[WIP] Error takes sprintf like arguments directly.

Ref. !2233

license.txt

@@ -151,7 +151,7 @@ Comment: The original author gave authorization to change
 
 Files: matlab/+pruned_SS/prodmom.m matlab/+pruned_SS/bivmom.m
 Copyright: 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
-           2019-2020 Dynare Team
+           2019-2023 Dynare Team
 License: GPL-3+
 Comment: The author gave authorization to redistribute
  these functions under GPL-3+ with Dynare and would 
@@ -242,7 +242,7 @@ License: BSD-2-clause
 
 Files: matlab/utilities/graphics/colorspace.m
 Copyright: 2005-2010 Pascal Getreuer
-           2017 Dynare Team
+           2017-2023 Dynare Team
 License: BSD-2-clause
 
 Files: examples/fs2000_data.m

matlab/+bvar/forecast.m

@@ -154,8 +154,8 @@ end
 % Store results
 
 DirectoryName = [ M_.dname '/bvar_forecast' ];
-if ~isdir(DirectoryName)
-    if ~isdir(M_.dname)
+if ~isfolder(DirectoryName)
+    if ~isfolder(M_.dname)
         mkdir(M_.dname);
     end
     mkdir(DirectoryName);

matlab/+bvar/graph_init.m

@@ -1,5 +1,5 @@
-function dyn_graph=dynare_graph_init(figure_name,nplot,line_types,line_width)
-% function dynare_graph_init(figure_name,colors)
+function dyn_graph=graph_init(figure_name,nplot,line_types,line_width)
+% function graph_init(figure_name,colors)
 % initializes set of graphs
 %
 % INPUTS:
@@ -12,7 +12,7 @@ function dyn_graph=dynare_graph_init(figure_name,nplot,line_types,line_width)
 % SPECIAL REQUIREMENT
 %   none
 
-% Copyright © 2006-2017 Dynare Team
+% Copyright © 2006-2023 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/+bvar/irf.m

@@ -130,7 +130,7 @@ end
 
 % Save intermediate results
 DirectoryName = [ M_.dname '/bvar_irf' ];
-if ~isdir(DirectoryName)
+if ~isfolder(DirectoryName)
     mkdir('.',DirectoryName);
 end
 save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
@@ -146,4 +146,4 @@ for i=1:ny
         oo_.bvar.irf.Upper_bound.(variable_name).(shock_name) = posterior_up_conf_irfs(j,i,:);
         oo_.bvar.irf.Lower_bound.(variable_name).(shock_name) = posterior_down_conf_irfs(j,i,:);
     end
-end
+end

matlab/+bvar/toolbox.m

@@ -1,5 +1,5 @@
-function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
-%function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
+function [ny, nx, posterior, prior, forecast_data] = toolbox(nlags)
+%function [ny, nx, posterior, prior, forecast_data] = toolbox(nlags)
 % bvar_toolbox  Routines shared between BVAR methods
 % Computes several things for the estimations of a BVAR(nlags)
 %
@@ -42,7 +42,7 @@ function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
 %    - bvar_prior_{tau,decay,lambda,mu,omega,flat,train}
 
 % Copyright © 2003-2007 Christopher Sims
-% Copyright © 2007-2017 Dynare Team
+% Copyright © 2007-2023 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/+identification/bruteforce.m

@@ -1,5 +1,5 @@
-function [pars, cosnJ] = ident_bruteforce(dname,fname,J, max_dim_cova_group, TeX, name_tex, tittxt, tol_deriv)
-% [pars, cosnJ] = ident_bruteforce(dname,fname,J, max_dim_cova_group, TeX, name_tex, tittxt, tol_deriv)
+function [pars, cosnJ] = bruteforce(dname,fname,J, max_dim_cova_group, TeX, name_tex, tittxt, tol_deriv)
+% [pars, cosnJ] = bruteforce(dname,fname,J, max_dim_cova_group, TeX, name_tex, tittxt, tol_deriv)
 % -------------------------------------------------------------------------
 % given the Jacobian matrix J of moment derivatives w.r.t. parameters
 % computes, for  each column of J, the groups of columns from 1 to n that

matlab/+pruned_SS/allVL1.m

@@ -78,14 +78,14 @@ end
 Counter(0);
 
 switch L1ops
-    case {'==' '='},
+    case {'==' '='}
         if isnan(MaxCounter)
             % return the number of solutions
             v = nchoosek(n+L1-1,L1); % nchoosek(n+L1-1,n-1)
         else
             v = allVL1eq(n, L1);
         end
-    case '<=', % call allVL1eq for various sum targets
+    case '<=' % call allVL1eq for various sum targets
         if isnan(MaxCounter)
             % return the number of solutions
             %v = nchoosek(n+L1,L1)*factorial(n-L1); BUG <- 16/Sep/2009: 
@@ -99,7 +99,7 @@ switch L1ops
             v = cell2mat(arrayfun(@(j) allVL1eq(n, j), (0:L1)', ...
                          'UniformOutput', false));
         end
-    case '<',
+    case '<'
         v = allVL1(n, L1-1, '<=', MaxCounter);
     otherwise
         error('allVL1: unknown L1ops')

matlab/+pruned_SS/prodmom.m

@@ -21,7 +21,7 @@
 % Multivariate Analysis, 2008, vol. 99, issue 3, pages 542-554.
 % =========================================================================
 % Copyright © 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
-% Copyright © 2019-2020 Dynare Team
+% Copyright © 2019-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -38,7 +38,7 @@
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 % =========================================================================
-function y = prodmom(V,ii,nu);
+function y = prodmom(V,ii,nu)
 if nargin<3
    nu = ones(size(ii));
 end
@@ -104,4 +104,4 @@ for i=1:fix(prod(nu+1)/2)
         end
     end
 end
-y = y/prod([1:s2]);
+y = y/prod([1:s2]);

matlab/CheckPath.m

@@ -33,7 +33,7 @@ info = 0;
 
 DirectoryName = [ dname '/' type ]; % don't use filesep as it will create issues with LaTeX on Windows
 
-if ~isdir(dname)
+if ~isfolder(dname)
     % Make sure there isn't a file with the same name, see trac ticket #47
     if isfile(dname)
         delete(dname)
@@ -41,7 +41,7 @@ if ~isdir(dname)
     mkdir('.', dname);
 end
 
-if ~isdir(DirectoryName)
+if ~isfolder(DirectoryName)
     % Make sure there isn't a file with the same name, see trac ticket #47
     if isfile(DirectoryName)
         delete(DirectoryName)

matlab/MakeAllFigures.m

@@ -56,7 +56,8 @@ elseif NumberOfPlots == 1
 end
 
 for plt = 1:NumberOfPlots
-    eval(['NumberOfCurves = Info.Box' int2str(plt) '.Number;'])
+    field1 = sprintf('Box%u', plt);
+    NumberOfCurves = Info.(field1).Number;
     NumberOfObservations = zeros(2,1);
     x = cell(NumberOfCurves,1);
     y = cell(NumberOfCurves,1);
@@ -66,10 +67,11 @@ for plt = 1:NumberOfPlots
     binf = NaN(NumberOfCurves,1);
     bsup = NaN(NumberOfCurves,1);
     for curve = 1:NumberOfCurves
-        eval(['x{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.xdata;'])
-        eval(['y{' curve '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.ydata;'])
-        eval(['name = Info.Box' int2str(plt) '.Curve' int2str(curve) '.variablename;'])
-        eval(['PltType{' curve  '} = Info.Box' int2str(plt) '.Curve' int2str(curve) '.type']);
+        field2 = sprintf('Curve%u', curve);
+        x{curve} = Info.(field1).(field2).xdata;
+        y{curve} = Info.(field1).(field2).ydata;
+        name = Info.(field1).(field2).variablename;
+        PltType{curve} = Info.(field1).(field2).type;
         if length(x{curve})-length(y{curve})
             disp('MakeFigure :: The number of observations in x doesn''t match with ')
             disp(['the number of observation in y for ' name ])
@@ -90,9 +92,9 @@ for plt = 1:NumberOfPlots
     if isnan(ymin(plt))
         ymin = 0;
     end
-    eval(['NAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.name;'])
+    NAMES{plt} = Info.(field1).name;
     if options_.TeX
-        eval(['TeXNAMES{' int2str(plt) '} = Info.Box' int2str(plt) '.texname;'])
+        TeXNAMES{plt} = Info.(field1).texname;
     end
     subplot(nr,nc,plt)
     hold on
@@ -148,22 +150,24 @@ end
 
 if Info.SaveFormat.Eps
     if isempty(Info.SaveFormat.Name)
-        eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.eps']);
+        print(sprintf('%s%s%u', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Number), '-depsc2')
     else
-        eval(['print -depsc2 ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.eps']);
+        print(sprintf('%s%s%s', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Name), '-depsc2')
     end
 end
+
 if Info.SaveFormat.Pdf && ~isoctave
     if isempty(Info.SaveFormat.Name)
-        eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
+        print(sprintf('%s%s%u', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Number), '-dpdf')
     else
-        eval(['print -dpdf ' M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
+        print(sprintf('%s%s%s', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Name), '-dpdf')
     end
 end
+
 if Info.SaveFormat.Fig && ~isoctave
     if isempty(Info.SaveFormat.Name)
-        saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) '.fig']);
+        saveas(FigHandle, sprintf('%s%s%u.fig', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Number));
     else
-        saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name '.fig']);
+        saveas(FigHandle, sprintf('%s%s%s.fig', M_.fname, Info.SaveFormat.GenericName, Info.SaveFormat.Name));
     end
-end
+end

matlab/accessors/get_param_by_name.m

@@ -33,7 +33,7 @@ global M_
 i = strmatch(pname,M_.param_names,'exact');
 
 if isempty(i)
-    error(sprintf('Can''t find parameter %s', pname))
+    error('Can''t find parameter %s', pname)
 end
 
 x = M_.params(i);

matlab/check_list_of_variables.m

@@ -41,7 +41,7 @@ if options_.dsge_var && options_.bayesian_irf
         for i=1:size(varlist,1)
             idx = strmatch(varlist{i}, options_.varobs, 'exact');
             if isempty(idx)
-                disp(sprintf('%s is not an observed variable!', varlist{i}))
+                dprintf('%s is not an observed variable!', varlist{i})
                 msg = true;
             end
         end
@@ -169,4 +169,4 @@ while ~isempty(remain)
 end
 if index<max_number_of_words_per_line
     disp(line_of_text)
-end
+end

matlab/convergence_diagnostics/geweke_moments.m

@@ -103,6 +103,6 @@ for taper_index=1:length(taper_steps)
     results_vec(1,4+taper_index*2-1)=NSE_taper;
     results_vec(1,4+taper_index*2)=total_variance/(n_draws_used*NSE_taper^2);
 
-    eval(['results_struct.nse_taper_',num2str(taper),'= NSE_taper;']);
-    eval(['results_struct.rne_taper_',num2str(taper),'= total_variance/(n_draws_used*NSE_taper^2);']);
+    results_struct.(sprintf('nse_taper_%u', taper)) = NSE_taper;
+    results_struct.(sprintf('rne_taper_%u', taper)) = total_variance/(n_draws_used*NSE_taper^2);
 end % end of for mm loop

matlab/discretionary_policy/discretionary_policy_engine.m

@@ -48,7 +48,7 @@ function [H,G,retcode]=discretionary_policy_engine(AAlag,AA0,AAlead,BB,bigw,inst
 %  Dennis, Richard (2007): Optimal policy in rational expectations models: new solution algorithms,
 %       Macroeconomic Dynamics, 11, 31–55.
 
-% Copyright © 2007-2018 Dynare Team
+% Copyright © 2007-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -189,7 +189,7 @@ function [rcode,NQ]=CheckConvergence(Q,iter,MaxIter,crit)
 NQ=max(max(abs(Q)));% norm(Q); seems too costly
 if isnan(NQ)
     rcode=3;
-elseif iter>MaxIter;
+elseif iter>MaxIter
     rcode=2;
 elseif NQ<crit
     rcode=1;

matlab/distributions/weibull_specification.m

@@ -94,7 +94,7 @@ try
    for i=1:n_shapes
        for j=1:n_scales
           if debug
-              disp(sprintf('... mu=%s and s2=%s', num2str(mu(j,i)),num2str(s2(j,i))))
+              dprintf('... mu=%s and s2=%s', num2str(mu(j,i)),num2str(s2(j,i)))
           end
           if ~isnan(mu(j,i)) && ~isnan(s2(j,i)) && ~isinf(mu(j,i)) && ~isinf(s2(j,i))
               [shape, scale] = weibull_specification(mu(j,i), s2(j,i));

matlab/dynare_config.m

@@ -47,7 +47,6 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
      '/AIM/' ; ...
      '/backward/' ; ...
      '/cli/' ; ...
-     '/conditional_forecasts/'; ...
      '/convergence_diagnostics/' ; ...
      '/discretionary_policy/' ; ...
      '/distributions/' ; ...
@@ -55,7 +54,6 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
      '/estimation/'; ...
      '/estimation/smc/'; ...
      '/estimation/resampler/'; ...
-     '/gsa/' ; ...
      '/kalman/' ; ...
      '/kalman/likelihood' ; ...
      '/latex/' ; ...

matlab/ep/extended_path_homotopy.m

@@ -54,7 +54,7 @@ if ismember(method, [1, 2])
             flag = ~flag;
         end
         if debug
-            disp(sprintf('%s\t %1.8f\t %s',int2str(iteration),weight,int2str(flag)))
+            dprintf('%u\t %1.8f\t %u', iteration, weight, flag)
         end
         state(2:end) = state(1:end-1);
         state(1) = flag;
@@ -121,7 +121,7 @@ if isequal(method, 3) || (isequal(method, 2) && noconvergence)
             flag = ~flag;
         end
         if debug
-            disp(sprintf('%s\t %1.8f\t %s',int2str(index),weight,int2str(flag)))
+            dprintf('%u\t %1.8f\t %u', index, weight, flag)
         end
         if flag
             jndex = index;

matlab/estimation/GetPosteriorParametersStatistics.m

@@ -119,11 +119,11 @@ if np
                 oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
             end
         end
-        disp(sprintf(pformat, header_width, name, bayestopt_.p1(ip),...
-                     post_mean, ...
-                     hpd_interval, ...
-                     pnames{bayestopt_.pshape(ip)+1}, ...
-                     bayestopt_.p2(ip)));
+        dprintf(pformat, header_width, name, bayestopt_.p1(ip),...
+                post_mean, ...
+                hpd_interval, ...
+                pnames{bayestopt_.pshape(ip)+1}, ...
+                bayestopt_.p2(ip));
         if TeX
             k = estim_params_.param_vals(i,1);
             name = M_.param_names_tex{k};
@@ -167,7 +167,7 @@ if nvx
                 M_.Sigma_e(k,k) = post_mean*post_mean;
             end
         end
-        disp(sprintf(pformat,header_width,name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip)));
+        dprintf(pformat, header_width, name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
         if TeX
             name = M_.exo_names_tex{k};
             TeXCore(fid,name, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p1(ip), bayestopt_.p2(ip), post_mean, sqrt(post_var), hpd_interval);
@@ -205,7 +205,7 @@ if nvn
                 oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
             end
         end
-        disp(sprintf(pformat, header_width, name,bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip)));
+        dprintf(pformat, header_width, name,bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
         if TeX
             k = estim_params_.var_endo(i,1);
             name = M_.endo_names_tex{k};
@@ -257,7 +257,7 @@ if ncx
                 M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
             end
         end
-        disp(sprintf(pformat, header_width,name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip)));
+        dprintf(pformat, header_width,name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
         if TeX
             name = sprintf('(%s,%s)', M_.exo_names_tex{k1}, M_.exo_names_tex{k2});
             TeXCore(fid, name, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p1(ip), bayestopt_.p2(ip), post_mean, sqrt(post_var), hpd_interval);
@@ -304,7 +304,7 @@ if ncn
                 oo_ = Filloo(oo_, NAME, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
             end
         end
-        disp(sprintf(pformat, header_width, name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip)));
+        dprintf(pformat, header_width, name, bayestopt_.p1(ip), post_mean, hpd_interval, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
         if TeX
             name = sprintf('(%s,%s)', M_.endo_names_tex{k1}, M_.endo_names_tex{k2});
             TeXCore(fid, name, pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p1(ip), bayestopt_.p2(ip), post_mean, sqrt(post_var), hpd_interval);

matlab/estimation/display_estimation_results_table.m

@@ -72,8 +72,8 @@ if np
             fprintf('%-*s %10.4f %7.4f %7.4f \n', ...
                     header_width, name, xparam1(ip), stdh(ip), tstath(ip));
         end
-        eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
-        eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
+        oo_.(sprintf('%s_mode', field_name)).parameters.(name) = xparam1(ip);
+        oo_.(sprintf('%s_std_at_mode', field_name)).parameters.(name) = stdh(ip);
         ip = ip+1;
     end
     skipline()
@@ -94,8 +94,8 @@ if nvx
             fprintf('%-*s %10.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), stdh(ip), tstath(ip));
         end
         M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
-        eval(['oo_.' field_name '_mode.shocks_std.' name ' = xparam1(ip);']);
-        eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
+        oo_.(sprintf('%s_mode', field_name)).shocks_std.(name) = xparam1(ip);
+        oo_.(sprintf('%s_std_at_mode', field_name)).shocks_std.(name) = stdh(ip);
         ip = ip+1;
     end
     skipline()
@@ -116,8 +116,8 @@ if nvn
             fprintf('%-*s %10.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), ...
                     stdh(ip), tstath(ip))
         end
-        eval(['oo_.' field_name '_mode.measurement_errors_std.' name ' = xparam1(ip);']);
-        eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
+        oo_.(sprintf('%s_mode', field_name)).measurement_errors_std.(name) = xparam1(ip);
+        oo_.(sprintf('%s_std_at_mode', field_name)).measurement_errors_std.(name) = stdh(ip);
         ip = ip+1;
     end
     skipline()
@@ -142,8 +142,8 @@ if ncx
         end
         M_.Sigma_e(k1,k2) = xparam1(ip)*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
         M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
-        eval(['oo_.' field_name '_mode.shocks_corr.' NAME ' = xparam1(ip);']);
-        eval(['oo_.' field_name '_std_at_mode.shocks_corr.' NAME ' = stdh(ip);']);
+        oo_.(sprintf('%s_mode', field_name)).shocks_corr.(NAME) = xparam1(ip);
+        oo_.(sprintf('%s_std_at_mode', field_name)).shocks_corr.(NAME) = stdh(ip);
         ip = ip+1;
     end
     skipline()
@@ -166,8 +166,8 @@ if ncn
             fprintf('%-*s %10.4f %7.4f %7.4f \n',header_width, name, xparam1(ip), ...
                     stdh(ip), tstath(ip));
         end
-        eval(['oo_.' field_name '_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
-        eval(['oo_.' field_name '_std_at_mode.measurement_errors_corr.' NAME ' = stdh(ip);']);
+        oo_.(sprintf('%s_mode', field_name)).measurement_errors_corr.(NAME) = xparam1(ip);
+        oo_.(sprintf('%s_std_at_mode', field_name)).measurement_errors_corr.(NAME) = stdh(ip);
         ip = ip+1;
     end
     skipline()
@@ -407,4 +407,4 @@ function TeXEnd(fid)
 fprintf(fid,'\\end{longtable}\n ');
 fprintf(fid,'\\end{center}\n');
 fprintf(fid,'%% End of TeX file.\n');
-fclose(fid);
+fclose(fid);

matlab/estimation/draw_prior_density.m

@@ -80,7 +80,7 @@ switch pshape(indx)
     abscissa = linspace(infbound,supbound,steps);
     dens = density(abscissa,p6(indx),p7(indx),p3(indx));
   otherwise
-    error(sprintf('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx)));
+    error('draw_prior_density: unknown distribution shape (index %d, type %d)', indx, pshape(indx));
 end
 
 if pshape(indx) ~= 5
@@ -88,9 +88,9 @@ if pshape(indx) ~= 5
     if k1 == 1 || k1 == length(dens)
         k = find(dens > 10);
         dens(k) = NaN;
+        end
     end
-end
 binf = abscissa(1);
 bsup = abscissa(end);
 x = abscissa;
-f = dens;
+f = dens;

matlab/estimation/dynare_estimation_1.m

@@ -373,7 +373,7 @@ if ~issmc(options_) && any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode
             oo_.MarginalDensity.LaplaceApproximation = NaN;
         end
         skipline()
-        disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
+        dprintf('Log data density [Laplace approximation] is %f.', oo_.MarginalDensity.LaplaceApproximation)
         skipline()
     end
     if options_.dsge_var

matlab/estimation/maximize_prior_density.m

@@ -54,7 +54,7 @@ function [xparams, lpd, hessian_mat] = ...
                                                                   names, ...
                                                                   [], ...
                                                                   [], ...
-                                                                  Prior,
+                                                                  Prior, ...
                                                                   options_, ...
                                                                   M_, ...
                                                                   estim_params_, ...

matlab/estimation/minus_logged_prior_density.m

@@ -1,4 +1,4 @@
-function [fval, info, exitflag, ~, ~] = minus_logged_prior_density(xparams, Prior, options_, M_, estim_params_, oo_)
+function [fval, info, exitflag, fake1, fake2] = minus_logged_prior_density(xparams, Prior, options_, M_, estim_params_, oo_)
 
 % Evaluates minus the logged prior density.
  %
@@ -33,6 +33,8 @@ function [fval, info, exitflag, ~, ~] = minus_logged_prior_density(xparams, Prio
 
 exitflag = true;
 info = zeros(4,1);
+fake1 = [];
+fake2 = [];
 
 %------------------------------------------------------------------------------
 % 1. Get the structural parameters & define penalties

matlab/estimation/model_comparison.m

@@ -87,7 +87,7 @@ for i=1:NumberOfModels
         end
     end
     try
-        eval(['MarginalLogDensity(i) = mstruct.oo_.MarginalDensity.' type ';'])
+        MarginalLogDensity(i) = mstruct.oo_.MarginalDensity.(type);
     catch
         if strcmpi(type,'LaplaceApproximation')
             if isfield(mstruct.oo_,'mle_mode')

matlab/estimation/posterior_sampler_iteration.m

@@ -125,7 +125,7 @@ switch posterior_sampling_method
         % check whether draw is valid and compute posterior
         if all( proposed_par(:) > mh_bounds.lb(indices(blocks==block_iter,1),:) ) && all( proposed_par(:) < mh_bounds.ub(indices(blocks==block_iter,1),:) )
             try
-                logpost = - feval('TaRB_optimizer_wrapper', proposed_par(:),...
+                logpost = - TaRB_optimizer_wrapper(proposed_par(:),...
                                   current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
                                   varargin{:});
             catch

matlab/get_new_or_existing_ei_index.m

@@ -36,24 +36,24 @@ function indx = get_new_or_existing_ei_index(substructure_name, name1, name2)
 
 global estimation_info
 
-if eval(['isempty(estimation_info.' substructure_name ')'])
+if isempty(estimation_info.(substructure_name))
     indx = 1;
     return
 end
 
 if isempty(name2) % parameter or std() statement
-    indx = eval(['find(strcmp(name1, estimation_info.' substructure_name ') == 1)']);
+    indx = find(strcmp(name1, estimation_info.(substructure_name)));
 else % corr statement
-    indx = eval(['find(strcmp([''' name1 ':' name2 '''], estimation_info.' substructure_name ') == 1)']);
+    indx = find(strcmp(sprintf('%s:%s', name1, name2), estimation_info.(substructure_name)));
     if isempty(indx)
-        indx = eval(['find(strcmp([''' name2 ':' name1 '''], estimation_info.' substructure_name ') == 1)']);
+        indx = find(strcmp(sprintf('%s:%s', name2, name1), estimation_info.(substructure_name)));
     end
 end
 
 if isempty(indx)
-    indx = eval(['size(estimation_info.' substructure_name ', 2) + 1']);
+    indx = size(estimation_info.(substructure_name), 2) + 1;
 end
 
 if size(indx, 2) > 1
-    error(['Error: ' name1 ' ' name2 'found more than once in estimation_info.subsamples_index']);
+    error('Error: %s %s found more than once in estimation_info.subsamples_index', name1, name2);
 end

matlab/kalman/likelihood/kalman_filter.m

@@ -194,13 +194,10 @@ while notsteady && t<=last
         end
     end
     if badly_conditioned_F
-        if ~all(abs(F(:))<kalman_tol)
-            % Use univariate filter (will remove observations with zero variance prediction error)
-            return
-        else
-            % Pathological case, discard draw.
-            return
-        end
+        % if ~all(abs(F(:))<kalman_tol), then use univariate filter (will remove
+        % observations with zero variance prediction error), otherwise this is a
+        % pathological case and the draw is discarded
+        return
     else
         F_singular = false;
         if rescale_prediction_error_covariance

matlab/kalman/likelihood/kalman_filter_d.m

@@ -87,13 +87,9 @@ while rank(Z*Pinf*Z',diffuse_kalman_tol) && (t<=last)
         else                                                            %rank of F_{\infty,t} is 0
             Fstar  = Z*Pstar*Z' + H;                                    % (5.7) in DK (2012)
             if rcond(Fstar) < kalman_tol                                %F_{*} is singular
-                if ~all(abs(Fstar(:))<kalman_tol)
-                    % The univariate diffuse kalman filter should be used.
-                    return
-                else                                                    %rank 0
-                                                                        %pathological case, discard draw
-                    return
-                end
+                % if ~all(abs(Fstar(:))<kalman_tol), then use univariate diffuse filter,
+                % otherwise this is a pathological case and the draw is discarded
+                return
             else
                 iFstar = inv(Fstar);
                 dFstar = det(Fstar);

matlab/kalman/likelihood/kalman_filter_fast.m

@@ -151,13 +151,9 @@ while notsteady && t<=last
         v  = Y(:,t)-a(Z);
     end
     if rcond(F) < kalman_tol
-        if ~all(abs(F(:))<kalman_tol)
-            % The univariate diffuse kalman filter should be used.
-            return
-        else
-            %pathological case, discard draw
-            return
-        end
+        % if ~all(abs(F(:))<kalman_tol), then use univariate diffuse filter, otherwise
+        % this is a pathological case and the draw is discarded
+        return
     else
         F_singular = 0;
         dF      = det(F);

matlab/kalman/likelihood/missing_observations_kalman_filter.m

@@ -191,13 +191,9 @@ while notsteady && t<=last
             end
         end
         if badly_conditioned_F && (~occbin_.status || (occbin_.status && t<first_period_occbin_update))
-            if ~all(abs(F(:))<kalman_tol)
-                % Use univariate filter.
-                return
-            else
-                % Pathological case, discard draw
-                return
-            end
+            % if ~all(abs(F(:))<kalman_tol), then use univariate filter, otherwise this is a
+            % pathological case and the draw is discarded
+            return
         else
             F_singular = false;
         end

matlab/kalman/likelihood/missing_observations_kalman_filter_d.m

@@ -105,13 +105,9 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
             else                                                            %rank of F_{\infty,t} is 0
                 Fstar = ZZ*Pstar*ZZ' + H(d_index,d_index);                  % (5.7) in DK (2012)
                 if rcond(Fstar) < kalman_tol                                %F_{*} is singular
-                    if ~all(abs(Fstar(:))<kalman_tol)
-                        % The univariate diffuse kalman filter should be used.
-                        return
-                    else %rank 0
-                         %pathological case, discard draw
-                        return
-                    end
+                    % if ~all(abs(Fstar(:))<kalman_tol), then use univariate diffuse filter,
+                    % otherwise this is a pathological case and the draw is discarded
+                    return
                 else
                     iFstar = inv(Fstar);
                     dFstar = det(Fstar);

matlab/lmmcp/get_complementarity_conditions.m

@@ -57,8 +57,7 @@ for i=1:size(etags,1)
         if ~isempty(kop)
             k = find(strcmp(strtrim(str(1:kop-1)), M_.endo_names)); %get variable index with restriction
             if isempty(k)
-                error(sprintf(['Complementarity condition %s: variable %s is ' ...
-                               'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
+                error('Complementarity condition %s: variable %s is not recognized', etags{i,3}, strtrim(str(1:kop-1)))
             end
             ub(k) = str2num(str(kop+1:end));
             eq_index(eq_nbr) = k;
@@ -68,15 +67,13 @@ for i=1:size(etags,1)
             if ~isempty(kop)
                 k = find(strcmp(strtrim(str(1:kop-1)), M_.endo_names)); %get variable index with restriction
                 if isempty(k)
-                    error(sprintf(['Complementarity condition %s: variable %s is ' ...
-                                   'not recognized'],etags{i,3},strtrim(str(1:kop-1))))
+                    error('Complementarity condition %s: variable %s is not recognized', etags{i,3}, strtrim(str(1:kop-1)))
                 end
                 lb(k) = str2num(str(kop+1:end));
                 eq_index(eq_nbr) = k;
                 eq_index(k) = eq_nbr;
             else
-                error(sprintf(['Complementarity condition %s can''t be ' ...
-                               'parsed'],etags{i,3}))
+                error('Complementarity condition %s can''t be parsed',etags{i,3})
             end
         end
     end

matlab/modules/dseries

@@ -1 +1 @@
-Subproject commit a3874403fe5b403f74d727006cb4ccf198cf63c8
+Subproject commit 8acb7f8d5f6418717a9ad47e6b6b911354a86409

matlab/ms-sbvar/ms_mardd.m

@@ -130,7 +130,7 @@ for k=1:nvar
         for draws = 1:ndraws1
             if ~mod(draws,nbuffer)
                 skipline()
-                disp(sprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs',k,draws))
+                dprintf('The %dth column or equation in A0 with %d 1st tossed-away draws in Gibbs', k, draws)
             end
             A0gbs1 = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
             A0gbs0=A0gbs1;    % repeat the Gibbs sampling
@@ -141,7 +141,7 @@ for k=1:nvar
         for draws = 1:ndraws2
             if ~mod(draws,nbuffer)
                 skipline()
-                disp(sprintf('The %dth column or equation in A0 with %d usable draws in Gibbs',k,draws))
+                dprintf('The %dth column or equation in A0 with %d usable draws in Gibbs', k, draws)
             end
             [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks);
             %------ See p.71, Forecast (II).
@@ -163,7 +163,7 @@ for k=1:nvar
         % The log value of p(a0_k|Y,a_others) where a_others: other a's at some point such as the peak of ONLY some a0's
     else
         skipline()
-        disp(sprintf('The last(6th) column or equation in A0 with no Gibbs draws'))
+        disp('The last(6th) column or equation in A0 with no Gibbs draws')
         [A0gbs1, Wcell] = fn_gibbsrvar(A0gbs0,UT,nvar,fss,n0,indx_ks)
         %------ See p.71, Forecast (II).
         %------ Computing p(a0_k|Y,a_others) at some point such as the peak along the dimensions of indx_ks.

matlab/optimization/cmaes.m

@@ -1739,7 +1739,7 @@ while irun <= myeval(opts.Restarts) % for-loop does not work with resume
         if N < 102
             disp(['mean solution:' sprintf(' %+.1e', xmean)]);
             disp(['std deviation:' sprintf('  %.1e', sigma*sqrt(diagC))]);
-            disp(sprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)'));
+            dprintf('use plotcmaesdat.m for plotting the output at any time (option LogModulo must not be zero)');
         end
         if exist('sfile', 'var')
             disp(['Results saved in ' sfile]);
@@ -1944,7 +1944,7 @@ end
 % ---------------------------------------------------------------
 function flush
 if isoctave
-    feval('fflush', stdout);
+    fflush(stdout);
 end
 
 % ---------------------------------------------------------------
@@ -2256,7 +2256,7 @@ ax(2) = max(minxend, ax(2));
 axis(ax);
 
 % add some annotation lines
-[ignore idx] = sort(d.x(end,6:end));
+[ignore, idx] = sort(d.x(end,6:end));
 % choose no more than 25 indices
 idxs = round(linspace(1, size(d.x,2)-5, min(size(d.x,2)-5, 25)));
 yy = repmat(NaN, 2, size(d.x,2)-5);

matlab/optimization/simplex_optimization_routine.m

@@ -384,19 +384,19 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
         fval_(1:length(fval)) = fval;
         if isfinite(fv(end)) && isfinite(fv(1))
             if fv(end)<0
-                disp(sprintf('%s  %s  %12.7E  %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
+                dprintf('%s  %s  %12.7E  %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move)
             else
                 if fv(1)>0
-                    disp(sprintf('%s  %s %12.7E %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
+                    dprintf('%s  %s %12.7E %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move)
                 else
-                    disp(sprintf('%s  %s  %12.7E %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move))
+                    dprintf('%s  %s  %12.7E %12.7E  %12.7E  %12.7E  %s', iter_, fval_, fv(1), fv(end), critF, critX, move)
                 end
             end
         else
             if isfinite(fv(1))
-                disp(sprintf(['%s  %s  %12.7E                                 %12.7E  %s'], iter_, fval_, fv(1) , critX, move))
+                dprintf(['%s  %s  %12.7E                                 %12.7E  %s'], iter_, fval_, fv(1) , critX, move)
             else
-                disp(sprintf('%s  %s                                         %12.7E  %s', iter_, fval_, critX, move))
+                dprintf('%s  %s                                         %12.7E  %s', iter_, fval_, critX, move)
             end
         end
     end
@@ -551,4 +551,4 @@ for j = 1:n
 end
 % Sort by increasing order of the objective function values.
 [fv,sort_idx] = sort(fv);
-v = v(:,sort_idx);
+v = v(:,sort_idx);

matlab/optimization/simpsa.m

@@ -264,7 +264,7 @@ while 1
         if nITERATIONS == 0
             disp(' Nr Iter  Nr Fun Eval    Min function       Best function        TEMP           Algorithm Step');
         else
-            disp(sprintf('%5.0f      %5.0f       %12.6g     %15.6g      %12.6g       %s',nITERATIONS,nFUN_EVALS,Y(1),YBEST,TEMP,'best point'));
+            dprintf('%5.0f      %5.0f       %12.6g     %15.6g      %12.6g       %s',nITERATIONS,nFUN_EVALS,Y(1),YBEST,TEMP,'best point');
         end
     end
 
@@ -319,7 +319,7 @@ while 1
         end
 
         if strcmp(OPTIONS.DISPLAY,'iter')
-            disp(sprintf('%5.0f      %5.0f       %12.6g     %15.6g      %12.6g       %s',nITERATIONS,nFUN_EVALS,Y(1),YBEST,TEMP,ALGOSTEP));
+            dprintf('%5.0f      %5.0f       %12.6g     %15.6g      %12.6g       %s',nITERATIONS,nFUN_EVALS,Y(1),YBEST,TEMP,ALGOSTEP);
         end
 
         % if output function given then run output function to plot intermediate result

matlab/parallel/AnalyseComputationalEnvironment.m

@@ -175,12 +175,12 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
 
         if Environment
             if OScallerWindows
-                [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+                [si1, de1]=system(['ping ', DataInput(Node).ComputerName]);
             else
-                [si1 de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
+                [si1, de1]=system(['ping ', DataInput(Node).ComputerName, ' -c 4']);
             end
         else
-            [si1 de1]=system(['ping ', DataInput(Node).ComputerName]);
+            [si1, de1]=system(['ping ', DataInput(Node).ComputerName]);
         end
 
         if (si1)
@@ -387,15 +387,15 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
         else
             if ~strcmp(DataInput(Node).ComputerName,MasterName) % run on remote machine
                 if  strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
-                    [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+                    [NonServeS, NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
                 else
-                    [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+                    [NonServeS, NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e -u ',DataInput(Node).UserName,' -p ',DataInput(Node).Password,' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
                 end
             else % run on local machine via the network: user and passwd cannot be used!
                 if  strfind([DataInput(Node).MatlabOctavePath], 'octave') % Hybrid computing Matlab(Master)->Octave(Slaves) and Vice Versa!
-                    [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
+                    [NonServeS, NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' Tracing.m']);
                 else
-                    [NonServeS NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
+                    [NonServeS, NenServeD]=system(['start /B psexec \\',DataInput(Node).ComputerName,' -e ',' -W ',DataInput(Node).RemoteDrive,':\',DataInput(Node).RemoteDirectory,'\',RemoteTmpFolder ' -low   ',DataInput(Node).MatlabOctavePath,' -nosplash -nodesktop -minimize -r Tracing']);
                 end
             end
 
@@ -570,7 +570,7 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
 
     % Questo controllo penso che si possa MIGLIORARE!!!!!
     if  isempty (RealCPUnbr) && Environment1==0
-        [si0 de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
+        [si0, de0]=system(['psinfo \\',DataInput(Node).ComputerName]);
     end
     RealCPUnbr=GiveCPUnumber(de0,Environment1);
 

matlab/parallel/distributeJobs.m

@@ -129,7 +129,7 @@ if SumOfJobs~=NumbersOfJobs
         % Many choices are possible:
         % - ... (see above).
 
-        [NonServe VeryFast]= min(CPUWeight);
+        [NonServe, VeryFast]= min(CPUWeight);
 
         while SumOfJobs<NumbersOfJobs
             JobsForNode(VeryFast)=JobsForNode(VeryFast)+1;

matlab/parallel/dynareParallelDir.m

@@ -59,7 +59,7 @@ for indPC=1:length(Parallel)
             if isoctave % Patch for peculiar behaviour of ls under Linux.
 
                 % It is necessary to capture the ls warning message and properly manage the jolly char '*'!
-                [check ax]=system(['ls ' ,filename, ' 2> OctaveStandardOutputMessage.txt']);
+                [check, ax]=system(['ls ' ,filename, ' 2> OctaveStandardOutputMessage.txt']);
 
                 if check ~= 0 || ~isempty(strfind(ax,'No such file or directory'))
                     ax=[];

matlab/parallel/dynareParallelGetFiles.m

@@ -61,13 +61,13 @@ for indPC=1:length(Parallel)
 
                     if isempty (FindAst)
 
-                        [NonServeL NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},NamFileInput{jfil,2},' ',NamFileInput{jfil,1}]);
+                        [NonServeL, NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},NamFileInput{jfil,2},' ',NamFileInput{jfil,1}]);
 
                     else
 
                         filenameTemp=NamFileInput{jfil,2};
 
-                        [NotUsed FlI]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filenameTemp, ' 2> OctaveStandardOutputMessage.txt']);
+                        [NotUsed, FlI]=system(['ssh ',ssh_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,' ls ',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',filenameTemp, ' 2> OctaveStandardOutputMessage.txt']);
 
                         if isempty (FlI)
                             return
@@ -81,13 +81,13 @@ for indPC=1:length(Parallel)
                         for i=1: NumFileToCopy
                             Ni=num2str(i);
                             filenameTemp(1,AstPos)=Ni;
-                            [NonServeL NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},filenameTemp,' ',NamFileInput{jfil,1}]);
+                            [NonServeL, NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},filenameTemp,' ',NamFileInput{jfil,1}]);
                         end
                     end
 
                 else
 
-                    [NonServeL NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},NamFileInput{jfil,2},' ',NamFileInput{jfil,1}]);
+                    [NonServeL, NonServeR]= system(['scp ',scp_token,' ',Parallel(indPC).UserName,'@',Parallel(indPC).ComputerName,':',Parallel(indPC).RemoteDirectory,'/',PRCDir,'/',NamFileInput{jfil,1},NamFileInput{jfil,2},' ',NamFileInput{jfil,1}]);
                 end
 
             end

matlab/parallel/masterParallel.m

@@ -710,11 +710,7 @@ if parallel_recover ==0
                 pcerdone(j) = prtfrc;
                 idCPU(j) = njob;
                 if isoctave || options_.console_mode
-                    if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
-                        statusString = [statusString, int2str(j), ' %3.f%% done! '];
-                    else
-                        statusString = [statusString, int2str(j), ' %3.f%% done! '];
-                    end
+                    statusString = [statusString, int2str(j), ' %3.f%% done! '];
                 else
                     status_String{j} = waitbarString;
                     status_Title{j} = waitbarTitle;
@@ -722,11 +718,7 @@ if parallel_recover ==0
             catch % ME
                   % To define!
                 if isoctave || options_.console_mode
-                    if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
-                        statusString = [statusString, int2str(j), ' %3.f%% done! '];
-                    else
-                        statusString = [statusString, int2str(j), ' %3.f%% done! '];
-                    end
+                    statusString = [statusString, int2str(j), ' %3.f%% done! '];
                 end
             end
         end
@@ -901,7 +893,7 @@ switch Strategy
         end
 
         if isempty(dir('dynareParallelLogFiles'))
-            [A B C]=rmdir('dynareParallelLogFiles');
+            [A, B, C]=rmdir('dynareParallelLogFiles');
             mkdir('dynareParallelLogFiles');
         end
         try
@@ -919,7 +911,7 @@ switch Strategy
     delete(['temp_input.mat'])
     if newInstance
         if isempty(dir('dynareParallelLogFiles'))
-            [A B C]=rmdir('dynareParallelLogFiles');
+            [A, B, C]=rmdir('dynareParallelLogFiles');
             mkdir('dynareParallelLogFiles');
         end
     end

matlab/parallel/storeGlobalVars.m

@@ -31,14 +31,14 @@ function storeGlobalVars(fname,append)
 
 
 GlobalNames = who('global');
-% varlist = '';
+
 for j=1:length(GlobalNames)
     eval(['global ',GlobalNames{j},';']);
-    eval(['fGlobalVar.',GlobalNames{j},'=',GlobalNames{j},';']);
+    fGlobalvar.(GlobalNames{j}) = GlobalNames{j};
 end
 
 if nargin<2
     save(fname,'fGlobalVar');
 else
     save(fname,'fGlobalVar','-append');
-end
+end

matlab/partial_information/PI_gensys.m

@@ -76,7 +76,7 @@ try
         warning('off','MATLAB:nearlySingularMatrix');
         warning('off','MATLAB:singularMatrix');
         UAVinv=inv(C2); % i.e. inv(U02'*a1*V02)
-        [LastWarningTxt LastWarningID]=lastwarn;
+        [LastWarningTxt, LastWarningID]=lastwarn;
         if any(any(isinf(UAVinv)))==1
             singular=1;
         end
@@ -178,9 +178,9 @@ G0pi=eye(n+FL_RANK+NX);
 try
     if isoctave && octave_ver_less_than('9')
         % Need to force QZ complex on Octave ⩽ 8 (otherwise it returns the real one)
-        [a b q z]=qz(complex(G0pi),complex(G1pi));
+        [a, b, q, z]=qz(complex(G0pi),complex(G1pi));
     else
-        [a b q z]=qz(G0pi,G1pi);
+        [a, b, q, z]=qz(G0pi,G1pi);
     end
 catch
     try
@@ -239,7 +239,7 @@ for i=1:nn
 end
 div ;
 if ~zxz
-    [a b q z]=qzdiv(div,a,b,q,z);
+    [a, b, q, z]=qzdiv(div,a,b,q,z);
 end
 
 gev=[diag(a) diag(b)];

matlab/partial_information/PI_gensys_singularC.m

@@ -71,7 +71,7 @@ try
         singular=1;
     else
         UAVinv=inv(C2);
-        [LastWarningTxt LastWarningID]=lastwarn;
+        [LastWarningTxt, LastWarningID]=lastwarn;
         if any(any(isinf(UAVinv)))==1
             singular=1;
         end

matlab/perfect-foresight-models/det_cond_forecast.m

@@ -888,7 +888,7 @@ else
                 % in case of mixed surprise and perfect foresight on the
                 % endogenous path, at each date all the exogenous paths have to be
                 % stored. The paths are stacked in exo.
-                for time = time_index_constraint;
+                for time = time_index_constraint
                     exo(past_val + time,j_pos) = oo_.exo_simul(time,j_pos);
                 end
             else

matlab/perfect-foresight-models/solve_block_decomposed_problem.m

@@ -48,7 +48,7 @@ else
 end
 if options_.verbosity
     printline(41)
-    disp(sprintf('MODEL SIMULATION (method=%s):',mthd))
+    dprintf('MODEL SIMULATION (method=%s):', mthd)
     skipline()
 end
 

matlab/print_expectations.m

@@ -24,7 +24,7 @@ function print_expectations(eqname, expectationmodelname, expectationmodelkind,
 %
 % The variable expectationmodelkind can take two values 'var' or 'pac'.
 
-% Copyright © 2018-2021 Dynare Team
+% Copyright © 2018-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -347,7 +347,7 @@ for i=1:maxlag
             for k=1:length(expectationmodel.components)
                 parameter = M_.params(expectationmodel.components(k).h_param_indices(id));
                 if parameter>=0
-                    expressions{k} = sprintf('%s+%1.16f*%s', expressions{k}, parameter, variable);;
+                    expressions{k} = sprintf('%s+%1.16f*%s', expressions{k}, parameter, variable);
                 else
                     expressions{k} = sprintf('%s-%1.16f*%s', expressions{k}, -parameter, variable);
                 end

matlab/read_variables.m

@@ -126,5 +126,4 @@ switch (extension)
 end
 
 cd(old_pwd)
-disp(sprintf('Loading %d observations from %s\n',...
-             size(dyn_data_01,1),fullname))
+dprintf('Loading %d observations from %s', size(dyn_data_01, 1), fullname)

matlab/shock_decomposition/graph_decomp_detail.m

@@ -108,7 +108,7 @@ end
 if abs(floor(x(1))-xind_tick(1))-abs(ceil(x(end))-xind_tick(end))<-1
     xind_tick = xind_tick+1;
 end
-if length(xind_tick)==gend,
+if length(xind_tick)==gend
     xind_tick = x(2:end);
 end
 % xind_tick = [x(ind_yrs(1))-floor(dind_tick/2):dind_tick:x(ind_yrs(end))+floor(dind_tick/2)]+1;
@@ -154,7 +154,7 @@ comp_nbr0=comp_nbr;
 %%plot decomposition
 for j=1:nvar
     z1 = squeeze(z(i_var(j),:,:));
-    if screen_shocks,
+    if screen_shocks
         [~, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
         if initval
             labels = char(char(shock_names(isort(1:16))),'Others', 'All shocks');

matlab/utilities/graphics/colorspace.m

@@ -82,7 +82,7 @@ function varargout = colorspace(Conversion,varargin)
 %  ("short").
 
 % Copyright © 2005-2010 Pascal Getreuer
-% Copyright © 2017 Dynare Team
+% Copyright © 2017-2023 Dynare Team
 %
 % Redistribution and use in source and binary forms, with or without
 % modification, are permitted provided that the following conditions are
@@ -203,7 +203,7 @@ switch Space
     Space = 'hsv';
   case {'hsl','hsi','hls'}
     Space = 'hsl';
-  case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
+  case {'rgb','yuv','yiq','ydbdr','jpegycbcr','xyz','lab','luv','lch'}
     return
 end
 return