}"")
+                print(f""===================="")
+        except Exception as e:
+            print(e)
+            inv_hess = np.zeros((2,2))
+            diag_inv_hess = np.zeros(2)
+            err = np.zeros(2)
+            print(f""diag_inv_hess_c = {diag_inv_hess}"")
+            print(f""===================="")
+        # ############################################################################ #
+        
+        
+        # det = np.linalg.det(H_Numdiff_C)
+        # print(H_Numdiff_C)
+        
+        # Gradient_nd_c[i] = G_numdiff_C
+        
+        
+        # err = np.sqrt(np.diag(np.linalg.inv(H_Numdiff_C)))
+        
+        # ############################################################################ #
+        diag_n[i] = diag_inv_hess[0]
+        diag_k[i] = diag_inv_hess[1]
+    
+        err_n[i] = err[0]
+        err_k[i] = err[1]
+        
+        hess_n[i] = H_Numdiff_C[0][0] 
+        hess_k[i] = H_Numdiff_C[1][1] 
+        
+        inv_n[i] = inv_hess[0][0]
+        inv_k[i] = inv_hess[1][1]
+        # ############################################################################ #
+        
+        # inv_hess_nd_c = np.linalg.inv(H_Numdiff_C)
+        
+        # print(f""inv nd hess = {np.linalg.inv(H_Numdiff_C)}"")
+        # print(f""nd hess C =  {H_Numdiff_C}"")
+        # print(f""inv nd hess = {inv_hess_nd_c}"")
+        # ---------------------------------------------------------------------------- #
+    
+        # ========================== numdifftools in R space ========================= #
+        # pouet2 = lambda x: errorFct2(x[0],x[1],thick,f[i],np.real(measAbsTh[i]),np.imag(measAbsTh[i]))
+
+        # H_Numdiff_R = nd.Hessian(pouet2)([nInv[i], kInv[i]])
+        
+        # inv_hess_r = np.linalg.inv(H_Numdiff_R)
+        # diag_inv_hess_r = np.diag(inv_hess_r)
+        
+        
+        
+        # print(f""hess_r = {diag_inv_hess_r}"")
+        # ############################################################################ #
+        
+        
+        # inv_hess_nd_r = np.linalg.inv(H_Numdiff_R)
+
+        # err2 = np.sqrt(np.diag(np.linalg.inv(H_Numdiff_R)))
+        # err2_n[i] = err2[0]
+        # err2_k[i] = err2[1]
+        # # print(f""nd hess R =  {H_Numdiff_R}"")
+        # # print(f""inv nd hess R= {inv_hess_nd_r}"")
+        # # print(f""error nd Hess C = {err2}"")
+
+        # Hessian_nd_r[i] = H_Numdiff_R
+        # ---------------------------------------------------------------------------- #
+    
+    
+    
+        # ============================= sympy in R space ============================= #
+        # values = {n: nInv[i], k: kInv[i], w: 2*np.pi*f[i], d: thick, C1: np.real(measAbsTh[i]), C2: np.imag(measAbsTh[i])}
+        # H_sympy_R = Hessian.subs(values)
+        
+        # H_sympy_R = np.array(H_sympy_R).astype(np.float64)
+        # Hessian_sp_r[i]= H_sympy_R
+        # inv_hess_sp_r = np.linalg.inv(H_sympy_R)
+        # err3 = np.sqrt(np.diag(np.linalg.inv(H_sympy_R)))
+        # err3_n[i] = err3[0]
+        # err3_k[i] = err3[1]
+        # # print(f""sp hess R =  {H_sympy_R}"")
+        # # print(f""inv sp hess R= {inv_hess_sp_r}"")
+        # # print(f""error nd Hess C = {err3}"")
+        # ---------------------------------------------------------------------------- #
+        
+        ##Aditya_code
+
+    ref_index = j * kInv + nInv
+
+
+    print(""phase:"",phse)
+
+    err_n = np.nan_to_num(err_n)
+    err_k = np.nan_to_num(err_k)
+
+    # indexes = pd.DataFrame([nInv, kInv, err_n, err_k], columns=[""n"", ""k"", ""err_n"", ""err_k""])
+    # indexes = pd.DataFrame({""n"": nInv, ""k"": kInv, ""err_n"" : err_n, ""err_k"" : err_k})
+    # print(indexes)
+    # table = pa.Table.from_pandas(indexes)
+    
+    # ############################################################################ #
+    # table = pa.Table.from_arrays([freq/1e12,nInv,err_n, kInv,err_k],names=[""freq"",""n"",""err_n"", ""k"", ""err_k""])
+    table = pa.Table.from_arrays([freq/1e12,nInv,hess_n,inv_n,diag_n,err_n,err_n_bfgs, kInv,hess_k,inv_k,diag_k,err_k, err_k_bfgs],names=[""freq"",""n"",""hess_n"",""inv_n"",""diag_n"",""err_n"", ""err_n_bfgs"", ""k"",""hess_k"",""inv_k"",""diag_k"", ""err_k"", ""err_k_bfgs""])
+    
+    err_filename = path_(Csts.FileName).name
+    err_filename = filename.joinpath(f""errors_{err_filename}"")
+    
+    options = csv_.WriteOptions(include_header=True,delimiter='\t')
+    csv_.write_csv(table,err_filename,options)
+    # ############################################################################ #
+
+    # ref_index = j * kInv + nInv
+    # print(f""filename = {Csts.FileName}"")
+    return ref_index
+
+def dielcal(mytransferfunction, z, myglobalparameters,FP, phse,scattering=None):
+    global zz, mytransferfunction2
+
+
+    """""" 
+    Takes variables from the the input and uses the inverseProblem
+    The first part is the initialization and the last line is the optimisation
+    """"""
+    mytransferfunction2 = np.copy(mytransferfunction)
+    zz = np.copy(z)
+    ref_index = np.ones(len(myglobalparameters.w))
+    myangleTF = np.unwrap(np.angle(mytransferfunction))
+
+    for compt in range(0, 3):  # the max of this loop was choosen to optimize the effective index for a 250micron thick quartz substrate it gives good result at 1/1000 but shows weird convergeance (larger is not better so this may had to be changed with the sample)
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        myangleTrans = np.nan_to_num(np.angle(tt))  # ici il y a un truc a bien regarder
+        phase = np.unwrap(myangleTF + myangleTrans) + phse * np.pi # we are taking the phase from the transfer function
+        # phase = np.unwrap(myangleTF + myangleTrans)   # we are taking the phase from the transfer function
+        # phase_diff = np.diff(phase)
+        # phase_bool = np.where(phase_diff>180)
+        # print(f""phase diff: {phase_bool}"")
+        
+        # print(zz)
+        # print( myglobalparameters.w)
+        naprox = 1 + abs(phase * c / zz / myglobalparameters.w)  # this gives the real part of the index
+        # print(naprox)
+        
+        ref_index = naprox + j * (ref_index.imag)  # this gives the real part of the index
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        kaprox = -np.nan_to_num(c / z / myglobalparameters.w * np.log(abs(mytransferfunction) / (tt)))
+        ref_index = j * kaprox.real + ref_index.real
+    ref_index[0] = 1
+    # for i,k in enumerate(kaprox):
+        # if k.real > 1e-2:
+            # print(f""k guess is large"")
+            # #print(kaprox.real[i])
+            # #print(mytransferfunction[i])
+            # #print(myglobalparameters.w[i]/1e12)
+    # print(ref_index)
+
+    ref_index2 = inverseProblem(myglobalparameters.w / 2 / np.pi, mytransferfunction2, zz, FP, ref_index, 6e12, phse)
+
+    return np.array(ref_index2 ** 2)
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/epsillon3-28-8.py",".py","21415","531","# =============================================================================
+# Aditya_code
+# =============================================================================
+import sympy as sp
+import numdifftools as nd
+import pandas as pd
+import pyarrow as pa
+# from tqdm import *
+import pyarrow.csv  as csv_
+from pathlib import Path as path_
+
+ROOT_DIR = path_(__file__).parent
+# filename = path_(ROOT_DIR).joinpath('errors.csv')
+filename = path_(ROOT_DIR)
+
+# ============================================================================ #
+#                        sympy error function definition                       #
+# ============================================================================ #
+n, k, w, d, C1, C2 = sp.symbols('n k w d C1 C2', real=True)
+
+# # Define complex-valued function
+c = 2.998e8
+A1 = (4*n**3+4*n*k**2+8*n**2+8*k**2+4*n)/((k**2+(1+n)**2)**2)
+B1 = (4*k*(-n**2-k**2+1))/((k**2+(1+n)**2)**2)
+D1 = w*d*k/c
+D2 = w*d*(n-1)/c
+f =  C1**2 + C2**2 - 2*(C1*A1 - C2*B1)*sp.exp(-D1)*sp.cos(D2) + 2*(C1*B1 + A1*C2)*sp.exp(-D1)*sp.sin (D2) + A1**2*sp.exp(-2*D1) + B1**2*sp.exp(-2*D1)
+Hessian = sp.hessian(f,(n,k))
+# ---------------------------------------------------------------------------- #
+
+
+# =============================================================================
+# Aditya_code
+# =============================================================================
+
+###############################################################################
+###############################################################################
+# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math, cmath
+
+# from __future__ import print_function
+from pyswarm import pso
+import random
+import numpy as np
+import scipy.optimize as optimize
+import matplotlib.pyplot as plt
+from numpy import *
+from pylab import *
+from scipy.io.matlab import mio
+
+# from scipy import optimize
+from scipy.optimize import minimize
+import csts as Csts
+
+###############################################################################
+###############################################################################
+j = 1j
+c = 2.998e8
+
+name = Csts.FileName
+###############################################################################
+
+# defining the Error function
+def errorFct(n, k, d, frequency, measAmp, measAngle, FP, eqs = ""log"",print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+    
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    # print(f""z :  {z}"")
+    # print(f""measAmp :  {measAmp}"")
+    
+    #à utiliser en premier lieu
+    if eqs == ""exp"":
+        ThZ = abs(z)*abs(exp(j*(angleTaf+angleTfa+angleTfe+angleFP)))
+        measZ = measAmp*abs(exp(j*measAngle))
+        erreur = abs((ThZ-measZ))**2
+    
+    #à utiliser si la version en exp ne marche pas (partie réelle de l'indice de réfraction inversée)
+    elif eqs == ""log"":
+        ThZ = (np.log(abs(z))+1j*(angleTaf+angleTfa+angleTfe+angleFP))
+        measZ = ((log(measAmp))+1j*measAngle)
+        erreur = abs((ThZ-measZ))**2
+    
+    # print_bool = True
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err = {erreur}"")
+
+    #version antérieure, pas bon car séparait la formule en deux
+    #errorAngle = (angleTaf + angleTfa +temp_ angleTfe + angleFP - measAngle) ** 2
+    #errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    #erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+
+# ############## try to filter wrong n and k values for hessian ############## #
+def errorFct_hess(n, k, n_ini, k_ini, d, frequency, measAmp, measAngle, FP, print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    
+    # if n < n_ini or n < 0:
+    # if  n < 0 :
+        # # n = n_ini
+        # n = abs(n)
+    # if k < 0 :
+        # # k = k_ini
+        # k = abs(k)
+    
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    
+    
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    #à utiliser en premier lieu
+    # ThZ = abs(z)*abs(exp(j*(angleTaf+angleTfa+angleTfe+angleFP)))
+    # measZ = measAmp*abs(exp(j*measAngle))
+    # erreur = abs((ThZ-measZ))**2
+    
+    # erreur = np.square(np.abs(z-measAmp))
+    
+    #à utiliser si la version en exp ne marche pas (partie réelle de l'indice de réfraction inversée)
+    ThZ = (np.log(abs(z))+1j*(angleTaf+angleTfa+angleTfe+angleFP))
+    measZ = ((log(measAmp))+1j*measAngle)
+    erreur = abs((ThZ-measZ))**2
+    
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err = {erreur}"")
+        
+        # print(f""refIndex : {refInd}"")
+        # print(f""THz={ThZ}"")
+        # print(f""measz = {measZ}"")
+
+    #version antérieure, pas bon car séparait la formule en deux
+    #errorAngle = (angleTaf + angleTfa + angleTfe + angleFP - measAngle) ** 2
+    #errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    #erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+    
+
+def errorFct_TF(n, k, d, frequency, ftrans_meas, FP, print_bool = False):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        # FP_effect = 1 / (1 - ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        FP_effect = 1 / (1 - np.square((refInd - nair) / (refInd + nair)) * np.exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    
+    erreur = np.square(np.abs(z-ftrans_meas))
+    
+    if print_bool:
+        print(f""n : {n}"")
+        print(f""k : {k}"")
+        print(f""err_trans = {erreur}"")
+    
+
+    return erreur
+
+##Aditya_code
+
+# def errorFct2(n, k, d, f, c1, c2):
+    # 
+    # w = 2*np.pi*f
+    # a1 = (4*n**3+4*n*k**2+8*n**2+8*k**2+4*n)/((k**2+(1+n)**2)**2)
+    # b1 = (4*k*(-n**2-k**2+1))/((k**2+(1+n)**2)**2)
+    # d1 = w*d*k/c
+    # d2 = w*d*(n-1)/c
+    # erreur2 =  c1**2 + c2**2 - 2*(c1*a1 - c2*b1)*np.exp(-d1)*np.cos(d2) + 2*(c1*b1 + a1*c2)*np.exp(-d1)*np.sin(d2) + a1**2*np.exp(-2*d1) + b1**2*np.exp(-2*d1)
+    # 
+    # print(f""n_r : {n}"")
+    # print(f""k_r : {k}"")
+    # print(f""erreur_r : {erreur2}"")
+
+    # return erreur2
+
+
+##Aditya_code
+
+# defining the 'so-called' inverse problem to get the refractive index
+def inverseProblem(freq, fctTrans, thick, FP, init, limitUp):
+    """""" Calculates n and k over frequency.
+        freq : Frequency in Hz, must be POSITIVE;
+        fctTrans : Transfer function, must be same size as freq and correspond to positive frequencies;
+        thick : thickness of the sample in meters [m];
+        FP : If equal to 1 takes into account the Farby-Perot effect;
+        init : initial guess for the inverse problem. init should be an array : init = [3, 0.1], for example;
+        limitUp : High frequency limit of the study in Hz;
+    """"""
+    init = np.transpose([init.real, init.imag])
+    # nnn = np.ones(init.shape)
+    # init = np.transpose([nnn, init.imag])
+    
+    f = freq[(freq <= limitUp)]
+
+    ## The measurement
+    meas = fctTrans[(freq <= limitUp)]
+
+    ## Inverse problem
+    nInv = zeros(freq.shape[0])
+    kInv = zeros(freq.shape[0])
+    
+    # =============================== Hessian matix ============================== #
+    Hessian_nd_c = np.empty((freq.shape[0]),dtype=matrix)
+    Gradient_nd_c = np.empty((freq.shape[0]),dtype=matrix)
+    # Hessian_nd_r = np.empty((freq.shape[0]),dtype=matrix)
+    # Hessian_sp_r = np.empty((freq.shape[0]),dtype=matrix)
+    
+    err_n  = np.zeros((freq.shape[0]))
+    err_k  = np.zeros((freq.shape[0]))
+    
+    diag_n = np.zeros((freq.shape[0]))
+    diag_k = np.zeros((freq.shape[0]))
+    
+    inv_n = np.zeros((freq.shape[0]))
+    inv_k = np.zeros((freq.shape[0]))
+    
+    hess_n = np.zeros((freq.shape[0]))
+    hess_k = np.zeros((freq.shape[0]))
+    
+    err_n_bfgs = np.zeros((freq.shape[0]))
+    err_k_bfgs = np.zeros((freq.shape[0]))
+    
+    # err2_n  = np.empty((freq.shape[0]))
+    # err2_k  = np.empty((freq.shape[0]))
+    # err3_n  = np.empty((freq.shape[0]))
+    # err3_k  = np.empty((freq.shape[0]))
+    # ---------------------------------------------------------------------------- #
+    
+    measAngleTh = unwrap(angle(meas)) 
+    # measAngleTh = unwrap(angle(meas)) + 2 * np.pi
+    measAbsTh = abs(meas)
+    
+    for i in range(0, f.shape[0] - 1):
+        # pouet = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP)
+        # res = minimize(pouet, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # res = minimize(pouet, init[i], method=""SLSQP"")
+        
+        # # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        err_function_min = lambda x: errorFct_TF(x[0], x[1], thick, f[i], fctTrans[i], FP)
+        res = minimize(err_function_min, init[i], method=""Nelder-Mead"", tol=1e-6)
+        
+        err_function_log = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP, ""log"")
+        
+        # res = minimize(err_function_log, init[i], method=""Nelder-Mead"", tol=1e-6)
+        err_n_bfgs[i] = 0
+        err_k_bfgs[i] = 0
+                
+        # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        # if i==0:
+            # res = minimize(err_function_log, init[i], method=""BFGS"", tol=1e-6)
+        # else:
+            # res = minimize(err_function_log, [nInv[i-1],kInv[i-1]], method=""BFGS"", tol=1e-6)
+
+        
+        # err_n_bfgs[i] = np.sqrt(res[""hess_inv""][0][0])
+        # err_k_bfgs[i] = np.sqrt(res[""hess_inv""][1][1])
+        
+        
+        
+        # # err_funtion_log_minimzied = errorFct(res['x'][0], res['x'][1], thick, f[i], measAbsTh[i], measAngleTh[i], FP,print_bool=True)
+        # err_fctrans = errorFct_TF(res['x'][0], res['x'][1], thick, f[i], fctTrans[i], FP,)
+        
+        err_function_trans = lambda x: errorFct_TF(x[0], x[1], thick, f[i], fctTrans[i], FP)
+        # res = minimize(err_function_trans, init[i], method=""BFGS"", tol=1e-6)
+        res = minimize(err_function_trans, init[i], method=""BFGS"", tol=1e-6)
+        
+        print_bool_res = False
+        
+        if print_bool_res:
+            print(f""###################"") 
+            print(f""freq : {f[i]/1e12}"")
+            print(f""init : {init[i]}"")
+            print(f""res : {res}"")
+            print(f""minimizing..."")
+            print(f""Status : {res['message']}"")
+            # print(f"" err_n_bfgs : {err_n_bfgs[i]}"")
+            # print(f"" err_k_bfgs : {err_k_bfgs[i]}"")
+            print(f""Total Evaluations: {res['nfev']}"")
+            print(f""n : {res['x'][0]}"")
+            print(f""k : {res['x'][1]}"")
+            # print(f""erreur : {err_funtion_log_minimzied}"")
+            # print(f""erreur : {err_fctrans}"")
+            print(f""===================="") 
+        
+        # cf https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize
+        # for other methods
+        nInv[i] = res.x[0]
+        kInv[i] = res.x[1]
+    
+    #TOVERIFY
+    for i in range(0, f.shape[0] - 1):
+        # puick = errorFct(res.x[0],res.x[1],thick,f[i],measAbsTh[i], measAngleTh[i], FP)
+        # puick = errorFct(nInv[i],kInv[i],thick,f[i],measAbsTh[i], measAngleTh[i], FP)
+        
+        ##Aditya_code
+
+        # ========================== numdifftools in C space ========================= #
+        # G_numdiff_C = nd.Gradient(pouet)([res.x[0], res.x[1]])
+        
+        # H_Numdiff_C = nd.Hessian(pouet, method = ""central"", order =2)([res.x[0], res.x[1]])
+        # H_Numdiff_C = nd.Hessian(pouet)([res.x[0], res.x[1]])
+        
+        
+        # ############################################################################ #
+        if print_bool_res:
+            print(f""###################"")
+            print(f""replacing"")
+            print(f""freq: {f[i]/1e12}"")
+            print(f""n : {nInv[i]}"")
+            print(f""k : {kInv[i]}"")
+        
+        try:
+            step_val = 1e-6 * (1e-3 / thick)
+            # print(f""step : {step_val}"")
+            # # pouetpouet = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP,print_bool=True)
+            # pouetpouet = lambda x: errorFct_hess(x[0], x[1], nInv[i], kInv[i],  thick, f[i], measAbsTh[i], measAngleTh[i],FP,print_bool=True)
+            # H_Numdiff_C = nd.Hessian(pouetpouet, step=1e-6)([nInv[i] , kInv[i]])
+            err_fct_hess = lambda x: errorFct_TF(x[0], x[1],  thick, f[i], fctTrans[i],FP,print_bool=True)
+            H_Numdiff_C = nd.Hessian(err_fct_hess, step=step_val)([nInv[i] , kInv[i]])
+            Hessian_nd_c[i] = H_Numdiff_C
+            inv_hess = np.linalg.inv(H_Numdiff_C)
+            diag_inv_hess = np.diag(inv_hess)
+            err = np.sqrt(diag_inv_hess)
+            # print(f""hess_c = {H_Numdiff_C}"")
+            # print(f""inv_hess_c = {inv_hess}"")
+            if print_bool_res:
+                print(f""diag_inv_hess_c = {diag_inv_hess}"")
+                print(f""===================="")
+        except Exception as e:
+            print(e)
+            inv_hess = np.zeros((2,2))
+            diag_inv_hess = np.zeros(2)
+            err = np.zeros(2)
+            print(f""diag_inv_hess_c = {diag_inv_hess}"")
+            print(f""===================="")
+        # ############################################################################ #
+        
+        
+        # det = np.linalg.det(H_Numdiff_C)
+        # print(H_Numdiff_C)
+        
+        # Gradient_nd_c[i] = G_numdiff_C
+        
+        
+        # err = np.sqrt(np.diag(np.linalg.inv(H_Numdiff_C)))
+        
+        # ############################################################################ #
+        diag_n[i] = diag_inv_hess[0]
+        diag_k[i] = diag_inv_hess[1]
+    
+        err_n[i] = err[0]
+        err_k[i] = err[1]
+        
+        hess_n[i] = H_Numdiff_C[0][0] 
+        hess_k[i] = H_Numdiff_C[1][1] 
+        
+        inv_n[i] = inv_hess[0][0]
+        inv_k[i] = inv_hess[1][1]
+        # ############################################################################ #
+        
+        # inv_hess_nd_c = np.linalg.inv(H_Numdiff_C)
+        
+        # print(f""inv nd hess = {np.linalg.inv(H_Numdiff_C)}"")
+        # print(f""nd hess C =  {H_Numdiff_C}"")
+        # print(f""inv nd hess = {inv_hess_nd_c}"")
+        # ---------------------------------------------------------------------------- #
+    
+        # ========================== numdifftools in R space ========================= #
+        # pouet2 = lambda x: errorFct2(x[0],x[1],thick,f[i],np.real(measAbsTh[i]),np.imag(measAbsTh[i]))
+
+        # H_Numdiff_R = nd.Hessian(pouet2)([nInv[i], kInv[i]])
+        
+        # inv_hess_r = np.linalg.inv(H_Numdiff_R)
+        # diag_inv_hess_r = np.diag(inv_hess_r)
+        
+        
+        
+        # print(f""hess_r = {diag_inv_hess_r}"")
+        # ############################################################################ #
+        
+        
+        # inv_hess_nd_r = np.linalg.inv(H_Numdiff_R)
+
+        # err2 = np.sqrt(np.diag(np.linalg.inv(H_Numdiff_R)))
+        # err2_n[i] = err2[0]
+        # err2_k[i] = err2[1]
+        # # print(f""nd hess R =  {H_Numdiff_R}"")
+        # # print(f""inv nd hess R= {inv_hess_nd_r}"")
+        # # print(f""error nd Hess C = {err2}"")
+
+        # Hessian_nd_r[i] = H_Numdiff_R
+        # ---------------------------------------------------------------------------- #
+    
+    
+    
+        # ============================= sympy in R space ============================= #
+        # values = {n: nInv[i], k: kInv[i], w: 2*np.pi*f[i], d: thick, C1: np.real(measAbsTh[i]), C2: np.imag(measAbsTh[i])}
+        # H_sympy_R = Hessian.subs(values)
+        
+        # H_sympy_R = np.array(H_sympy_R).astype(np.float64)
+        # Hessian_sp_r[i]= H_sympy_R
+        # inv_hess_sp_r = np.linalg.inv(H_sympy_R)
+        # err3 = np.sqrt(np.diag(np.linalg.inv(H_sympy_R)))
+        # err3_n[i] = err3[0]
+        # err3_k[i] = err3[1]
+        # # print(f""sp hess R =  {H_sympy_R}"")
+        # # print(f""inv sp hess R= {inv_hess_sp_r}"")
+        # # print(f""error nd Hess C = {err3}"")
+        # ---------------------------------------------------------------------------- #
+        
+        ##Aditya_code
+
+    ref_index = j * kInv + nInv
+
+    err_n = np.nan_to_num(err_n)
+    err_k = np.nan_to_num(err_k)
+
+    # indexes = pd.DataFrame([nInv, kInv, err_n, err_k], columns=[""n"", ""k"", ""err_n"", ""err_k""])
+    # indexes = pd.DataFrame({""n"": nInv, ""k"": kInv, ""err_n"" : err_n, ""err_k"" : err_k})
+    # print(indexes)
+    # table = pa.Table.from_pandas(indexes)
+    
+    # ############################################################################ #
+    # table = pa.Table.from_arrays([freq/1e12,nInv,err_n, kInv,err_k],names=[""freq"",""n"",""err_n"", ""k"", ""err_k""])
+    table = pa.Table.from_arrays([freq/1e12,nInv,hess_n,inv_n,diag_n,err_n,err_n_bfgs, kInv,hess_k,inv_k,diag_k,err_k, err_k_bfgs],names=[""freq"",""n"",""hess_n"",""inv_n"",""diag_n"",""err_n"", ""err_n_bfgs"", ""k"",""hess_k"",""inv_k"",""diag_k"", ""err_k"", ""err_k_bfgs""])
+    
+    err_filename = path_(Csts.FileName).name
+    err_filename = filename.joinpath(f""errors_{err_filename}"")
+    
+    options = csv_.WriteOptions(include_header=True,delimiter='\t')
+    csv_.write_csv(table,err_filename,options)
+    # ############################################################################ #
+
+    # ref_index = j * kInv + nInv
+    # print(f""filename = {Csts.FileName}"")
+    return ref_index
+
+def dielcal(mytransferfunction, z, myglobalparameters,FP ,scattering=None):
+    global zz, mytransferfunction2
+
+
+    """""" 
+    Takes variables from the the input and uses the inverseProblem
+    The first part is the initialization and the last line is the optimisation
+    """"""
+    mytransferfunction2 = np.copy(mytransferfunction)
+    zz = np.copy(z)
+    ref_index = np.ones(len(myglobalparameters.w))
+    myangleTF = np.unwrap(np.angle(mytransferfunction))
+
+    for compt in range(0, 3):  # the max of this loop was choosen to optimize the effective index for a 250micron thick quartz substrate it gives good result at 1/1000 but shows weird convergeance (larger is not better so this may had to be changed with the sample)
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        myangleTrans = np.nan_to_num(np.angle(tt))  # ici il y a un truc a bien regarder
+        phase = np.unwrap(myangleTF + myangleTrans) # we are taking the phase from the transfer function
+        
+        naprox = 1 + abs(phase * c / zz / myglobalparameters.w)  # this gives the real part of the index
+        
+        ref_index = naprox + j * (ref_index.imag)  # this gives the real part of the index
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        kaprox = -np.nan_to_num(c / z / myglobalparameters.w * np.log(abs(mytransferfunction) / (tt)))
+        ref_index = j * kaprox.real + ref_index.real
+    ref_index[0] = 1
+
+    ref_index2 = inverseProblem(myglobalparameters.w / 2 / np.pi, mytransferfunction2, zz, FP, ref_index, 6e12)
+
+    return np.array(ref_index2 ** 2)
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/launch_optimization.py",".py","229","5","import subprocess 
+command = """" 
+process = subprocess.Popen(command.split(),stdout=subprocess.PIPE,stderr=subprocess.PIPE) 
+output,error = process.communicate() 
+print(""Output : "" + str(output) + ""\n Error: "" + str(error) + ""\n"")","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/epsillonlayers8.py",".py","23789","499","# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math
+
+# from __future__ import print_function
+from pyswarm import pso
+import random
+import numpy as np
+import scipy.optimize as optimize
+import matplotlib.pyplot as plt
+from numpy import *
+from pylab import *
+from scipy.io.matlab import mio
+
+# from scipy import optimize
+from scipy.optimize import minimize
+
+###############################################################################
+###############################################################################
+j = 1j
+c = 2.998e8
+###############################################################################
+
+# defining the Error function
+def errorFct(n, k, d, frequency, measAmp, measAngle, FP):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 1:
+        FP_effect = 1 / (1-  ((refInd - nair) / (refInd + nair)) ** 2 * exp(-2 * 1j * refInd * omeg * d / c))
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe * FP_effect
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    errorAngle = (angleTaf + angleTfa + angleTfe + angleFP - measAngle) ** 2
+    errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+
+def errorFct2(n, k, d, frequency, meas, meassave, FP, eps_cell, thickn1, thickn2, fsave):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg=2*np.pi*frequency
+    refindex1 = np.sqrt(eps_cell)
+    refindex2 = np.sqrt(eps_cell)
+    omegsave = 2*np.pi*fsave
+    nair=1.0
+    nairsave = np.ones(len(fsave))
+    refInd = n - 1j * k
+
+    refIndsave = np.ones(len(fsave))*refInd
+    refindex1save = np.ones(len(fsave))*refindex1
+    refindex2save = np.ones(len(fsave))*refindex2
+    
+    # Fresnel coefficients (transmission)
+    t2a=2*refindex2save/(refindex2save+nairsave)
+    ta1=2*nairsave/(refindex1save+nairsave)
+    t1s=2*refindex1save/(refindex1save+refIndsave)
+    ts2=2*refIndsave/(refindex2save+refIndsave)
+    tt = t2a * ta1 * t1s * ts2
+    
+#    rs1=(refIndsave-refindex1save)/(refIndsave+refindex1save)
+#    rs2=(refIndsave-refindex2save)/(refIndsave+refindex2save)
+#    r1s=(refindex1save-refIndsave)/(refIndsave+refindex1save)
+#    r2s=(refindex2save-refIndsave)/(refIndsave+refindex2save)
+#    r1a=(refindex1save-nairsave)/(refindex1save+nairsave)
+#    r2a=(refindex2save-nairsave)/(refindex2save+nairsave)
+    rs1=(refIndsave-refindex1save)/(refIndsave+refindex1save)
+    rs2=(refIndsave-refindex2save)/(refIndsave+refindex2save)
+    r1s=(refindex1save-refIndsave)/(refIndsave+refindex1save)
+    r2s=(refindex2save-refIndsave)/(refIndsave+refindex2save)
+    r1a=(refindex1save-nairsave)/(nairsave+refindex1save)
+    r2a=(refindex2save-nairsave)/(nairsave+refindex2save)
+    
+    den1=np.exp(j*omegsave*(refindex1save*thickn1+refIndsave*d+refindex2save*thickn2)/c)
+    den2=-r1a*r1s*np.exp(j*omegsave*(refIndsave*d+refindex2save*thickn2-refindex1save*thickn1)/c)
+    den3=-r1a*rs2*np.exp(j*omegsave*(refindex2save*thickn2-refIndsave*d-refindex1save*thickn1)/c)
+    den4=r2a*r2a*r2s*r2s*np.exp(j*omegsave*(-refindex1save*thickn1-refindex2save*thickn2+refIndsave*d)/c)
+    den5=-rs1*r2a*np.exp(j*omegsave*(refindex1save*thickn1-refindex2save*thickn2-refIndsave*d)/c)
+    den6=-r2a*r2a*np.exp(j*omegsave*-1*(refindex1save*thickn1+refindex2save*thickn2+refIndsave*d)/c)
+    den7=-rs1*rs1*np.exp(j*omegsave*(refindex1save*thickn1+refindex2save*thickn2-refIndsave*d)/c)
+    den8=-r2a*r2s*np.exp(j*omegsave*(refindex1save*thickn1+refIndsave*d-refindex2save*thickn2)/c)
+		
+    Zsample2=tt/(den1+den2+den3+den4+den5+den6+den7+den8)
+    #Zsample2=tt/(den1+den2)
+    #Zref2=np.exp(-j*omegsave*nairsave*(thickn1+d+thickn2)/c)
+    t1a=2*refindex1save/(refindex1save+nairsave)
+    ta2=2*nairsave/(refindex2save+nairsave)
+    
+    ttref=t2a*ta1*t1a*ta2
+    
+    ra1=-r1a
+    ra2=-r2a
+    
+    den1ref=np.exp(j*omegsave*(refindex1save*thickn1+nairsave*d+refindex2save*thickn2)/c)
+    den2ref=-r1a*r1a*np.exp(j*omegsave*(nairsave*d+refindex2save*thickn2-refindex1save*thickn1)/c)
+    den3ref=-r1a*ra2*np.exp(j*omegsave*(refindex2save*thickn2-nairsave*d-refindex1save*thickn1)/c)
+    den4ref=r2a*r2a*r2a*r2a*np.exp(j*omegsave*(-refindex1save*thickn1-refindex2save*thickn2+nairsave*d)/c)
+    den5ref=-ra1*r2a*np.exp(j*omegsave*(refindex1save*thickn1-refindex2save*thickn2-nairsave*d)/c)
+    den6ref=-r2a*r2a*np.exp(j*omegsave*-1*(refindex1save*thickn1+refindex2save*thickn2+nairsave*d)/c)
+    den7ref=-ra1*ra1*np.exp(j*omegsave*(refindex1save*thickn1+refindex2save*thickn2-nairsave*d)/c)
+    den8ref=-r2a*r2a*np.exp(j*omegsave*(refindex1save*thickn1+nairsave*d-refindex2save*thickn2)/c)
+    
+    
+    Zref2=ttref/(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref)
+    
+#    Zref1=np.where(np.isnan(Zref)==False,Zref,1e-100+1e-100*1j)
+#    Zref2=np.where(np.isinf(Zref1)==False,Zref1,1e+100+1e+100*1j)
+#    Zsample1=np.where(np.isnan(Zsample)==False,Zsample,1e-100+1e-100*1j)
+#    Zsample2=np.where(np.isinf(Zsample1)==False,Zsample1,1e+100+1e+100*1j)		
+
+    transfcttot=Zsample2/Zref2
+    
+    anglettunwrap = np.unwrap(np.angle(tt))
+    anglett=anglettunwrap[-1]
+    angletf = np.unwrap(np.angle((den1+den2+den3+den4+den5+den6+den7+den8))) #-2*10*np.pi
+    angletfunwrap = angletf[-1]
+    
+    anglettrefunwrap = np.unwrap(np.angle(ttref))
+    anglettref = anglettrefunwrap[-1]
+    angleref = np.unwrap(np.angle(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref))
+    anglerefunwrap = angleref[-1]
+    #anglerefunwrap = omeg*nair*(thickn1+d+thickn2)/c
+    measunwrap=np.unwrap(np.angle(meassave))
+    anglemeas=measunwrap[-1]
+    errorAngle2 = ((anglett - angletfunwrap + anglerefunwrap - anglettref) - anglemeas) ** 2
+    errorAbs = (np.log(abs(transfcttot[-1])) - np.log(abs(meas))) ** 2
+    erreur2 = (errorAngle2 + errorAbs)
+    return erreur2
+
+def errorFct3(n, k, d, frequency, meas, meassave, FP, eps_cell, thickn1, thickn2, fsave):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg=2*np.pi*frequency
+    refindex1 = np.sqrt(eps_cell)
+    refindex2 = np.sqrt(eps_cell)
+    omegsave = 2*np.pi*fsave
+    nair=1.0
+    nairsave = np.ones(len(fsave))
+    refInd = n - 1j * k
+
+    refIndsave = np.ones(len(fsave))*refInd
+    refindex1save = np.ones(len(fsave))*refindex1
+    refindex2save = np.ones(len(fsave))*refindex2
+    
+    # Fresnel coefficients (transmission)
+    t2a=2*refindex2save/(refindex2save+nairsave)
+    ta1=2*nairsave/(refindex1save+nairsave)
+    t1s=2*refindex1save/(refindex1save+refIndsave)
+    ts2=2*refIndsave/(refindex2save+refIndsave)
+    tt = t2a * ta1 * t1s * ts2
+    
+#    rs1=(refIndsave-refindex1save)/(refIndsave+refindex1save)
+#    rs2=(refIndsave-refindex2save)/(refIndsave+refindex2save)
+#    r1s=(refindex1save-refIndsave)/(refIndsave+refindex1save)
+#    r2s=(refindex2save-refIndsave)/(refIndsave+refindex2save)
+#    r1a=(refindex1save-nairsave)/(refindex1save+nairsave)
+#    r2a=(refindex2save-nairsave)/(refindex2save+nairsave)
+    rs1=(refIndsave-refindex1save)/(refIndsave+refindex1save)
+    rs2=(refIndsave-refindex2save)/(refIndsave+refindex2save)
+    r1s=(refindex1save-refIndsave)/(refIndsave+refindex1save)
+    r2s=(refindex2save-refIndsave)/(refIndsave+refindex2save)
+    r1a=(refindex1save-nairsave)/(nairsave+refindex1save)
+    r2a=(refindex2save-nairsave)/(nairsave+refindex2save)
+    
+    den1=np.exp(j*omegsave*(refindex1save*thickn1+refIndsave*d+refindex2save*thickn2)/c)
+    den2=-r1a*r1s*np.exp(j*omegsave*(refIndsave*d+refindex2save*thickn2-refindex1save*thickn1)/c)
+    den3=-r1a*rs2*np.exp(j*omegsave*(refindex2save*thickn2-refIndsave*d-refindex1save*thickn1)/c)
+    den4=r2a*r2a*r2s*r2s*np.exp(j*omegsave*(-refindex1save*thickn1-refindex2save*thickn2+refIndsave*d)/c)
+    den5=-rs1*r2a*np.exp(j*omegsave*(refindex1save*thickn1-refindex2save*thickn2-refIndsave*d)/c)
+    den6=-r2a*r2a*np.exp(j*omegsave*-1*(refindex1save*thickn1+refindex2save*thickn2+refIndsave*d)/c)
+    den7=-rs1*rs1*np.exp(j*omegsave*(refindex1save*thickn1+refindex2save*thickn2-refIndsave*d)/c)
+    den8=-r2a*r2s*np.exp(j*omegsave*(refindex1save*thickn1+refIndsave*d-refindex2save*thickn2)/c)
+		
+    Zsample2=tt/(den1+den2+den3+den4+den5+den6+den7+den8)
+    #Zsample2=tt/(den1+den2)
+    #Zref2=np.exp(-j*omegsave*nairsave*(thickn1+d+thickn2)/c)
+    
+    t1a=2*refindex1save/(refindex1save+nairsave)
+    ta2=2*nairsave/(refindex2save+nairsave)
+    
+    ttref=t2a*ta1*t1a*ta2
+    
+    ra1=-r1a
+    ra2=-r2a
+    
+    den1ref=np.exp(j*omegsave*(refindex1save*thickn1+nairsave*d+refindex2save*thickn2)/c)
+    den2ref=-r1a*r1a*np.exp(j*omegsave*(nairsave*d+refindex2save*thickn2-refindex1save*thickn1)/c)
+    den3ref=-r1a*ra2*np.exp(j*omegsave*(refindex2save*thickn2-nairsave*d-refindex1save*thickn1)/c)
+    den4ref=r2a*r2a*r2a*r2a*np.exp(j*omegsave*(-refindex1save*thickn1-refindex2save*thickn2+nairsave*d)/c)
+    den5ref=-ra1*r2a*np.exp(j*omegsave*(refindex1save*thickn1-refindex2save*thickn2-nairsave*d)/c)
+    den6ref=-r2a*r2a*np.exp(j*omegsave*-1*(refindex1save*thickn1+refindex2save*thickn2+nairsave*d)/c)
+    den7ref=-ra1*ra1*np.exp(j*omegsave*(refindex1save*thickn1+refindex2save*thickn2-nairsave*d)/c)
+    den8ref=-r2a*r2a*np.exp(j*omegsave*(refindex1save*thickn1+nairsave*d-refindex2save*thickn2)/c)
+    
+    
+    Zref2=ttref/(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref)
+    
+#    Zref1=np.where(np.isnan(Zref)==False,Zref,1e-100+1e-100*1j)
+#    Zref2=np.where(np.isinf(Zref1)==False,Zref1,1e+100+1e+100*1j)
+#    Zsample1=np.where(np.isnan(Zsample)==False,Zsample,1e-100+1e-100*1j)
+#    Zsample2=np.where(np.isinf(Zsample1)==False,Zsample1,1e+100+1e+100*1j)		
+
+    transfcttot=Zsample2/Zref2
+    anglettunwrap = np.unwrap(np.angle(tt))
+    anglett=anglettunwrap[-1]
+    angletf = np.unwrap(np.angle((den1+den2+den3+den4+den5+den6+den7+den8))) #-2*10*np.pi
+    angletfunwrap = angletf[-1]
+    
+    anglettrefunwrap = np.unwrap(np.angle(ttref))
+    anglettref = anglettrefunwrap[-1]
+    angleref = np.unwrap(np.angle(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref))
+    anglerefunwrap = angleref[-1]
+    #anglerefunwrap = omeg*nair*(thickn1+d+thickn2)/c
+    measunwrap=np.unwrap(np.angle(meassave))
+    anglemeas=measunwrap[-1]
+    errorAngle2 = ((anglett - angletfunwrap + anglerefunwrap - anglettref) - anglemeas) ** 2
+    errorAbs = (np.log(abs(transfcttot[-1])) - np.log(abs(meas))) ** 2
+    erreur2 = (errorAngle2 + errorAbs)
+    return erreur2, errorAbs, errorAngle2, anglett, angletfunwrap, anglerefunwrap, anglemeas
+
+# defining the 'so-called' inverse problem to get the refractive index
+def inverseProblem(freq, fctTrans, thick, FP, init, limitUp):
+    """""" Calculates n and k over frequency.
+        freq : Frequency in Hz, must be POSITIVE;
+        fctTrans : Transfer function, must be same size as freq and correspond to positive frequencies;
+        thick : thickness of the sample in meters [m];
+        FP : If equal to 1 takes into account the Farby-Perot effect;
+        init : initial guess for the inverse problem. init should be an array : init = [3, 0.1], for example;
+        limitUp : High frequency limit of the study in Hz;
+    """"""
+    init = np.transpose([init.real, init.imag])
+    f = freq[(freq <= limitUp)]
+
+    ## The measurement
+    meas = fctTrans[(freq <= limitUp)]
+
+    ## Inverse problem
+    nInv = zeros(freq.shape[0])
+    kInv = zeros(freq.shape[0])
+
+    measAngleTh = unwrap(angle(meas))
+    measAbsTh = abs(meas)
+    for i in range(0, f.shape[0] - 1):
+        pouet = lambda x: errorFct(x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP)
+        res = minimize(pouet, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # cf https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize
+        # for other methods
+        nInv[i] = res.x[0]
+        kInv[i] = res.x[1]
+
+    ref_index = j * kInv + nInv
+
+    return ref_index
+
+# defining the 'so-called' inverse problem to get the refractive index
+def inverseProblem2(freq, fctTrans, thick, FP, init, limitUp, eps_cell, thickn1, thickn2):
+    """""" Calculates n and k over frequency.
+        freq : Frequency in Hz, must be POSITIVE;
+        fctTrans : Transfer function, must be same size as freq and correspond to positive frequencies;
+        thick : thickness of the sample in meters [m];
+        FP : If equal to 1 takes into account the Farby-Perot effect;
+        init : initial guess for the inverse problem. init should be an array : init = [3, 0.1], for example;
+        limitUp : High frequency limit of the study in Hz;
+    """"""
+    init = np.transpose([init.real, init.imag])
+    f = freq[(freq <= limitUp)]
+    fsave = []
+
+    ## The measurement
+    meas = fctTrans#[(freq <= limitUp)]
+
+    ## Inverse problem
+    nInv = zeros(freq.shape[0])
+    kInv = zeros(freq.shape[0])
+    erreur2=zeros(freq.shape[0])
+    errorAbs=zeros(freq.shape[0])
+    errorAngle2=zeros(freq.shape[0])
+    anglett=zeros(freq.shape[0])
+    angletfunwrap=zeros(freq.shape[0])
+    anglerefunwrap=zeros(freq.shape[0])
+    anglemeas=zeros(freq.shape[0])
+    #measAngleTh = unwrap(angle(meas))
+    #measAbsTh = abs(meas)
+    
+    for i in range(0, f.shape[0] - 1):
+        fsave=np.append(fsave,f[i])
+        meassave=meas[(freq <= freq[i])]
+        pouet = lambda x: errorFct2(x[0], x[1], thick, f[i], meas[i], meassave, FP, eps_cell[i], thickn1, thickn2, fsave)
+        res = minimize(pouet, init[i], method=""Nelder-Mead"", tol=1e-6)
+        
+        pouet2 = errorFct3(res.x[0], res.x[1], thick, f[i], meas[i], meassave, FP, eps_cell[i], thickn1, thickn2, fsave)
+        erreur2[i]=pouet2[0]
+        errorAbs[i]=pouet2[1]
+        errorAngle2[i]=pouet2[2]
+        anglett[i]=pouet2[3]
+        angletfunwrap[i]=pouet2[4]
+        anglerefunwrap[i]=pouet2[5]
+        anglemeas[i]=pouet2[6]
+        nInv[i] = res.x[0]
+        kInv[i] = res.x[1] 
+        
+    ref_index = 1j * kInv + nInv
+
+    return ref_index,erreur2,errorAbs,errorAngle2,anglett,angletfunwrap,anglerefunwrap,anglemeas
+
+def dielcal(mytransferfunction, z, myglobalparameters, FP):
+    global zz, mytransferfunction2
+
+
+    """""" 
+    Takes variables from the the input and uses the inverseProblem
+    The first part is the initialization and the last line is the optimisation
+    """"""
+    mytransferfunction2 = np.copy(mytransferfunction)
+    zz = np.copy(z)
+    ref_index = np.ones(len(myglobalparameters.w))
+    myangleTF = np.unwrap(np.angle(mytransferfunction))
+
+    for compt in range(0, 3):  # the max of this loop was choosen to optimize the effective index for a 250micron thick quartz substrate it gives good result at 1/1000 but shows weird convergeance (larger is not better so this may had to be changed with the sample)
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * zz * (ref_index) / c)
+        myangleTrans = np.nan_to_num(np.angle(tt))  # ici il y a un truc a bien regarder
+        phase = np.unwrap(myangleTF + myangleTrans)  # we are taking the phase from the transfer function
+        naprox = 1 + abs(phase * c / zz / myglobalparameters.w)  # this gives the real part of the index
+        ref_index = naprox + j * (ref_index.imag)  # this gives the real part of the index
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w *zz* (ref_index) / c)
+        kaprox = -np.nan_to_num(c / zz / myglobalparameters.w * np.log(abs(mytransferfunction) / (tt)))
+        ref_index = j * kaprox.real + ref_index.real
+    ref_index[0] = 1
+
+    ref_index2 = inverseProblem( myglobalparameters.w / 2 / np.pi, mytransferfunction2, zz, FP, ref_index, 6e12)
+
+    return np.array(ref_index2 ** 2)
+
+def dielcal2(mytransferfunction, z, myglobalparameters, eps_cell, thickn1, thickn2,FP):
+    global zz, mytransferfunction2
+
+
+    """""" 
+    Takes variables from the the input and uses the inverseProblem
+    The first part is the initialization and the last line is the optimisation
+    """"""
+    mytransferfunction2 = np.copy(mytransferfunction)
+    zz = np.copy(z)
+    ref_index = np.ones(len(myglobalparameters.w))+j*np.zeros(len(myglobalparameters.w))
+    myangleTF = np.unwrap(np.angle(mytransferfunction))
+    
+    refindex1=np.sqrt(eps_cell)
+    refindex2=np.sqrt(eps_cell)
+    omeg=myglobalparameters.w
+
+    for compt in range(0, 1):  # the max of this loop was choosen to optimize the effective index for a 250micron thick quartz substrate it gives good result at 1/1000 but shows weird convergeance (larger is not better so this may had to be changed with the sample)
+        t2a=2*refindex2/(refindex2+1)
+        ta1=2/(refindex1+1)
+        t1s=2*refindex1/(refindex1+ref_index)
+        ts2=2*ref_index/(refindex2+ref_index)
+        tt = t2a * ta1 * t1s * ts2
+        
+        rs1=(ref_index-refindex1)/(ref_index+refindex1)
+        rs2=(ref_index-refindex2)/(ref_index+refindex2)
+        r1s=(refindex1-ref_index)/(ref_index+refindex1)
+        r2s=(refindex2-ref_index)/(ref_index+refindex2)
+        r1a=(refindex1-1)/(refindex1+1)
+        r2a=(refindex2-1)/(refindex2+1)
+        
+        den1=np.exp(j*omeg*(refindex1*thickn1+ref_index*zz+refindex2*thickn2)/c)
+        den2=-r1a*r1s*np.exp(j*omeg*(ref_index*zz+refindex2*thickn2-refindex1*thickn1)/c)
+        den3=-r1a*rs2*np.exp(j*omeg*(refindex2*thickn2-ref_index*zz-refindex1*thickn1)/c)
+        den4=r2a*r2a*r2s*r2s*np.exp(j*omeg*(-refindex1*thickn1-refindex2*thickn2+ref_index*zz)/c)
+        den5=-rs1*r2a*np.exp(j*omeg*(refindex1*thickn1-refindex2*thickn2-ref_index*zz)/c)
+        den6=-r2a*r2a*np.exp(j*omeg*-1*(refindex1*thickn1+refindex2*thickn2+ref_index*zz)/c)
+        den7=-rs1*rs1*np.exp(j*omeg*(refindex1*thickn1+refindex2*thickn2-ref_index*zz)/c)
+        den8=-r2a*r2s*np.exp(j*omeg*(refindex1*thickn1+ref_index*zz-refindex2*thickn2)/c)
+        		
+        Zsample2=tt/(den1+den2+den3+den4+den5+den6+den7+den8)
+        
+        t1a=2*refindex1/(refindex1+1)
+        ta2=2*1/(refindex2+1)
+        
+        ttref=t2a*ta1*t1a*ta2
+        
+        ra1=-r1a
+        ra2=-r2a
+        
+        den1ref=np.exp(j*omeg*(refindex1*thickn1+1*zz+refindex2*thickn2)/c)
+        den2ref=-r1a*r1a*np.exp(j*omeg*(1*zz+refindex2*thickn2-refindex1*thickn1)/c)
+        den3ref=-r1a*ra2*np.exp(j*omeg*(refindex2*thickn2-1*zz-refindex1*thickn1)/c)
+        den4ref=r2a*r2a*r2a*r2a*np.exp(j*omeg*(-refindex1*thickn1-refindex2*thickn2+1*zz)/c)
+        den5ref=-ra1*r2a*np.exp(j*omeg*(refindex1*thickn1-refindex2*thickn2-1*zz)/c)
+        den6ref=-r2a*r2a*np.exp(j*omeg*-1*(refindex1*thickn1+refindex2*thickn2+1*zz)/c)
+        den7ref=-ra1*ra1*np.exp(j*omeg*(refindex1*thickn1+refindex2*thickn2-1*zz)/c)
+        den8ref=-r2a*r2a*np.exp(j*omeg*(refindex1*thickn1+1*zz-refindex2*thickn2)/c)
+        
+        
+        #Zref2=1/np.exp(j*omeg*(1*thickn1+1*zz+1*thickn2)/c)
+        Zref2=ttref/(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref)
+        transfctth=Zsample2/Zref2
+
+
+        myangleTrans = np.nan_to_num(np.unwrap(np.angle(transfctth)))  # ici il y a un truc a bien regarder
+        myangleTF = np.unwrap(np.nan_to_num(np.angle(mytransferfunction)))#-np.nan_to_num(np.unwrap(np.angle(Spulseinit)))
+        phase = (-myangleTF + myangleTrans)  # we are taking the phase from the transfer function
+        naprox = 1+abs(phase * c / (zz * omeg))  # this gives the real part of the index
+        #naprox = 2*np.ones(len(myglobalparameters.w))
+        ref_index = naprox + j * (ref_index.imag)  # this gives the real part of the index
+        
+        t2a=2*refindex2/(refindex2+1)
+        ta1=2/(refindex1+1)
+        t1s=2*refindex1/(refindex1+ref_index)
+        ts2=2*ref_index/(refindex2+ref_index)
+        tt = t2a * ta1 * t1s * ts2
+        
+        rs1=(ref_index-refindex1)/(ref_index+refindex1)
+        rs2=(ref_index-refindex2)/(ref_index+refindex2)
+        r1s=(refindex1-ref_index)/(ref_index+refindex1)
+        r2s=(refindex2-ref_index)/(ref_index+refindex2)
+        r1a=(refindex1-1)/(refindex1+1)
+        r2a=(refindex2-1)/(refindex2+1)
+        
+        den1=np.exp(j*omeg*(refindex1*thickn1+ref_index*zz+refindex2*thickn2)/c)
+        den2=-r1a*r1s*np.exp(j*omeg*(ref_index*zz+refindex2*thickn2-refindex1*thickn1)/c)
+        den3=-r1a*rs2*np.exp(j*omeg*(refindex2*thickn2-ref_index*zz-refindex1*thickn1)/c)
+        den4=r2a*r2a*r2s*r2s*np.exp(j*omeg*(-refindex1*thickn1-refindex2*thickn2+ref_index*zz)/c)
+        den5=-rs1*r2a*np.exp(j*omeg*(refindex1*thickn1-refindex2*thickn2-ref_index*zz)/c)
+        den6=-r2a*r2a*np.exp(j*omeg*-1*(refindex1*thickn1+refindex2*thickn2+ref_index*zz)/c)
+        den7=-rs1*rs1*np.exp(j*omeg*(refindex1*thickn1+refindex2*thickn2-ref_index*zz)/c)
+        den8=-r2a*r2s*np.exp(j*omeg*(refindex1*thickn1+ref_index*zz-refindex2*thickn2)/c)
+        		
+        Zsample2=tt/(den1+den2+den3+den4+den5+den6+den7+den8)
+        
+        t1a=2*refindex1/(refindex1+1)
+        ta2=2*1/(refindex2+1)
+        
+        ttref=t2a*ta1*t1a*ta2
+        
+        ra1=-r1a
+        ra2=-r2a
+        
+        den1ref=np.exp(j*omeg*(refindex1*thickn1+1*zz+refindex2*thickn2)/c)
+        den2ref=-r1a*r1a*np.exp(j*omeg*(1*zz+refindex2*thickn2-refindex1*thickn1)/c)
+        den3ref=-r1a*ra2*np.exp(j*omeg*(refindex2*thickn2-1*zz-refindex1*thickn1)/c)
+        den4ref=r2a*r2a*r2a*r2a*np.exp(j*omeg*(-refindex1*thickn1-refindex2*thickn2+1*zz)/c)
+        den5ref=-ra1*r2a*np.exp(j*omeg*(refindex1*thickn1-refindex2*thickn2-1*zz)/c)
+        den6ref=-r2a*r2a*np.exp(j*omeg*-1*(refindex1*thickn1+refindex2*thickn2+1*zz)/c)
+        den7ref=-ra1*ra1*np.exp(j*omeg*(refindex1*thickn1+refindex2*thickn2-1*zz)/c)
+        den8ref=-r2a*r2a*np.exp(j*omeg*(refindex1*thickn1+1*zz-refindex2*thickn2)/c)
+        
+        
+        #Zref2=1/np.exp(j*omeg*(1*thickn1+1*zz+1*thickn2)/c)
+        Zref2=ttref/(den1ref+den2ref+den3ref+den4ref+den5ref+den6ref+den7ref+den8ref)
+        
+        transfctth=Zsample2/Zref2
+        
+        kaprox = -np.nan_to_num(c*(1/zz)*2*np.log(abs(mytransferfunction/transfctth))/(2*omeg))
+        #kaprox=np.zeros(len(omeg))
+        ref_index = j * kaprox.real + ref_index.real
+    #ref_index[0] = 1
+
+    #print(ref_index)
+#    eps =2.5*np.ones(len(myglobalparameters.w))
+#    delta_eps=35
+#    tau=3e-12
+#    eps=eps+delta_eps/(1E0+j*tau*myglobalparameters.w)
+#    ref_index = np.sqrt(eps)
+    
+#    plt.figure('ref_index',figsize=(12,8))
+#    plt.plot(myglobalparameters.freq,np.real(ref_index),'b-')
+#    plt.plot(myglobalparameters.freq,np.imag(ref_index),'r-')
+    
+    test = inverseProblem2(myglobalparameters.w / 2 / np.pi, mytransferfunction2, zz, 1, ref_index, 6e12, eps_cell, thickn1, thickn2)
+    ref_index2 = test[0]
+    erreur2=test[1]
+    errorAbs=test[2]
+    errorAngle2=test[3]
+    anglett=test[4]
+    angletfunwrap=test[5]
+    anglerefunwrap=test[6]
+    anglemeas=test[7]   
+    return np.array(ref_index2 ** 2), erreur2,errorAbs,errorAngle2,anglett,angletfunwrap,anglerefunwrap,anglemeas
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/epsillon3-original.py",".py","5717","152","# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math, cmath
+
+# from __future__ import print_function
+from pyswarm import pso
+import random
+import numpy as np
+import scipy.optimize as optimize
+import matplotlib.pyplot as plt
+from numpy import *
+from pylab import *
+from scipy.io.matlab import mio
+
+# from scipy import optimize
+from scipy.optimize import minimize
+
+###############################################################################
+###############################################################################
+j = 1j
+c = 2.998e8
+
+
+###############################################################################
+
+# defining the Error function
+def errorFct(n, k, d, frequency, measAmp, measAngle, FP):
+    """""" Function to create a model giving, the frequencies, its ""n"", its ""k"", the thickness of the sample in m
+    and if the Fabry-Perot effect is taken into account""""""
+    omeg = 2 * pi * frequency
+    nair = 1  # Refractive index of Air
+    refInd = n - 1j * k
+    # Fresnel coefficients (transmission)
+    Taf = 2 * nair / (nair + (refInd))  # Air => Sample
+    Tfa = 2 * (refInd) / (nair + (refInd))  # Sample => Air
+    Tpe = exp(-1j * (refInd - nair) * omeg * d / c)  # Propagation
+
+    # FP_effect is the term taking into account Fabry-Perot multiple reflections in the sample
+    FP_effect = 1
+
+    if FP == 0:
+        FP_effect = 1 / (
+            1
+            - ((refInd - nair) / (refInd + nair)) ** 2
+            * exp(-2 * 1j * refInd * omeg * d / c)
+        )
+        z = Taf * Tfa * Tpe * FP_effect
+    else:
+        z = Taf * Tfa * Tpe 
+
+    angleTaf = angle(Taf)
+    angleTfa = angle(Tfa)
+    angleTfe = -(n - nair) * omeg * d / c
+    angleFP = angle(FP_effect)
+
+    #à utiliser en premier lieu
+    #ThZ = abs(z)*abs(exp(j*(angleTaf+angleTfa+angleTfe+angleFP)))
+    #measZ = measAmp*abs(exp(j*measAngle))
+    #erreur = abs((ThZ-measZ)**2)
+
+    #à utiliser si la version en exp ne marche pas (partie réelle de l'indice de réfraction inversée)
+    ThZ = (log(abs(z))+j*(angleTaf+angleTfa+angleTfe+angleFP))
+    measZ = ((log(measAmp))+j*measAngle)
+    erreur = abs((ThZ-measZ)**2)
+
+    #version antérieure, pas bon car séparait la formule en deux
+    #errorAngle = (angleTaf + angleTfa + angleTfe + angleFP - measAngle) ** 2
+    #errorAbs = (log(abs(z)) - log(measAmp)) ** 2
+    #erreur = abs(errorAngle + errorAbs)
+
+    return erreur
+
+
+# defining the 'so-called' inverse problem to get the refractive index
+def inverseProblem(freq, fctTrans, thick, FP, init, limitUp):
+    """""" Calculates n and k over frequency.
+        freq : Frequency in Hz, must be POSITIVE;
+        fctTrans : Transfer function, must be same size as freq and correspond to positive frequencies;
+        thick : thickness of the sample in meters [m];
+        FP : If equal to 1 takes into account the Farby-Perot effect;
+        init : initial guess for the inverse problem. init should be an array : init = [3, 0.1], for example;
+        limitUp : High frequency limit of the study in Hz;
+    """"""
+    init = np.transpose([init.real, init.imag])
+    f = freq[(freq <= limitUp)]
+
+    ## The measurement
+    meas = fctTrans[(freq <= limitUp)]
+
+    ## Inverse problem
+    nInv = zeros(freq.shape[0])
+    kInv = zeros(freq.shape[0])
+
+    measAngleTh = unwrap(angle(meas))
+    measAbsTh = abs(meas)
+    for i in range(0, f.shape[0] - 1):
+        pouet = lambda x: errorFct(
+            x[0], x[1], thick, f[i], measAbsTh[i], measAngleTh[i], FP
+        )
+        res = minimize(pouet, init[i], method=""Nelder-Mead"", tol=1e-6)
+        # cf https://docs.scipy.org/doc/scipy/reference/optimize.html#module-scipy.optimize
+        # for other methods
+        nInv[i] = res.x[0]
+        kInv[i] = res.x[1]
+
+    ref_index = j * kInv + nInv
+
+    return ref_index
+
+def dielcal(mytransferfunction, z, myglobalparameters,FP ,scattering=None):
+    global zz, mytransferfunction2
+
+
+    """""" 
+    Takes variables from the the input and uses the inverseProblem
+    The first part is the initialization and the last line is the optimisation
+    """"""
+    mytransferfunction2 = np.copy(mytransferfunction)
+    zz = np.copy(z)
+    ref_index = np.ones(len(myglobalparameters.w))
+    myangleTF = np.unwrap(np.angle(mytransferfunction))
+
+    for compt in range(
+        0, 3
+    ):  # the max of this loop was choosen to optimize the effective index for a 250micron thick quartz substrate it gives good result at 1/1000 but shows weird convergeance (larger is not better so this may had to be changed with the sample)
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        myangleTrans = np.nan_to_num(np.angle(tt))  # ici il y a un truc a bien regarder
+        phase = np.unwrap(
+            myangleTF + myangleTrans
+        )  # we are taking the phase from the transfer function
+        
+        naprox = 1 + abs(
+            phase * c / zz / myglobalparameters.w
+        )  # this gives the real part of the index
+        
+        ref_index = naprox + j * (ref_index.imag)  # this gives the real part of the index
+        tt = 4 * ref_index / (1 + ref_index) ** 2
+        propa = np.exp(j * myglobalparameters.w * (ref_index) / c * zz)
+        kaprox = -np.nan_to_num(
+            c / z / myglobalparameters.w * np.log(abs(mytransferfunction) / (tt))
+        )
+        ref_index = j * kaprox.real + ref_index.real
+    ref_index[0] = 1
+
+    ref_index2 = inverseProblem(
+        myglobalparameters.w / 2 / np.pi, mytransferfunction2, zz, FP, ref_index, 6e12
+    )
+
+    return np.array(ref_index2 ** 2)
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/fit_TDSf.py",".py","26775","568","#!/usr/bin/python
+# -*- coding: latin-1 -*-
+
+## This two lines is to chose the econding
+# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math
+import pickle
+from pyswarm import pso   ## Library for optimization
+import random
+import numpy as np   ## Library to simplify the linear algebra calculations
+import scipy.optimize as optimize  ## Library for optimization
+import scipy.signal as scp # Library for signal processing
+import scipy.special as special
+import matplotlib.pyplot as plt ## Library for plotting results
+import traceback
+from scipy.optimize import curve_fit ## Library for optimization
+from epsillon3 import dielcal ## Library for resolving the inverse problem in our case (see the assumptions necessary to use this library)
+
+
+import warnings
+#warnings.filterwarnings(""ignore"") #this is just to remove the 'devided by zero' runtime worning for low frequency
+#we stricly advise to comment the above line as soon as you modify the code!
+
+
+
+###############################################################################
+###############################################################################
+import fit_TDSm as Model
+j = 1j
+c = 2.998e8
+h = 6.62607015E-34
+k= 1.38064852E-23
+###############################################################################
+# =============================================================================
+# External Python modules (serves for optimization algo #3)
+# =============================================================================
+## Parallelization that requieres mpi4py to be installed, if mpi4py was not installed successfully comment frome line 32 to line 40 (included)
+try:
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    myrank = comm.Get_rank()
+    size = comm.Get_size()
+except:
+    print(traceback.format_exc())
+    print('mpi4py is required for parallelization')
+    myrank=0
+
+
+#end
+# =============================================================================
+# Extension modules
+# =============================================================================
+
+try:
+    from pyOpt import Optimization   ## Library for optimization
+    from pyOpt import ALPSO  ## Library for optimization
+except:
+    print(traceback.format_exc())
+    print(""Error importing pyopt"")
+#from pyOpt import SLSQP  ## Library for optimization
+
+
+# =============================================================================
+# classes we will use
+# =============================================================================
+
+class globalparameters:
+   def __init__(self, t, freq,w):
+      self.t = t
+      self.freq = freq
+      self.w = w
+
+# =============================================================================
+
+class inputdatafromfile:
+   def __init__(self, path):
+      self.timeAndPulse = np.loadtxt(path) ## We load the data of the measured pulse
+      self.Pulseinit = self.timeAndPulse[:,1]
+      self.Spulseinit = (np.fft.rfft((self.Pulseinit)))  ## We compute the spectrum of the measured pulse
+
+# =============================================================================
+
+class mydata:
+   def __init__(self, pulse,refSpulse):
+      self.pulse = pulse # pulse with sample
+      self.Spulse = np.fft.rfft((pulse)) # spectral field with sample
+      self.mytransferfunction = np.fft.rfft((pulse))/refSpulse
+#      self.Spulse= fft_gpu((pulse))
+#      self.mytransferfunction = fft_gpu((pulse))/refSpulse
+      self.mynorm= np.linalg.norm(refSpulse)
+
+# =============================================================================
+
+class myfitdata:
+    def __init__(self, layers, delay_guess = 0, leftover_guess = np.zeros(2)):
+      global  myglobalparameters, myinputdata, myreferencedata, pathwithoutsample, pathwithsample
+      f=open(os.path.join(""temp"",'temp_file_1_ini.bin'),'rb')
+#      [myinputdata, myreferencedata, myglobalparameters, nsample, delaymax, mode] = pickle.load(f)
+      [pathwithoutsample,pathwithsample, freqWindow, timeWindow,  fitDelay, delaymax_guess, delay_limit, delayfixed, mode, fitLeftover, leftcoef_guess, leftcoef_limit, leftfixed]=pickle.load(f)
+      f.close()
+      
+      datawithsample=np.loadtxt(pathwithsample)    ## We load the signal of the measured pulse with sample
+      myreferencedata=inputdatafromfile(pathwithoutsample)
+      
+      myglobalparameters = globalparameters
+      myglobalparameters.t = myreferencedata.timeAndPulse[:,0]*1e-12 #this assumes input files are in ps ## We load the list with the time of the experiment
+      nsample = len(myglobalparameters.t)
+      dt = myglobalparameters.t.item(2)-myglobalparameters.t.item(1)   ## Sample rate
+      myglobalparameters.freq = np.fft.rfftfreq(nsample, dt)        ## We create a list with the frequencies for the spectrum
+      myglobalparameters.w = myglobalparameters.freq*2*np.pi
+      
+      myinputdata=mydata(datawithsample[:,1],myreferencedata.Spulseinit)    ## We create a variable containing the data related to the measured pulse with sample
+      if mode == ""superresolution"":
+        frep=99.991499600e6 # repetition frequency of the pulse laser used in the tds measurments in Hz, 99
+        nsampleZP=np.round(1/(frep*dt)) #number of time sample betwen two pulses. IT has to be noted that it could be better to have an integer number there then the rounding does not change much
+        nsamplenotreal=nsampleZP.astype(int)
+        myglobalparameters.t=np.arange(nsampleZP)*dt  # 0001 #
+        myglobalparameters.freq = np.fft.rfftfreq(nsamplenotreal, dt)
+        myglobalparameters.w = 2*np.pi*myglobalparameters.freq
+        
+        myreferencedata.Pulseinit=np.pad(myreferencedata.timeAndPulse[:,1],(0,nsamplenotreal-nsample),'constant',constant_values=(0))
+        myreferencedata.Spulseinit=(fft_gpu((myreferencedata.Pulseinit)))    # fft computed with GPU
+        
+        myinputdata=mydata(np.pad(datawithsample[:,1],(0,nsamplenotreal-nsample),'constant',constant_values=(0)),myreferencedata.Spulseinit)
+      # Filter data
+      myreferencedata.Spulseinit = myreferencedata.Spulseinit*freqWindow
+      myinputdata.Spulse         = myinputdata.Spulse*freqWindow
+      myreferencedata.Pulseinit  = np.fft.irfft(myreferencedata.Spulseinit, n = len(myreferencedata.Pulseinit))
+      myinputdata.pulse          = np.fft.irfft(myinputdata.Spulse, n = len(myinputdata.pulse))
+      
+      myreferencedata.Pulseinit = myreferencedata.Pulseinit*timeWindow
+      myreferencedata.Spulseinit = (np.fft.rfft((myreferencedata.Pulseinit)))
+      
+      self.mytransferfunction = layers.transferfunction(myglobalparameters.w,delay_guess,leftover_guess)
+      self.pulse = self.calculedpulse(layers,delay_guess,leftover_guess)
+      self.Spulse = (np.fft.rfft((self.pulse)))
+      self.epsilon = []
+      
+      optim_materials = []
+      for layer in layers.layers:
+          if layer.material.fit_material: #changer pour plrs fois meme mat
+              optim_materials.append(layer.material)
+      for mat in optim_materials:
+          self.epsilon.append(mat.epsilon(myglobalparameters.w))
+          
+    # =============================================================================
+    # function that returns the convolved pulse to the transfer function, it does it by different Drude model with one oscillator, n oscillators, etc
+    # =============================================================================
+    def calculedpulse(self,layers,delay_guess,leftover_guess):
+        global myinputdata,myreferencedata, myglobalparameters
+        Z = layers.transferfunction(myglobalparameters.w,delay_guess=delay_guess,leftover_guess=leftover_guess)
+        Spectrumtot=Z*myreferencedata.Spulseinit
+        Pdata=np.fft.irfft((np.array(Spectrumtot)), n = len(myreferencedata.Pulseinit))
+        return Pdata
+
+# =============================================================================
+# Classes for Materials and Layers
+# =============================================================================
+
+class Material:
+    materialCounter=0 #allow to add a unique id to each Material, so it can find the right variables
+    def __init__(self, name='', nbTerms = [], param=np.array([1]), down = None, up=None, file = None, fit_material = 0):
+        self.id=Material.materialCounter
+        Material.materialCounter+=1
+        # get parameters from file
+        if file:
+            f = open(file)
+            header = f.readline()
+            f.close()
+            header = header.split()
+            choices = dict(zip(header[1::2],header[2::2]))
+            name = choices.get('Name')
+            nbTerms = []
+            for model in Model.materialModels:
+                nbTerms.append(int(choices.get(model.name)))
+            param = np.loadtxt(file, dtype = np.float64)
+            #print(param)
+        # choices
+        self.name = name
+        if len(nbTerms) != 0:
+            self.nbTerms = nbTerms
+        else:
+            self.nbTerms = [0]*len(Model.materialModels)
+        if param.ndim == 0:
+            self.param = np.array([param])
+        else:
+            self.param = param
+        self.change_variables(self.nbTerms)
+        self.up = up
+        self.down = down
+        self.fit_material = fit_material
+        
+        self.header = ""Name {0}"".format(self.name.split('.')[0])
+        for i in range(len(Model.materialModels)):
+            self.header+="" {0} {1}"".format(Model.materialModels[i].name,self.nbTerms[i])
+        
+        if self.param is not None:    
+            self.eps_inf = self.variableDictionary.get(""epsilon_inf_{}"".format(self.id))
+    
+    def change_param(self,param, variables, down=None,up=None):
+        if param.ndim == 0:
+            self.param = np.array([param])
+        else:
+            self.param = param
+        if down is not None:
+            self.down = down
+        if up is not None:
+            self.up = up
+        if self.param is not None:
+            try:
+                self.variableDictionary = dict(zip(variables,self.param))
+                self.eps_inf = self.variableDictionary.get(""epsilon_inf_{}"".format(self.id))
+            except:
+                print(traceback.format_exc())
+                pass
+        
+    def change_variables(self, nbTerms):
+        self.nbTerms = nbTerms
+        self.myvariables=[""epsilon_inf_{}"".format(self.id)]
+        self.myunits=[""dimensionless""]
+        if self.name != '':
+            self.mydescriptions=[""{} : Permittivity at very high frequency\n"".format(self.name)]
+        else:
+            self.mydescriptions=[""Permittivity at very high frequency\n""]
+        for i in range(len(Model.materialModels)):
+            for j in range(nbTerms[i]):
+                for k in range(len(Model.materialModels[i].variableNames)):
+                    self.myvariables.append(""{0}_{1}_{2}_{3}"".format(Model.materialModels[i].variableNames[k],i,j,self.id)) #Adds all the index necessary to make sure the name is unique in the list and avoid conflict
+                    self.myunits.append(Model.materialModels[i].variableUnits[k])
+                    if Model.materialModels[i].isCumulative:
+                        if self.name != '':
+                            self.mydescriptions.append(""{0} : {1}{2}\n"".format(self.name,Model.materialModels[i].variableDescriptions[k],j))
+                        else:
+                            self.mydescriptions.append(""{0}{1}\n"".format(Model.materialModels[i].variableDescriptions[k],j))
+                    else:
+                        if self.name != '':
+                            self.mydescriptions.append(""{0} : {1}\n"".format(self.name,Model.materialModels[i].variableDescriptions[k]))
+                        else:
+                            self.mydescriptions.append(""{0}\n"".format(Model.materialModels[i].variableDescriptions[k]))
+        try:
+            self.variableDictionary = dict(zip(self.myvariables,self.param))
+            #print(self.variableDictionary)
+        except:
+            print(traceback.format_exc())
+            pass
+
+    def variableNames(self):
+        return self.myvariables
+    def variableUnits(self):
+        return self.myunits
+    def variableDescriptions(self):
+        return self.mydescriptions
+   
+    def epsilon(self, w):
+        eps = (self.eps_inf+0*1j)*np.ones(len(w))
+        
+        for i in range(len(Model.materialModels)):
+            for j in range(self.nbTerms[i]):
+                paramList=[self.variableDictionary.get(""{0}_{1}_{2}_{3}"".format(variableName,i,j,self.id)) for variableName in Model.materialModels[i].variableNames]
+                eps += Model.materialModels[i].epsilon(eps,w,paramList)
+        return eps
+    
+# =============================================================================
+
+class Layer:
+    def __init__(self,thickness,material,uncertainty=0,fit_index=1, id_fp=1):
+        self.thickness = thickness
+        self.uncertainty = uncertainty/100
+        self.fit_thickness = 1-fit_index # 1 (True) if Yes, 0 (False) is No
+        self.material = material # object of type Material
+        
+    def update_ini(self, other_layer):
+        self.thickness = other_layer.thickness
+        self.uncertainty = other_layer.uncertainty
+        self.fit_thickness = other_layer.fit_thickness
+        # update material unless both are material to optimize
+        if (self.material.fit_material == 0)|(other_layer.material.fit_material == 0): 
+            self.material = other_layer.material
+# =============================================================================
+
+class Interface:
+    interfaceCounter=0
+    def __init__(self, name = '', isMetasurface = 0, nbTerms = [],param=np.array([1]), down = None, up=None, file = None, fit_metasurface = 0):
+        self.id=Interface.interfaceCounter
+        Interface.interfaceCounter+=1
+        if file:                                                            #need to check if this need to change
+            param = np.loadtxt(file, dtype = np.float64)
+            f = open(file)
+            header = f.readline()
+            f.close
+            header = header.split()
+            choices = dict(zip(header[1::2],header[2::2]))
+            name = choices.get(""Name"")
+            isMetasurface = 1
+        self.name = name
+        self.isMetasurface = isMetasurface
+        if len(nbTerms) != 0:
+            self.nbTerms = nbTerms
+        else:
+            self.nbTerms = [0]*len(Model.interfaceModels)
+
+
+
+        self.down = down
+        self.up = up
+
+        if param.ndim == 0:
+            self.param = np.array([param])
+        else:
+            self.param = param
+            
+        self.change_variables(self.nbTerms)
+
+        self.fit_metasurface = fit_metasurface
+        
+        
+        self.header = ""Name {0}"".format(self.name.split('.')[0])
+        for i in range(len(Model.interfaceModels)):
+            self.header+="" {0} {1}"".format(Model.interfaceModels[i].name,self.nbTerms[i])
+        
+
+    def change_variables(self, nbTerms):
+        self.nbTerms = nbTerms
+        self.myvariableNames=[]
+        self.myunits=[]
+        self.mydescriptions=[]
+        for i in range(len(Model.interfaceModels)):
+            for j in range(nbTerms[i]):
+                for k in range(len(Model.interfaceModels[i].variableNames)):
+                    self.myvariableNames.append(""{0}_{1}_{2}_{3}"".format(Model.interfaceModels[i].variableNames[k],i,j,self.id)) #Adds all the index necessary to make sure the name is unique in the list and avoid conflict
+                    self.myunits.append(Model.interfaceModels[i].variableUnits[k])
+                    if self.name != '':
+                        self.mydescriptions.append(""{0} : {1}{2}\n"".format(self.name,Model.interfaceModels[i].variableDescriptions[k],j))
+                    else:
+                        self.mydescriptions.append(""{0}{1}\n"".format(Model.interfaceModels[i].variableDescriptions[k],j))
+        try:
+            self.variableDictionary = dict(zip(self.myvariableNames,self.param))
+        except:
+            print(traceback.format_exc())
+            pass
+
+    def change_param(self,param, variables, down=None,up=None):
+        if param.ndim == 0:
+            self.param = np.array([param])
+        else:
+            self.param = param
+        if down is not None:
+            self.down = down
+        if up is not None:
+            self.up = up
+        if self.param is not None:          
+            try:
+                self.variableDictionary = dict(zip(variables,self.param))
+            except:
+                print(traceback.format_exc())
+                pass
+    def variableNames(self):
+        return self.myvariableNames
+    def variableUnits(self):
+        return self.myunits
+    def variableDescriptions(self):
+        return self.mydescriptions
+
+
+# =============================================================================
+
+class Layers:
+    def __init__(self,layers = [], interfaces = []):
+        self.layers = layers # list of object of type Layer. size should be nlayers
+        self.nlayers = len(layers)
+        if interfaces == []:
+            self.interfaces = [Interface() for i in range(self.nlayers+1)]
+        else:
+            self.interfaces = interfaces # list of object of type Interface. size should be nlayers+1
+        self.names = []
+        for layer in self.layers:
+            self.names.append(layer.material.name)
+
+    def set_FP(self, id_fp):
+        self.index_FP = 1-id_fp # 1 (True) if Yes, 0 (False) is No
+
+    def update_ini(self,other_layers):
+        refresh = True
+        if self.names == other_layers.names:
+            refresh = False
+            other_layerlist = other_layers.layers
+            for i in range(self.nlayers):
+                self.layers[i].update_ini(other_layerlist[i])
+        else:
+            self.layers = other_layers.layers
+            self.interfaces = other_layers.interfaces
+            self.nlayers = len(self.layers)
+            self.names = []
+            for layer in self.layers:
+                self.names.append(layer.material.name)
+        return refresh
+        
+    
+    # transmission and reflexions coefficients depends on interfaces and adjacent layers
+    def coefficients(self,w):
+        self.refractive_indexes_with_air = np.ones([self.nlayers+2,len(w)],dtype = np.complex128)
+        self.refractive_indexes_with_air[1:self.nlayers+1] = [np.sqrt(self.layers[i].material.epsilon(w)) for i in range(self.nlayers)]
+        
+        self.rf = np.zeros([self.nlayers+1,len(w)],dtype = np.complex128) # forward reflexion
+        self.rb = np.zeros([self.nlayers+1,len(w)],dtype = np.complex128) # backward reflexion
+        self.tf = np.ones([self.nlayers+1,len(w)],dtype = np.complex128) # forward transmission
+        self.tb = np.ones([self.nlayers+1,len(w)],dtype = np.complex128) # forward transmission
+        n = self.refractive_indexes_with_air # to make the calculs easier to read over
+        
+        for i in range(self.nlayers+1):
+            self.rf[i] = (n[i+1]-n[i])/(n[i+1]+n[i])
+            self.rb[i] = -self.rf[i]
+            self.tf[i] = (2*n[i])/(n[i+1]+n[i])
+            self.tb[i] = (2*n[i+1])/(n[i+1]+n[i])
+            if self.interfaces[i].isMetasurface==1:
+                H=0
+                for k in range(len(Model.interfaceModels)):
+                    for j in range(self.interfaces[i].nbTerms[k]):
+                        paramList=[self.interfaces[i].variableDictionary.get(""{0}_{1}_{2}_{3}"".format(variableName,k,j,self.interfaces[i].id)) for variableName in Model.interfaceModels[k].variableNames]
+                        H+=Model.interfaceModels[k].H(w,paramList)
+                tf_t= self.tf[i]*(1-H)
+                rf_t= self.rf[i] - self.tb[i]*H
+                tb_t= self.tb[i]*(1-H)
+                rb_t= self.rb[i] + self.tf[i]*H
+                self.tf[i]=tf_t
+                self.tb[i]=tb_t
+                self.rf[i]=rf_t
+                self.rb[i]=rb_t
+            
+    def transferfunction(self,w,delay_guess = 0,leftover_guess = np.zeros(2)): #may have to be seriously mmodified for metasurfaces, as I've worked under the assumption that rij = -rji ans things like that <- done for 1 layer, to do for the othe cases
+        self.coefficients(w)
+        if self.nlayers == 1:
+            layer = self.layers[0]
+            thickn = layer.thickness
+            material = layer.material
+            ref_index = np.sqrt(material.epsilon(w))
+            
+#            t12=2/(1+ref_index)
+#            t21=2*ref_index/(1+ref_index)
+#            r22=(ref_index-1)/(1+ref_index)
+#            r22b=r22
+#            rr=r22*r22b
+#            tt=t12*t21
+
+#            self.coefficients(w)
+            ref_index = self.refractive_indexes_with_air[1]
+            rr = self.rb[0]*self.rf[1]      # ras rsa
+            tt = self.tf[0]*self.tf[1]      # tair->sample*tsample->air
+            
+            propa=np.exp(-j*w*(ref_index)*thickn/c)
+            propaair=np.exp(-j*w*thickn/c)
+
+            #Récupérer le contenu de File_FP
+            f=open(os.path.join(""temp"",'temp_file_FP.bin'),'rb')
+            id_FP = pickle.load(f)
+            f.close()
+            self.index_FP = 1 - id_FP # 1 (True) if Yes, 0 (False) is No
+
+            if self.index_FP == 1 :
+                FP=1/(1-rr*(propa**2))
+            else : 
+                FP=1
+            
+
+            coef = np.zeros(2)
+            for i in range(0,len(leftover_guess)):
+                coef[i] = leftover_guess[i]
+            wnorm = w*1e-12
+            leftnoise = (np.ones(len(wnorm))-(coef[0]*np.ones(len(wnorm))+coef[1]*(j*wnorm)**2)) 
+            Z = tt*propa*(FP/propaair)*np.exp(j*w*delay_guess)*leftnoise #delay
+            #print('Z')
+            #print(Z)
+            return Z
+            
+        # one should implement analytical solution for 2 layers for greater performance
+        
+        if self.nlayers == 3:         
+            d1 = self.layers[0].thickness
+            dS = self.layers[1].thickness
+            d2 = self.layers[2].thickness
+            
+            delta1 = j*w*self.refractive_indexes_with_air[1]*d1/c #[0] is air, like [4]
+            deltaS = j*w*self.refractive_indexes_with_air[2]*dS/c
+            delta2 = j*w*self.refractive_indexes_with_air[3]*d2/c
+            
+            tm1 = 1
+            tm2 = np.exp(-2*delta1)                 *self.rf[0]*self.rf[1] #rA1 r1S
+            tm3 = np.exp(-2*delta2)                 *self.rf[2]*self.rf[3] #rS2 r2A 
+            tm4 = np.exp(-2*delta1-2*delta2)        *self.rf[0]*self.rf[1]*self.rf[2]*self.rf[3] #rA1 r1S rS2 r2A
+            tm5 = np.exp(-2*deltaS-2*delta2)        *self.rf[1]*self.rf[3] #r1S r2A
+            tm6 = np.exp(-2*delta1-2*deltaS-2*delta2)*self.rf[0]*self.rf[3] #rA1 r2A
+            tm7 = np.exp(-2*deltaS)                 *self.rf[1]*self.rf[2] #r1S rS2
+            tm8 = np.exp(-2*delta1-2*deltaS)        *self.rf[0]*self.rf[2] #rA1 rS2
+            
+            propa = self.tf[0]*self.tf[1]*self.tf[2]*self.tf[3]*np.exp(-delta1-deltaS-delta2+j*w*(d1+dS+d2)/c) #propagation in air has been taken into account
+            
+            Z = propa/(tm1+tm2+tm3+tm4+tm5+tm6+tm7+tm8)*np.exp(j*w*delay_guess)
+# =============================================================================
+ #            tm1 = np.exp(+delta1+deltaS+delta2)
+ #            tm2 = np.exp(-delta1+deltaS+delta2)*self.rf[0]*self.rf[1] #rA1 r1S
+ #            tm3 = np.exp(+delta1+deltaS-delta2)*self.rf[2]*self.rf[3] #rS2 r2A
+ #            tm4 = np.exp(-delta1+deltaS-delta2)*self.rf[0]*self.rf[1]*self.rf[2]*self.rf[3] #rA1 r1S rS2 r2A
+ #            tm5 = np.exp(+delta1-deltaS-delta2)*self.rf[1]*self.rf[3] #r1S r2A
+ #            tm6 = np.exp(-delta1-deltaS-delta2)*self.rf[0]*self.rf[3] #rA1 r2A
+ #            tm7 = np.exp(+delta1-deltaS+delta2)*self.rf[1]*self.rf[2] #r1S rS2
+ #            tm8 = np.exp(-delta1-deltaS+delta2)*self.rf[0]*self.rf[2] #rA1 rS2           
+ #            Z = tt/(tm1+tm2+tm3+tm4+tm5+tm6+tm7+tm8)
+# =============================================================================
+            return Z
+
+        else: # General case, consider changing it to S-parameters for better convergence
+            tt = 1
+            d = 0
+            prop = 0
+            T = []
+            P = []
+            l = len(w)
+            for i in range(self.nlayers):
+                 prop = prop-j*w*self.refractive_indexes_with_air[i+1]*self.layers[i].thickness/c
+            for i in range(self.nlayers+1):
+                tt = tt*self.tf[i]
+                T.append(np.array([[np.ones(l),self.rf[i]],
+                                    [self.rf[i],np.ones(l)]]))
+            for i in range(self.nlayers):
+                d = d+self.layers[i].thickness
+                P.append(np.array([[np.ones(l), np.zeros(l)],
+                                    [np.zeros(l),np.exp(-2*j*w*self.refractive_indexes_with_air[i+1]*self.layers[i].thickness/c)]]))
+            M = T[0]
+            for i in range(self.nlayers):
+                M = multiply(M,P[i],l)
+                M = multiply(M,T[i+1],l)
+            Z = tt*np.exp(prop+j*w*d/c)/M[0][0] * np.exp(j*w*delay_guess)
+            return Z
+    
+def multiply(A,B,l):
+    "" Takes two 2*2*l matrix and returns 2*2*l matrix""
+    M = np.zeros([2,2,l],dtype = np.complex128)
+    M[0][0] = A[0][0]*B[0][0]+A[0][1]*B[1][0]
+    M[0][1] = A[0][0]*B[0][1]+A[0][1]*B[1][1]
+    M[1][0] = A[1][0]*B[0][0]+A[1][1]*B[1][0]
+    M[1][1] = A[1][0]*B[0][1]+A[1][1]*B[1][1]
+    return M
+
+# =============================================================================
+# Change that if the GPU is needed
+# =============================================================================
+def fft_gpu(y):
+#    global using_gpu
+#    if using_gpu==1:
+#        ygpu = cp.array(y)
+#        outgpu=cp.fft.rfft(ygpu)  # implied host->device
+#        out=outgpu.get()
+#    else:
+    out = np.fft.rfft(y)
+    return(out)
+
+def ifft_gpu(y):
+#    global using_gpu
+#    if using_gpu==1:  ## Only works if the number of elements before doing the fft is pair
+#        ygpu = cp.array(y)
+#        outgpu=cp.fft.irfft(ygpu)  # implied host->device
+#        out=outgpu.get()                            
+#    else:
+    out = np.fft.irfft(y)
+    return(out)
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/fit_TDSc.py",".py","48910","960","#!/usr/bin/python
+# -*- coding: latin-1 -*-
+
+## This two lines is to chose the econding
+# =============================================================================
+# Standard Python modules
+# =============================================================================
+import os, sys, time, math
+import pickle
+import subprocess
+from pyswarm import pso   ## Library for optimization
+import random
+import numpy as np   ## Library to simplify the linear algebra calculations
+import scipy.optimize as optimize  ## Library for optimization
+import matplotlib.pyplot as plt ## Library for plotting results
+from scipy.optimize import curve_fit ## Library for optimization
+from epsillon3 import dielcal ## Library for resolving the inverse problem in our case (see the assumptions necessary to use this library)
+from epsillonlayers8 import dielcal2
+import h5py
+from collections import Counter
+
+import fit_TDSf as TDS
+import fit_TDSm as Model
+
+import warnings
+#warnings.filterwarnings(""ignore"") #this is just to remove the 'devided by zero' runtime worning for low frequency
+#we stricly advise to comment the above line as soon as you modify the code!
+
+###############################################################################
+###############################################################################
+j = 1j
+c = 2.998e8
+###############################################################################
+# =============================================================================
+# External Python modules (serves for optimization algo #3)
+# =============================================================================
+## Parallelization that requieres mpi4py to be installed, if mpi4py was not installed successfully comment frome line 32 to line 40 (included)
+try:
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    myrank = comm.Get_rank()
+    size = comm.Get_size()
+except:
+    print('mpi4py is required for parallelization')
+    myrank=0
+
+
+#end
+# =============================================================================
+# Extension modules
+# =============================================================================
+
+class ControlerBase:
+    def __init__(self):
+        self.clients_tab0 = list()
+        self.clients_tab1 = list()
+        self.clients_tab2 = list()
+        self.clients_tab3 = list()
+        self.message = """"
+
+    def addClient0(self, client):
+        self.clients_tab0.append(client)
+    
+    def addClient(self, client):
+        self.clients_tab1.append(client)
+
+    def addClient2(self, client):
+        self.clients_tab2.append(client)
+
+    def addClient3(self, client):
+        self.clients_tab3.append(client)
+        
+    def refreshAll0(self, message):
+        self.message = message
+        for client in self.clients_tab0:
+            client.refresh()
+
+    def refreshAll(self, message):
+        self.message = message
+        for client in self.clients_tab1:
+            client.refresh()
+
+    def refreshAll2(self, message):
+        self.message = message
+        for client in self.clients_tab2:
+            client.refresh()
+
+    def refreshAll3(self, message):
+        self.message = message
+        for client in self.clients_tab3:
+            client.refresh()
+
+
+class Controler(ControlerBase):
+    def __init__(self):
+        super().__init__()
+        # Creation:
+        self.nbTerms0 = []
+        self.material0=None
+        self.nb_param0 = None
+        self.myvariables0 = None
+        self.mydescription0 = None
+        self.myunits0 = None
+        # Initialisation:
+        self.nlayers = None
+        self.nfixed_material = None
+        self.noptim_material = None
+        self.nfixed_metasurface = None
+        self.noptim_metasurface = None
+        self.materialList = []
+        self.materialNames = []
+        self.materials_param=[] 
+        self.mylayers=None
+        self.layerlist=None
+        self.distinctInterfaceList = []
+        self.interfaceList = []
+
+        #To take into consideration Fabry-Perot effect, or not, in dielcal functions
+        self.FP = []
+        self.nbpi= None
+        
+        self.epsilonTarget = None
+        
+        self.datawithsample=None    ## We load the signal of the measured pulse with sample
+        self.myreferencedata=TDS.inputdatafromfile
+        self.myreferencedata.timeAndPulse=None ## We load the data of the measured reference pulse
+        self.myreferencedata.Pulseinit=None
+        self.myreferencedata.Spulseinit=None  ## We compute the spectrum of the measured reference pulse
+        
+        self.myglobalparameters=TDS.globalparameters
+        self.myglobalparameters.t=None #this assumes input files are in ps ## We load the list with the time of the experiment
+        self.myglobalparameters.freq = None        ## We create a list with the frequencies for the spectrum
+        self.myglobalparameters.w=None
+        
+        self.nsample=None
+        self.dt=None   ## Sample rate
+        self.nb_param=None
+        self.myvariables=None
+        self.myunits=None
+        self.mydescription=None
+        self.mesparam=None
+        self.myfitteddata=None
+        self.previewdata=None
+        self.fictionaldata=None
+        self.errorIndex=0
+        self.normalisedWeight=None
+        self.normalisedNoise=None
+        self.noisematrix = None
+        
+        ## parameters for the optimization algorithm
+        self.swarmsize=1000
+        self.maxiter=20
+
+        # Variables for existence of temp Files
+        self.is_temp_file_0 = 0 # temporary file storing material parameters
+        self.is_temp_file_1 = 0 # temp file storing parameter choices
+        self.is_temp_file_2 = 0
+        self.is_temp_file_3 = 0 # temp file storing optimization results
+        self.is_temp_file_4 = 0
+        self.is_temp_file_5 = 0 # temp file storing algorithm choices
+
+        # Variable to see if initialisation is done
+        self.initialised = 0
+        
+# =============================================================================
+# Creation tab
+# =============================================================================
+    def invalid_n_model0(self,msg):
+        self.refreshAll0(msg + "" \n"")
+    def error_message_path0(self):
+        self.refreshAll0(""Error: Please enter a valid path."")
+    def ploting_text0(self,message):
+        self.refreshAll0(message)
+    def no_temp_file_0(self):
+        self.refreshAll0(""Unable to execute without running model choices first."")
+        
+    def material_parameters(self,nbTerms, isInterface):
+        """"""Creates variable list and save choices in temp file when a model of material is submitted""""""
+        self.nbTerms0 = nbTerms
+        if not isInterface:
+            self.material0 = TDS.Material(nbTerms = nbTerms)
+        else:
+            self.material0 = TDS.Interface(isMetasurface = 1, nbTerms = nbTerms)
+        self.myvariables0 = self.material0.variableNames()
+        self.myunits0 = self.material0.variableUnits()
+        self.mydescription0 = self.material0.variableDescriptions()
+        
+        self.nb_param0 = len(self.myvariables0)
+
+        # Save in a temporal file the model choices for the optimizatoin
+        mode_choicies_opt = self.nbTerms0
+
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+
+        f=open(os.path.join(""temp"",'temp_file_0.bin'),'wb')
+        pickle.dump(mode_choicies_opt,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.is_temp_file_0 = 1
+        self.refreshAll0("""")
+    
+        
+    def save_material_param(self,material,directory):
+        """"""save parameters values and model choices in a file to be used as fixed material/metasurface""""""
+        headerChoicies = material.header
+        np.savetxt(os.path.join(directory,material.name),material.param,header = headerChoicies)
+    
+    
+# =============================================================================
+# Initialisation tab
+# =============================================================================
+
+    def init(self):
+        self.refreshAll(""Initialisation: Ok"")
+        
+    def error_message_path(self):
+        self.refreshAll(""Error: Please enter a valid path."")
+
+    def loading_text(self):
+        self.refreshAll(""\n Processing... \n"")
+
+    def choices_ini(self,path_without_sample,path_with_sample,nlayers,nfixed_material,
+                    noptim_material,nfixed_metasurface, noptim_metasurface,Lfiltering_index, 
+                    Hfiltering_index, zeros_index, dark_index, cutstart, cutend,sharpcut, slope, intercept, fitDelay, delaymax_guess, delay_limit, delayfixed, modesuper, fitLeftover, leftcoef_guess, leftcoef_limit, leftfixed):
+        """"""Process all the informations given in the first panel of initialisation: 
+            create instances of classes to store data, apply filters, store choices in temp file 1_ini.
+            Note that data is reload in creation of class myfitdata and before optimization, 
+            any operations on data, like filters, has to be applied in the 3 cases.""""""
+        self.pathwithoutsample = path_without_sample
+        self.pathwithsample = path_with_sample
+        self.fitDelay = fitDelay
+        self.delaymax_guess = delaymax_guess
+        self.delay_limit = delay_limit
+        self.fitLeftover = fitLeftover
+        self.leftcoef_guess = leftcoef_guess
+        self.leftcoef_limit = leftcoef_limit
+        self.leftfixed = leftfixed
+        self.delayfixed = delayfixed
+
+        self.datawithsample = np.loadtxt(self.pathwithsample)    ## We load the signal of the measured pulse with sample
+        self.myreferencedata = TDS.inputdatafromfile(self.pathwithoutsample)
+        
+        self.myglobalparameters.t = self.myreferencedata.timeAndPulse[:,0]*1e-12 # this assumes input files are in ps ## We load the list with the time of the experiment
+        self.nsample = len(self.myglobalparameters.t)
+        self.dt=self.myglobalparameters.t.item(2)-self.myglobalparameters.t.item(1)  ## Sample rate
+        self.myglobalparameters.freq = np.fft.rfftfreq(self.nsample, self.dt)        ## We create a list with the frequencies for the spectrum
+        self.myglobalparameters.w = self.myglobalparameters.freq*2*np.pi
+        
+        self.myinputdata = TDS.mydata(self.datawithsample[:,1],self.myreferencedata.Spulseinit)    ## We create a variable containing the data related to the measured pulse with sample
+        
+        if modesuper == 1:
+            self.mode = ""superresolution""
+            frep=99.991499600e6 # repetition frequency of the pulse laser used in the tds measurments in Hz, 99
+            nsampleZP=np.round(1/(frep*self.dt)) #number of time sample betwen two pulses. IT has to be noted that it could be better to have an integer number there then the rounding does not change much
+            self.nsamplenotreal=nsampleZP.astype(int)
+            self.myglobalparameters.t=np.arange(nsampleZP)*self.dt  # 0001 #
+            self.myglobalparameters.freq = np.fft.rfftfreq(self.nsamplenotreal, self.dt)
+            self.myglobalparameters.w = 2*np.pi*self.myglobalparameters.freq
+            
+            self.myreferencedata.Pulseinit=np.pad(self.myreferencedata.timeAndPulse[:,1],(0,self.nsamplenotreal-self.nsample),'constant',constant_values=(0))
+            self.myreferencedata.Spulseinit=(TDS.fft_gpu((self.myreferencedata.Pulseinit)))    # fft computed with GPU
+
+            self.myinputdata=TDS.mydata(np.pad(self.datawithsample[:,1],(0,self.nsamplenotreal-self.nsample),'constant',constant_values=(0)),self.myreferencedata.Spulseinit)
+        else:
+            self.mode = ""basic""
+            self.nsamplenotreal = self.nsample
+        
+        # if one changes defaults values in TDSg this also has to change:
+        self.Lfiltering = Lfiltering_index # 1 - Lfiltering_index 
+        self.Hfiltering = Hfiltering_index # 1 - Hfiltering_index
+        self.set_to_zeros = zeros_index # 1 - zeros_index
+        self.dark_ramp = dark_index
+        
+        # Filter data
+        Freqwindowstart = np.ones(len(self.myglobalparameters.freq))
+        Freqwindowend = np.ones(len(self.myglobalparameters.freq))
+        if self.Lfiltering:
+            stepsmooth = cutstart/sharpcut
+            Freqwindowstart = 0.5+0.5*np.tanh((self.myglobalparameters.freq-cutstart)/stepsmooth)
+        if self.Hfiltering:
+            #cutend = comm.bcast(cutend,root=0)
+            #sharpcut = comm.bcast(sharpcut,root=0)
+            stepsmooth = cutend/sharpcut
+            Freqwindowend = 0.5-0.5*np.tanh((self.myglobalparameters.freq-cutend)/stepsmooth)
+        self.Freqwindow = Freqwindowstart*Freqwindowend
+        self.myreferencedata.Spulseinit = self.myreferencedata.Spulseinit*self.Freqwindow
+        self.myinputdata.Spulse         = self.myinputdata.Spulse        *self.Freqwindow
+        self.myreferencedata.Pulseinit  = np.fft.irfft(self.myreferencedata.Spulseinit, n = len(self.myreferencedata.Pulseinit))
+        self.myinputdata.pulse          = np.fft.irfft(self.myinputdata.Spulse, n = len(self.myinputdata.pulse ) )
+
+
+        self.timeWindow = np.ones(self.nsamplenotreal)
+        if self.dark_ramp:
+            #Enlève la rampe du dark noise du signal
+            #for k in range(self.nsample):
+            self.myreferencedata.Pulseinit = self.myreferencedata.Pulseinit - slope*self.myglobalparameters.t*1e12+intercept
+            self.myinputdata.pulse = self.myinputdata.pulse - slope*self.myglobalparameters.t*1e12+intercept
+            
+        if self.set_to_zeros:
+            #Remplace la fin du pulse de reference par des 0 (de la longueur du decalage entre les 2 pulses)
+            imax1 = np.argmax(self.myreferencedata.Pulseinit)
+            imax2 = np.argmax(self.myinputdata.pulse)
+            tmax1 = self.myglobalparameters.t[imax1]
+            tmax2 = self.myglobalparameters.t[imax2]
+            deltaTmax = tmax2-tmax1
+            
+            tlim1 = self.myglobalparameters.t[self.nsample-1]-(5*deltaTmax/4)
+            tlim2 = self.myglobalparameters.t[self.nsample-1]-(deltaTmax)
+            for k in range(self.nsample):
+                if self.myglobalparameters.t[k] < tlim1:
+                     self.timeWindow[k] = 1
+                elif self.myglobalparameters.t[k] >= tlim2:
+                     self.timeWindow[k] = 0
+                else:
+                     term = (4/deltaTmax)*(self.myglobalparameters.t[k]-tlim1)
+                     self.timeWindow[k] = 1-(3*term**2-2*term**3)
+            self.myreferencedata.Pulseinit = self.myreferencedata.Pulseinit*self.timeWindow
+            self.myreferencedata.Spulseinit = (np.fft.rfft((self.myreferencedata.Pulseinit)))
+        
+        self.nlayers = nlayers
+        self.nfixed_material = nfixed_material
+        self.noptim_material = noptim_material
+        self.nfixed_metasurface = nfixed_metasurface
+        self.noptim_metasurface = noptim_metasurface
+        
+        # files for choices made
+        mode_choicies_opt=[self.pathwithoutsample, self.pathwithsample, 
+                           self.Freqwindow,self.timeWindow,self.fitDelay, self.delaymax_guess, self.delay_limit, self.delayfixed, self.mode, self.fitLeftover, self.leftcoef_guess, self.leftcoef_limit, self.leftfixed]
+#        [self.myinputdata, self.myreferencedata, self.myglobalparameters, self.nsample, self.delaymax, self.mode]
+
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+        f=open(os.path.join(""temp"",'temp_file_1_ini.bin'),'wb')
+        pickle.dump(mode_choicies_opt,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+
+
+    def param_ini(self,layerlist,position_optim_material,position_optim_thickness,position_optim_interface,id_FP, nbpi):
+        """"""Process information given in last panel of Initialisation tab, updates temp file 2 if it already exists""""""
+        self.position_optim_material = position_optim_material
+        self.position_optim_thickness = position_optim_thickness
+        self.position_optim_interface = position_optim_interface
+        self.FP= id_FP
+        #Transmit the FP index in a file
+        f=open(os.path.join(""temp"",'temp_file_FP.bin'),'wb')
+        pickle.dump(id_FP,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+
+        
+        if self.layerlist == None:
+            self.layerlist = layerlist
+            self.mylayers = TDS.Layers(layerlist,self.interfaceList)
+            self.mylayers.set_FP(id_FP)
+            
+        else:
+            new_layers = TDS.Layers(layerlist,self.interfaceList)
+            new_layers.set_FP(id_FP)
+            refresh = self.mylayers.update_ini(new_layers)
+            self.layerlist = self.mylayers.layers
+            self.nlayers = len(self.layerlist)
+            if self.is_temp_file_2:
+                f=open(os.path.join(""temp"",'temp_file_2.bin'),'wb')
+                pickle.dump([self.position_optim_thickness,self.position_optim_material,
+                             self.position_optim_interface,self.mylayers,self.layerlist,
+                             self.interfaceList, self.mesparam],f,pickle.HIGHEST_PROTOCOL)
+                f.close()
+            if refresh:
+                self.is_temp_file_2 = 0
+                if self.nb_param:
+                    self.nb_param = 0
+        self.refreshAll2('')
+        
+        global c,j
+        if (self.nlayers == 1):
+            layer = self.layerlist[0]
+            thickness=layer.thickness
+
+            # calculating the delay to infer the index
+            #self.epsilonTarget=dielcal(self.myinputdata.mytransferfunction,thickness,self.myglobalparameters) ## We search for the dielectric function using what we measured
+            
+            self.epsilonTarget=dielcal(self.myinputdata.mytransferfunction,thickness,self.myglobalparameters,self.FP, nbpi) ## We search for the dielectric function using what we measured
+            self.deltaT=self.myglobalparameters.t[np.argmax(self.myinputdata.pulse)]-self.myglobalparameters.t[np.argmax(self.myreferencedata.Pulseinit)] #retard entre les deux max
+            self.deltaTTT=self.myglobalparameters.t[np.argmin(self.myinputdata.pulse)]-self.myglobalparameters.t[np.argmin(self.myreferencedata.Pulseinit)] ## retard entre les deux min
+            self.deltaTT=(np.sum(np.square(self.myinputdata.pulse)*self.myglobalparameters.t)/np.sum(np.square(self.myinputdata.pulse))-
+                          np.sum(np.square(self.myreferencedata.Pulseinit)*self.myglobalparameters.t)/np.sum(np.square(self.myreferencedata.Pulseinit)))   #retard entre les deux barycentre, attention pour que ca fonctionne il faut que le rapport signal bruit soit le meme dans les deux cas !!
+    
+            self.refreshAll(""Delay between the two maxima of the pulses:"")
+            self.refreshAll('delta T = {0}'.format(self.deltaT))
+            self.refreshAll('n = {0}'.format(1+self.deltaT*c/thickness)) #indice qui en derive
+            self.refreshAll('epsillon =  {0} \n'.format(np.square(1+self.deltaT*c/thickness))) #indice qui en derive
+    
+            self.refreshAll(""Delay between the two minima of the pulses:"")
+            self.refreshAll('delta T = {0}'.format(self.deltaTTT))
+            self.refreshAll('n = {0}'.format(1+self.deltaTTT*c/thickness)) #indice qui en derive
+            self.refreshAll('epsillon = {0} \n'.format(np.square(1+self.deltaTTT*c/thickness))) #indice qui en derive
+    
+            self.refreshAll(""Delay between the two energy barycenter of the pulses\n (beware that noise brings it to the middle for each one):"")
+            self.refreshAll('delta T= {0}'.format(self.deltaTT))
+            self.refreshAll('n = {0}'.format(self.deltaTT*c/thickness)) #indice qui en derive
+            self.refreshAll('epsillon = {0} \n'.format(np.square(self.deltaTT*c/thickness))) #indice qui en derive
+
+# =============================================================================
+# Model parameters
+# =============================================================================
+    def reset_values(self):
+        self.myvariables=[]
+        self.myunits=[]
+        self.mydescription=[]
+    
+                                    
+    def parameters_values(self,nbTerms):
+        """"""Creates variable list and save choices in temp file when model of material to optimize is submitted""""""
+        self.nbTerms=nbTerms
+        
+        if self.myvariables is None:
+            self.myvariables=[]
+            self.myunits=[]
+            self.mydescription=[]
+
+        j=0
+        for material in self.materialList:
+            if material.fit_material == 1:
+                material.change_variables(nbTerms[j])
+                self.myvariables = self.myvariables+material.variableNames()
+                self.myunits = self.myunits + material.variableUnits()
+                self.mydescription = self.mydescription + material.variableDescriptions()
+                j+=1
+        for interface in self.distinctInterfaceList:
+            if interface.fit_metasurface == 1:
+                interface.change_variables(nbTerms[j])
+                self.myvariables = self.myvariables+interface.variableNames()
+                self.myunits = self.myunits + interface.variableUnits()
+                self.mydescription = self.mydescription + interface.variableDescriptions()
+                j+=1
+        self.mylayers = TDS.Layers(self.layerlist,self.interfaceList)
+        self.nb_param = len(self.myvariables)
+        
+
+        # Save in a temporal file the model choices for the optimization
+        mode_choicies_opt=[self.myvariables, self.epsilonTarget]
+
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+
+        f=open(os.path.join(""temp"",'temp_file_1.bin'),'wb')
+        pickle.dump(mode_choicies_opt,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.is_temp_file_1 = 1
+        self.refreshAll2("""")
+    
+    def invalid_n_model(self, msg):
+        self.refreshAll2(msg + "" \n"")
+   
+    def invalid_swarmsize(self):
+        self.refreshAll3(""Invalid swarmsize. \n"")
+
+    def invalid_niter(self):
+        self.refreshAll3(""Invalid number of iterations. \n"")
+
+    def invalid_param(self):
+        self.refreshAll2(""Invalid parameters, try running the initialisation again. \n"")
+
+    def invalid_tun_opti_first(self):
+        self.refreshAll2(""Run the initialisation first. \n"")
+        
+    def error_message_path2(self):
+        self.refreshAll2(""Error: Please enter a valid path."")
+
+
+    def save_optimisation_param(self,mesparam):
+        """"""Stores values submitted""""""
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+        self.mesparam = mesparam
+        self.mylayers = TDS.Layers(self.layerlist,self.interfaceList)
+        f=open(os.path.join(""temp"",'temp_file_2.bin'),'wb')
+        pickle.dump([self.position_optim_thickness,self.position_optim_material,self.position_optim_interface, self.mylayers,self.layerlist,self.interfaceList, self.mesparam],f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.is_temp_file_2 = 1
+    
+    def save_optimisation_param_outside(self,mesparam,directory,name):
+        """"""Save values submitted in external file to reuse them later""""""
+        np.savetxt(os.path.join(directory,name),mesparam)
+        
+# =============================================================================
+# Optimization
+# =============================================================================
+        
+    def algo_parameters(self,choix_algo,swarmsize,niter,errorIndex,errorFile):
+        """"""Save algorithm choices in temp file 5""""""
+        self.algo=choix_algo
+        if errorIndex == 0:
+            errorFile = None
+        else:
+            errorweightdata = h5py.File(errorFile, 'r')
+            nameerr = list(errorweightdata.keys())[0]
+            self.noisematrix = list(errorweightdata[nameerr])
+            
+        mode_choicies_opt=[choix_algo,int(swarmsize),int(niter),errorIndex,errorFile]
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+
+        f=open(os.path.join(""temp"",'temp_file_5.bin'),'wb')
+        pickle.dump(mode_choicies_opt,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.is_temp_file_5 = 1
+
+        self.refreshAll3("""")
+        
+        
+    def begin_optimization(self,nb_proc):
+        """"""Run optimization and update layers""""""
+        output=""""
+        error=""""
+        returncode=0
+        if sys.platform==""win32"" or sys.platform==""cygwin"":
+            print(""OS:Windows \n"")
+            if not os.path.isdir(""temp""):
+                os.mkdir(""temp"")
+            optimization_filename = os.path.join('temp',""optimization.bat"")
+            try:
+                with open(optimization_filename, 'w') as OPATH:
+                   OPATH.writelines(['call set Path=%Path%;C:\ProgramData\Anaconda3 \n',
+                   'call set Path=%Path%;C:\ProgramData\Anaconda3\condabin \n',
+                   'call set Path=%Path%;C:\ProgramData\Anaconda3\Scripts \n',
+                   #'call conda activate \n',
+                   'call mpiexec -n {0} python optimization.py'.format(nb_proc)])
+#                    OPATH.writelines([f'call mpiexec -n {nb_proc} optimization.exe'])
+                subprocess.call(optimization_filename)
+                returncode = 0
+                error = """"
+                output = """"
+            except:
+                print(""No parallelization! You don't have MPI installed or there's a problem with your MPI."")
+                with open(optimization_filename, 'w') as OPATH:
+                    OPATH.writelines([f'call optimization.exe'])
+                subprocess.call(optimization_filename)
+                returncode = 0
+                error = """"
+                output = """"
+        elif sys.platform==""linux"" or sys.platform==""darwin"":
+            print(""OS:Linux/MacOS \n"")
+            optimization_filename = os.path.join('temp',""optimization.sh"")
+            try:
+                # Check if Open MPI is correctly installed
+                try:
+                    command = 'mpiexec --version'
+                    process=subprocess.Popen(command.split(),stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+                    output_mpi,error_mpi = process.communicate()
+                    returncode_mpi=process.returncode
+                except:
+                    returncode_mpi = 1
+                    error_mpi = ""Command mpiexec not recognized.""
+
+                try:
+                    command = './py3-env/bin/python --version'
+                    process=subprocess.Popen(command.split(),stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+                    output_py3,error_py3 = process.communicate()
+                    returncode_py3=process.returncode
+                    python_path = ""./py3-env/bin/python""
+                except:
+                    try:
+                        command = ""python3 --version""
+                        process=subprocess.Popen(command.split(),stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+                        output_py3,error_py3 = process.communicate()
+                        returncode_py3=process.returncode
+                        python_path = ""python3""
+                    except:
+                        returncode_py3 = 1
+                        error_py3 = ""Command python3 not recognized.""
+
+                # Run optimization
+                if returncode_mpi==0:
+                    if returncode_py3==0:
+                        # command = 'mpiexec -n {0} {1} optimization.py'.format(nb_proc, python_path)
+                        command = f'mpiexec.mpich -n {nb_proc} python optimization.py'
+                    else:
+                        print(""Problem with python command : \n {} \n"".format(error_py3))
+                        return(0)
+                else:
+                    print(""No parallelization! You don't have MPI installed or there's a problem with your MPI: \n {}"".format(error_mpi))
+                    if returncode_py3==0:
+                        command = '{0} optimization.py'.format(python_path)
+                    else:
+                        print(""Problem with python command : \n {} \n"".format(error_py3))
+                        return(0)
+
+                try:
+                    with open(optimization_filename, 'w') as OPATH:
+                        OPATH.writelines(command)
+                    returncode = subprocess.call('chmod +x ./{}'.format(optimization_filename),shell=True)
+                    if returncode == 0:
+                        print(f""subprocess ran"")
+                        returncode = subprocess.call(f'{optimization_filename}',shell=True)
+                        # returncode = subprocess.call('./{}'.format(optimization_filename),shell=True)
+                    if returncode == 1:
+                        command = """"
+                        command = 'import subprocess \ncommand = ""{0}"" \nprocess = subprocess.Popen(command.split(),stdout=subprocess.PIPE,stderr=subprocess.PIPE) \noutput,error = process.communicate() \nprint(""Output : "" + str(output) + ""\\n Error: "" + str(error) + ""\\n"")'.format(command)
+                        with open(""launch_optimization.py"", 'w') as OPATH:
+                            OPATH.writelines(command)
+                        try:
+                            import launch_optimization
+                            try:
+                                f=open(os.path.join(""temp"",'temp_file_3.bin'),'rb')
+                                f.close()
+                                returncode=0
+                            except:
+                                print(""Unknown problem."")
+                                sys.exit()
+                        except:
+                            print(""Unknown problem."")
+                            sys.exit()
+                except:
+                    returncode = 1
+                    error = ""Unknow problem.""
+                    output = """"
+            except:
+                print(""Unknown problem."")
+                sys.exit()
+
+        else:
+            print(""System not supported."")
+            return(0)
+
+        if returncode==0:
+            f=open(os.path.join(""temp"",'temp_file_3.bin'),'rb')
+            var_inter=pickle.load(f)
+            f.close()
+            self.is_temp_file_3 = 1
+            xopt=var_inter[0]
+            message=var_inter[1]
+            delay_guess = 0
+            leftover_guess = np.zeros(2)
+            if any(i!=0 for i in self.leftcoef_guess):
+                count=-1
+                for i in range (0,len(self.leftcoef_guess)):                                          
+                    count=count+1
+                    leftover_guess[i] = xopt[-len(self.leftcoef_guess)+count]
+            if self.delaymax_guess !=0:
+                if any(i!=0 for i in self.leftcoef_guess):
+                    delay_guess = xopt[-len(self.leftcoef_guess)-1]
+                else:
+                    delay_guess = xopt[-1]
+            for i in self.position_optim_material:
+                self.layerlist[i].material.change_param(xopt,self.myvariables)
+            for i in self.position_optim_interface:
+                self.interfaceList[i].change_param(xopt,self.myvariables)
+            for i, pos in enumerate(self.position_optim_thickness):
+                self.layerlist[pos].thickness = xopt[self.nb_param+i]
+            mylayers = TDS.Layers(self.layerlist,self.interfaceList)
+            self.myfitteddata=TDS.myfitdata(mylayers,delay_guess=delay_guess,leftover_guess=leftover_guess)
+            if self.errorIndex == 1:
+                try:
+                    weight = np.loadtxt(self.errorfile)
+                    weightnorm = np.linalg.norm(weight)/np.linalg.norm(np.ones(self.nsamplenotreal))
+                    self.normalisedWeight = weight/weightnorm
+                except:
+                    self.normalisedWeight = None
+            else:
+                self.normalisedWeight = None
+            self.refreshAll3(message)
+        else:
+            self.refreshAll3(""Output : \n {} \n"".format(output))
+            print(""System not supported. \n"")
+            print('Output : \n {0} \n Error : \n {1} \n'.format(output, error))
+            return(0)
+
+    def loading_text3(self):
+        self.refreshAll3(""\n Processing... \n"")
+
+    def message_log_tab3(self,message):
+        self.refreshAll3(message)
+
+    def error_message_path3(self):
+        self.refreshAll3(""Error: Please enter a valid path."")
+
+    def error_message_output_paths(self):
+        self.refreshAll3(""Invalid output paths."")
+
+    def error_message_output_filename(self):
+        self.refreshAll3(""Invalid output filename."")
+
+    def get_output_paths(self,outputdir,time_domain,frequency_domain,out_opt_filename):
+        """"""Check output names and path and stores them in temp file 4 if they are valid. """"""
+        try:
+            self.outputdir = str(outputdir)
+        except:
+            self.refreshAll3(""Invalid output directory."")
+            return(0)
+        try:
+            self.time_domain = str(time_domain)
+        except:
+            self.refreshAll3(""Invalid name for time domain output."")
+            return(0)
+        try:
+            self.frequency_domain = str(frequency_domain)
+        except:
+            self.refreshAll3(""Invalid name for frequency domain output."")
+            return(0)
+        try:
+            self.out_opt_filename = str(out_opt_filename)
+        except:
+            self.refreshAll3(""Invalid name for frequency domain output."")
+            return(0)
+        output_paths = [self.outputdir,self.time_domain,self.frequency_domain, self.out_opt_filename]
+        if not os.path.isdir(""temp""):
+            os.mkdir(""temp"")
+        f=open(os.path.join(""temp"",'temp_file_4.bin'),'wb')
+        pickle.dump(output_paths,f,pickle.HIGHEST_PROTOCOL)
+        f.close()
+        self.is_temp_file_4 = 1
+
+    def preview(self):
+        """"""Creates data that will be plotted""""""
+        if self.is_temp_file_2 == 1:
+            f=open(os.path.join(""temp"",'temp_file_2.bin'),'rb') # enables preview to work between optmization and construction and pparameters change
+            [position_optim_thickness, position_optim_material, position_optim_interface, mylayers,mylayerlist,myinterfacelist,mesparam]=pickle.load(f)
+            f.close()
+            self.previewdata=TDS.myfitdata(mylayers, self.delaymax_guess, self.leftcoef_guess)
+            self.myfitteddata = None # Sinon on ne peut plus faire de preview apres avoir optimise, cf definition de refresh dans TDSg. Il y aurait d'autres manière de le faire.
+            erreur = None
+            pulsenorm = np.linalg.norm(self.myinputdata.pulse)
+            input_reduced = self.myinputdata.pulse[:self.nsample] #input_reduced norm is equal to pulsenorm
+
+            if self.errorIndex == 0:
+                Z = mylayers.transferfunction(self.myglobalparameters.w, self.delaymax_guess, self.leftcoef_guess)
+                if self.mode == ""basic"":
+                    fit_pulse = np.fft.irfft(Z*self.myreferencedata.Spulseinit, n = len(self.myreferencedata.Pulseinit))
+                    erreur=np.linalg.norm(fit_pulse-self.myinputdata.pulse)/pulsenorm
+                else:
+                    Spectrumtot=Z*self.myreferencedata.Spulseinit
+                    pulse_theo=(np.fft.irfft((np.array(Spectrumtot)), n = len(self.myreferencedata.Pulseinit))) # calcul from calculedpulse. In fact it is the same calcul as in the basic mode for i!=0
+                    pulse_theo_reduced = pulse_theo[:self.nsample]
+                    erreur=np.linalg.norm(input_reduced-pulse_theo_reduced)/pulsenorm 
+            if self.errorIndex == 1:
+                try:
+                    weight = np.loadtxt(self.errorFile)
+                    try:
+                        if len(weight[0]) == 2: #in case there is time
+                            weight = weight[:,1]
+                    except:
+                        pass
+                    weightnorm = np.linalg.norm(weight)/np.linalg.norm(np.ones(self.nsamplenotreal))
+                    self.normalisedWeight = weight/weightnorm
+                    self.normalisedNoise = None
+                    self.noisematrix = None
+
+                    Z = mylayers.transferfunction(self.myglobalparameters.w, self.delaymax_guess, self.leftcoef_guess)
+                    if self.mode == ""basic"":
+                        fit_pulse = np.fft.irfft(Z*self.myreferencedata.Spulseinit, n = len(self.myreferencedata.Pulseinit))
+                        erreur=np.linalg.norm((fit_pulse-self.myinputdata.pulse)*self.normalisedWeight)/pulsenorm
+                    else:
+                        Spectrumtot=Z*self.myreferencedata.Spulseinit
+                        pulse_theo=np.fft.irfft((np.array(Spectrumtot)),n = len(self.myreferencedata.Pulseinit))
+                        pulse_theo_reduced = pulse_theo[:self.nsample]
+                        erreur=np.linalg.norm((input_reduced-pulse_theo_reduced)*self.normalisedWeight)/pulsenorm
+                except:
+                    self.normalisedWeight = None
+                    self.normalisedNoise = None
+                    self.noisematrix = None
+            elif self.errorIndex == 2:
+                try:
+                    noise = np.loadtxt(self.errorFile)
+                    try:
+                        if len(noise[0]) == 2: #in case there is time
+                            noise = noise[:,1]
+                    except:
+                        pass
+                    noisenorm = np.linalg.norm(noise)/np.linalg.norm(np.ones(self.nsamplenotreal))
+                    self.normalisedNoise = noise/noisenorm
+                    self.normalisedWeight = None
+                    self.noisematrix = None
+                    Z = mylayers.transferfunction(self.myglobalparameters.w, self.delaymax_guess, self.leftcoef_guess)
+                    if self.mode == ""basic"":
+                        fit_pulse = np.fft.irfft(Z*self.myreferencedata.Spulseinit, n = len(self.myreferencedata.Pulseinit))
+                        erreur=np.linalg.norm((fit_pulse-self.myinputdata.pulse)/self.normalisedNoise)/pulsenorm
+                    
+                except:
+                    self.normalisedWeight = None
+                    self.normalisedNoise = None
+                    self.noisematrix = None
+
+            elif self.errorIndex == 3:
+                try:
+                    noisedata = h5py.File(self.errorFile, 'r')
+                    name = list(noisedata.keys())[0]
+                    self.noisematrix = list(noisedata[name])
+                    if np.shape(self.noisematrix)[0] != self.nsample or np.shape(self.noisematrix)[1] != self.nsample:
+                        self.refreshAll3('Please enter a valid path. The file should be a {} square matrix'.format(self.nsample))
+                        return 0
+                    self.normalisedWeight = None
+                    self.normalisedNoise = None
+                    #noisematnorm = np.linalg.norm(self.noisematrix)/np.linalg.norm(np.ones(self.nsamplenotreal)) #TODO
+                    noisematnorm = np.linalg.norm(self.noisematrix)
+                    normalisednoisemat = self.noisematrix/noisematnorm
+                    Z = mylayers.transferfunction(self.myglobalparameters.w, self.delaymax_guess, self.leftcoef_guess)
+                    if self.mode == ""basic"":
+                        fit_pulse = np.fft.irfft(Z*self.myreferencedata.Spulseinit, n = len(self.myreferencedata.Pulseinit))
+                        Rtls = np.dot(normalisednoisemat,(self.myinputdata.pulse-fit_pulse))
+                        erreur = np.sqrt(np.dot(np.transpose(Rtls),Rtls))/pulsenorm
+
+                except Exception as e:
+                    print(e)
+                    self.normalisedWeight = None
+                    self.normalisedNoise = None
+                    self.noisematrix = None
+            else:
+                self.normalisedWeight = None
+                self.normalisedNoise = None
+                self.noisematrix = None
+            if erreur:
+            	self.refreshAll3(""The preview error is :"" + str(erreur))
+            	self.refreshAll3(""Done"")
+            else:
+            	self.refreshAll3(""Weight or Noise is missing to print error"")
+        else:
+            self.no_temp_file_2()
+        
+    def generateFictionalSample(self,tempstd=0,ampstd=0, name = 'fictionalsample',directory = None): #files
+        """"""Creates fictionnal sample (preview + noise)""""""
+        if self.is_temp_file_2 == 1:
+            f=open(os.path.join(""temp"",'temp_file_2.bin'),'rb')
+            [position_optim_thickness, position_optim_material, position_optim_interface, mylayers,mylayerlist,myinterfacelist,mesparam]=pickle.load(f)
+            f.close()
+            self.fictionaldata=TDS.myfitdata(self.mylayers)
+            # creation of data with gaussian noise
+            outputtime=np.column_stack(((self.myglobalparameters.t+[random.gauss(0,tempstd) for i in range(self.nsample)])*1e12,self.fictionaldata.pulse+[random.gauss(0,ampstd) for i in range(self.nsample)]))
+            if directory!=None:
+                np.savetxt(os.path.join(directory,'{}fiction'.format(name)),outputtime)
+            else:
+                np.savetxt('fictional{}'.format(name),outputtime)
+            self.refreshAll3(""Fictional sample saved"")
+        else:
+            self.no_temp_file_2()
+
+    def compute_eps_init(self, fileName = None):
+        """"""Computes epsilon according to the thicknesses given in initialization.""""""
+        if self.is_temp_file_2 == 1:
+            f=open(os.path.join(""temp"",'temp_file_2.bin'),'rb') # enables preview to work between optimization and construction and parameters change
+            [position_optim_thickness, position_optim_material, position_optim_interface, mylayers,mylayerlist,myinterfacelist,mesparam]=pickle.load(f)
+            f.close()
+        else:
+            self.no_temp_file_2()
+            return(0)
+        if self.nlayers == 1:
+            if self.initialised:
+                self.epsilonTarget=dielcal(self.myinputdata.mytransferfunction,
+                                           mylayerlist[0].thickness,self.myglobalparameters,self.FP, nbpi)
+                self.refreshAll3(""Done"")
+            else:
+                self.refreshAll3(""PLease run initialization first"")
+        elif (self.nlayers == 3)&(self.nfixed_material == 1)&(fileName is not None):
+            if self.is_temp_file_3 == 1:
+                self.refreshAll3(""Unable to run after optimization"")
+                return(0)
+            if self.initialised:
+                try:
+                    emptydata = np.loadtxt(fileName)
+                    myepsilondata = TDS.mydata(self.datawithsample[:,1],np.fft.rfft(emptydata[:,1]))
+                    data=dielcal2(myepsilondata.mytransferfunction,
+                                                mylayerlist[1].thickness,self.myglobalparameters,
+                                                mylayerlist[0].material.epsilon(self.myglobalparameters.w),
+                                                mylayerlist[0].thickness, mylayerlist[2].thickness, self.FP, nbpi)
+                    self.epsilonTarget = data[0]
+                    self.refreshAll3(""Done"")
+                except:
+                    self.refreshAll3(""Please enter a matching file"")
+            else:
+                self.refreshAll3(""PLease run initialization first"")
+        else:
+            self.refreshAll3(""Unable to compute for other than one layer or full cuvette"")
+    
+    def compute_eps_opti(self, fileName = None):
+        """"""Computes epsilon according to the thicknesses found in optimization, if they were optimization parameters""""""
+        if self.nlayers == 1:
+            if self.is_temp_file_3 == 1:
+                f=open(os.path.join(""temp"",'temp_file_3.bin'),'rb')
+                var_inter=pickle.load(f)
+                f.close()
+                xopt=var_inter[0]
+                if self.layerlist[0].fit_thickness:
+                    thickness = xopt[-1]
+                    self.epsilonTarget=dielcal(self.myinputdata.mytransferfunction,
+                                                 thickness,self.myglobalparameters,self.FP, nbpi)
+                    self.refreshAll3(""Done"")
+                else:
+                    self.refreshAll3(""Thickness was not used as a variable in the last optimization."")
+                
+            else:
+                self.refreshAll3(""Please run optimization first"")
+        elif (self.nlayers == 3)&(self.nfixed_material == 1)&(fileName is not None):
+            if self.is_temp_file_3 == 1:
+                try:
+                    emptydata = np.loadtxt(fileName)
+                    myepsilondata = TDS.mydata(self.datawithsample[:,1],np.fft.rfft(emptydata[:,1]))
+                    data=dielcal2(myepsilondata.mytransferfunction,
+                                                self.layerlist[1].thickness,self.myglobalparameters,
+                                                self.layerlist[0].material.epsilon(self.myglobalparameters.w),
+                                                self.layerlist[0].thickness, self.layerlist[2].thickness,self.FP, nbpi)
+                    self.epsilonTarget = data[0]
+                    self.refreshAll3(""Done"")
+                except:
+                    self.refreshAll3(""Please enter a matching file"")
+        else:
+            self.refreshAll3(""Unable to compute for other than one layer"")
+    def compute_eps_phase_corraction(self, fileName=None):
+        if self.is_temp_file_2 == 1:
+            f=open(os.path.join(""temp"",'temp_file_2.bin'),'rb') # enables preview to work between optimization and construction and parameters change
+            [position_optim_thickness, position_optim_material, position_optim_interface, mylayers,mylayerlist,myinterfacelist,mesparam]=pickle.load(f)
+            f.close()
+        else:
+            self.no_temp_file_2()
+            return(0)
+        if self.nlayers == 1:
+            if self.initialised:
+                f=open(os.path.join(""temp"",'temp_file_phase.bin'),'rb')
+                nbpi= pickle.load(f)
+                f.close()
+                print(""phase2:"", nbpi)
+                self.epsilonTarget=dielcal(self.myinputdata.mytransferfunction,
+                                           mylayerlist[0].thickness,self.myglobalparameters,self.FP, nbpi)
+                self.refreshAll3(""Done"")
+            else:
+                self.refreshAll3(""PLease run initialization first"")
+        elif (self.nlayers == 3)&(self.nfixed_material == 1)&(fileName is not None):
+            if self.is_temp_file_3 == 1:
+                self.refreshAll3(""Unable to run after optimization"")
+                return(0)
+            if self.initialised:
+                try:
+                    emptydata = np.loadtxt(fileName)
+                    myepsilondata = TDS.mydata(self.datawithsample[:,1],np.fft.rfft(emptydata[:,1]))
+                    data=dielcal2(myepsilondata.mytransferfunction,
+                                                mylayerlist[1].thickness,self.myglobalparameters,
+                                                mylayerlist[0].material.epsilon(self.myglobalparameters.w),
+                                                mylayerlist[0].thickness, mylayerlist[2].thickness, self.FP, nbpi)
+                    self.epsilonTarget = data[0]
+                    self.refreshAll3(""Done"")
+                except:
+                    self.refreshAll3(""Please enter a matching file"")
+            else:
+                self.refreshAll3(""PLease run initialization first"")
+        else:
+            self.refreshAll3(""Unable to compute for other than one layer or full cuvette"")
+
+    def ploting_text3(self,message):
+        self.refreshAll3(message)
+        
+    def no_temp_file_1(self):
+        self.refreshAll3(""Unable to execute without running step Initialization and model choices first."")
+
+    def no_temp_file_2(self):
+        self.refreshAll3(""Unable to execute without running step 'model parameters' window first."")
+
+    def no_temp_file_4(self):
+        self.refreshAll3(""Unable to execute without selecting path for output data first."")
+    
+    def no_temp_file_5(self):
+        self.refreshAll3(""Unable to execute without optimization parameters"")
+","Python"
+"Metamaterial","THzbiophotonics/Fit-TDS","fit_TDS/csts.py",".py","14","1","FileName = f""""","Python"