function pm3(n1,n2,ifil,B,tit1,tit2,tit3,tit_tex,names1,names2,name3,DirectoryName,var_type)

% PARALLEL CONTEXT
% See also the comment in random_walk_metropolis_hastings.m funtion.


% Copyright (C) 2007-2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.

global options_ M_ oo_

nn = 3;
MaxNumberOfPlotsPerFigure = nn^2; % must be square
varlist = names2;
if isempty(varlist)
    varlist = names1;
    SelecVariables = (1:M_.endo_nbr)';
    nvar = M_.endo_nbr;
else
    nvar = size(varlist,1);
    SelecVariables = [];
    for i=1:nvar
        if ~isempty(strmatch(varlist(i,:),names1,'exact'))
            SelecVariables = [SelecVariables;strmatch(varlist(i,:),names1,'exact')];
        end
    end
end
if options_.TeX
    % needs to be fixed
    if isempty(tit_tex),
        tit_tex=M_.endo_names_tex;
    end
        
    varlist_TeX = [];
    for i=1:nvar
        if i==1
            varlist_TeX = tit_tex(SelecVariables(i),:);
        else
            varlist_TeX = char(varlist_TeX,tit_tex(SelecVariables(i),:));
        end
    end
end
Mean = zeros(n2,nvar);
Median = zeros(n2,nvar);
Var = zeros(n2,nvar);
Distrib = zeros(9,n2,nvar);
HPD = zeros(2,n2,nvar);
fprintf(['Estimation::mcmc: ' tit1 '\n']);
stock1 = zeros(n1,n2,B);
k = 0;
filter_step_ahead_indicator=0;
for file = 1:ifil
    load([DirectoryName '/' M_.fname var_type int2str(file)]);
    if size(size(stock),2) == 4
        if file==1 %on first run, initialize variable for storing filter_step_ahead
            stock1_filter_step_ahead=NaN(n1,n2,B,length(options_.filter_step_ahead)); 
        end
        filter_step_ahead_indicator=1;
        stock_filter_step_ahead=zeros(n1,n2,size(stock,4),length(options_.filter_step_ahead));
        for ii=1:length(options_.filter_step_ahead)
            K_step_ahead=options_.filter_step_ahead(ii);
            stock_filter_step_ahead(:,:,:,ii)=stock(ii,:,1+K_step_ahead:n2+K_step_ahead,:);
        end
        stock = squeeze(stock(1,:,1+1:1+n2,:)); %1 step ahead starts at entry 2
    end
    k = k(end)+(1:size(stock,3));
    stock1(:,:,k) = stock;
    if filter_step_ahead_indicator
        stock1_filter_step_ahead(:,:,k,:) = stock_filter_step_ahead;
    end
end
clear stock
if filter_step_ahead_indicator
    clear stock_filter_step_ahead
    filter_steps=length(options_.filter_step_ahead);
    Mean_filter_step_ahead = zeros(filter_steps,nvar,n2);
    Median_filter_step_ahead = zeros(filter_steps,nvar,n2);
    Var_filter_step_ahead = zeros(filter_steps,nvar,n2);
    Distrib_filter_step_ahead = zeros(9,filter_steps,nvar,n2);
    HPD_filter_step_ahead = zeros(2,filter_steps,nvar,n2);
end

tmp =zeros(B,1);
for i = 1:nvar
    for j = 1:n2
        [Mean(j,i),Median(j,i),Var(j,i),HPD(:,j,i),Distrib(:,j,i)] = ...
            posterior_moments(squeeze(stock1(SelecVariables(i),j,:)),0,options_.mh_conf_sig);
        if filter_step_ahead_indicator
            for K_step = 1:length(options_.filter_step_ahead)
                [Mean_filter_step_ahead(K_step,i,j),Median_filter_step_ahead(K_step,i,j),Var_filter_step_ahead(K_step,i,j),HPD_filter_step_ahead(:,K_step,i,j),Distrib_filter_step_ahead(:,K_step,i,j)] = ...
                    posterior_moments(squeeze(stock1_filter_step_ahead(SelecVariables(i),j,:,K_step)),0,options_.mh_conf_sig);
            end    
        end
    end
end
clear stock1
if filter_step_ahead_indicator %write matrices corresponding to ML
    clear stock1_filter_step_ahead
    FilteredVariablesKStepAhead=zeros(length(options_.filter_step_ahead),nvar,n2+max(options_.filter_step_ahead));
    FilteredVariablesKStepAheadVariances=zeros(length(options_.filter_step_ahead),nvar,n2+max(options_.filter_step_ahead));
    for K_step = 1:length(options_.filter_step_ahead)
        FilteredVariablesKStepAhead(K_step,:,1+options_.filter_step_ahead(K_step):n2+options_.filter_step_ahead(K_step))=Mean_filter_step_ahead(K_step,:,:);
        FilteredVariablesKStepAheadVariances(K_step,:,1+options_.filter_step_ahead(K_step):n2+options_.filter_step_ahead(K_step))=Mean_filter_step_ahead(K_step,:,:);
    end
    oo_.FilteredVariablesKStepAhead=FilteredVariablesKStepAhead;
    oo_.FilteredVariablesKStepAheadVariances=FilteredVariablesKStepAheadVariances;
end

for i = 1:nvar
    name = deblank(names1(SelecVariables(i),:));
    eval(['oo_.' name3 '.Mean.' name ' = Mean(:,i);']);
    eval(['oo_.' name3 '.Median.' name ' = Median(:,i);']);
    eval(['oo_.' name3 '.Var.' name ' = Var(:,i);']);
    eval(['oo_.' name3 '.deciles.' name ' = Distrib(:,:,i);']);
    eval(['oo_.' name3 '.HPDinf.' name ' = HPD(1,:,i);']);
    eval(['oo_.' name3 '.HPDsup.' name ' = HPD(2,:,i);']);
    if filter_step_ahead_indicator
        for K_step = 1:length(options_.filter_step_ahead)
            name4=['Filtered_Variables_',num2str(K_step),'_step_ahead'];
            eval(['oo_.' name4 '.Mean.' name ' = squeeze(Mean_filter_step_ahead(K_step,i,:));']);
            eval(['oo_.' name4 '.Median.' name ' = squeeze(Median_filter_step_ahead(K_step,i,:));']);
            eval(['oo_.' name4 '.Var.' name ' = squeeze(Var_filter_step_ahead(K_step,i,:));']);
            eval(['oo_.' name4 '.deciles.' name ' = squeeze(Distrib_filter_step_ahead(:,K_step,i,:));']);
            eval(['oo_.' name4 '.HPDinf.' name ' = squeeze(HPD_filter_step_ahead(1,K_step,i,:));']);
            eval(['oo_.' name4 '.HPDsup.' name ' = squeeze(HPD_filter_step_ahead(2,K_step,i,:));']);
        end
    end    
end
%%
%% 	Finally I build the plots.
%%

% Block of code executed in parallel, with the exception of file
% .tex generation always run sequentially. This portion of code is execute in parallel by
% pm3_core1.m function.

% %%%%%%%%%   PARALLEL BLOCK % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% %%% The file .TeX! are not saved in parallel.



% Store the variable mandatory for local/remote parallel computing.

localVars=[];

localVars.tit1=tit1;
localVars.nn=nn;
localVars.n2=n2;
localVars.Distrib=Distrib;
localVars.varlist=varlist;
localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
localVars.name3=name3;
localVars.tit3=tit3;
localVars.Mean=Mean;
% Like sequential execution!
nvar0=nvar;

if ~isoctave
    % Commenting for testing!
    if isnumeric(options_.parallel) || ceil(size(varlist,1)/MaxNumberOfPlotsPerFigure)<4,
        fout = pm3_core(localVars,1,nvar,0);
        
        % Parallel execution!
    else
        isRemoteOctave = 0;
        for indPC=1:length(options_.parallel),
            isRemoteOctave = isRemoteOctave + (findstr(options_.parallel(indPC).MatlabOctavePath, 'octave'));
        end
        if isRemoteOctave
            fout = pm3_core(localVars,1,nvar,0);
        else
            globalVars = struct('M_',M_, ...
                'options_', options_, ...
                'oo_', oo_);
            [fout, nvar0, totCPU] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
        end
    end
else
    % For the time being in Octave enviroment the pm3.m is executed only in
    % serial modality, to avoid problem with the plots.
    
    fout = pm3_core(localVars,1,nvar,0);
end

subplotnum = 0;

if options_.TeX,
    fidTeX = fopen([M_.dname '/Output/' M_.fname '_' name3 '.TeX'],'w');
    fprintf(fidTeX,'%% TeX eps-loader file generated by Dynare.\n');
    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
    fprintf(fidTeX,' \n');
    nvar0=cumsum(nvar0);

    i=0;    
    for j=1:length(nvar0),
    
    NAMES = [];
    TEXNAMES = [];
    nvar=nvar0(j);
    while i<nvar,
        i=i+1;
        if max(abs(Mean(:,i))) > 10^(-6)
            subplotnum = subplotnum+1;
            name = deblank(varlist(i,:));
            texname = deblank(varlist_TeX(i,:));
            if subplotnum==1
                NAMES = name;
                TEXNAMES = ['$' texname '$'];
            else
                NAMES = char(NAMES,name);
                TEXNAMES = char(TEXNAMES,['$' texname '$']);
            end
        end
        if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
            fprintf(fidTeX,'\\begin{figure}[H]\n');
            for jj = 1:size(TEXNAMES,1)
                fprintf(fidTeX,['\\psfrag{%s}[1][][0.5][0]{%s}\n'],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
            end
            fprintf(fidTeX,'\\centering \n');
            fprintf(fidTeX,['\\includegraphics[scale=0.5]{%s/Output/%s_' name3 '_%s}\n'],M_.dname,M_.fname,deblank(tit3(i,:)));
            fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,deblank(tit3(i,:)));
            fprintf(fidTeX,'\\end{figure}\n');
            fprintf(fidTeX,' \n');
            subplotnum = 0;
            NAMES = [];
            TEXNAMES = [];
        end
    end
    end
    fprintf(fidTeX,'%% End of TeX file.\n');
    fclose(fidTeX);
end

fprintf(['Estimation::mcmc: ' tit1 ', done!\n']);






