function oo_=pm3(M_,options_,oo_,n1,n2,ifil,B,tit1,tit2,tit_tex,names1,names2,name3,DirectoryName,var_type,dispString)
% oo_=pm3(M_,options_,oo_,n1,n2,ifil,B,tit1,tit2,tit_tex,names1,names2,name3,DirectoryName,var_type,dispString)
% Computes, stores and plots the posterior moment statistics.
%
% INPUTS
%  n1           [scalar] size of first dimension of moment matrix
%  n2           [scalar] size of second dimension of moment matrix
%  ifil         [scalar] number of moment files to load
%  B            [scalar] number of subdraws
%  tit1         [string] Figure title
%  tit2         [string] Save name for figure
%  tit_tex      [cell array] TeX-Names for Variables
%  names1       [cell array] Names of all variables in the moment matrix from
%                       which names2 is selected
%  names2       [cell array] Names of variables subset selected for moments
%  name3       [string] Name of the field in oo_ structure to be set
%  DirectoryName [string] Name of the directory in which to save and from
%                       where to read
%  var_type     [string] suffix of the filename from which to load moment
%                   matrix
%  dispString   [string] string to be displays in the command window
%
% OUTPUTS
%  oo_          [structure]     storing the results


% PARALLEL CONTEXT
% See also the comment in posterior_sampler.m function.


% Copyright © 2007-2025 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 <https://www.gnu.org/licenses/>.

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 = length(varlist);
    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
    if isempty(tit_tex)
        tit_tex=names1;
    end
    varlist_TeX = cell(nvar, 1);
    for i=1:nvar
        varlist_TeX(i) = {tit_tex{SelecVariables(i)}};
    end
end
Mean = zeros(n2,nvar);
Median = zeros(n2,nvar);
Var = zeros(n2,nvar);
Distrib = zeros(9,n2,nvar);
HPD = zeros(2,n2,nvar);
if options_.estimation.moments_posterior_density.indicator
    Density = zeros(options_.estimation.moments_posterior_density.gridpoints,2,n2,nvar);
end
fprintf(['%s: ' tit1 '\n'],dispString);
k = 0;
filter_step_ahead_indicator=0;
filter_covar_indicator=0;
state_uncert_indicator=0;

for file = 1:ifil
    loaded_file=load([DirectoryName '/' M_.fname var_type int2str(file)]);
    stock=loaded_file.stock;
    if strcmp(var_type,'_filter_step_ahead')
        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));
            stock1 = zeros(n1,n2,B);
        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
        k = k(end)+(1:size(stock,3));
        stock1(:,:,k) = stock;
        stock1_filter_step_ahead(:,:,k,:) = stock_filter_step_ahead;
    elseif strcmp(var_type,'_filter_covar')
        if file==1 %on first run, initialize variable for storing filter_step_ahead
            stock1_filter_covar=NaN(n1,n2,size(stock,3),B);
        end
        filter_covar_indicator=1;
        k = k(end)+(1:size(stock,4));
        stock1_filter_covar(:,:,:,k) = stock;
    elseif strcmp(var_type,'_trend_coeff')
        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));
            stock1 = zeros(n1,B);
        end
        k = k(end)+(1:size(stock,2));
        stock1(:,k) = stock;
    elseif strcmp(var_type,'_state_uncert')
        if file==1 %on first run, initialize variable for storing filter_step_ahead
            stock1_state_uncert=NaN(n1,n2,size(stock,3),B);
        end
        state_uncert_indicator=1;
        k = k(end)+(1:size(stock,4));
        stock1_state_uncert(:,:,:,k) = stock;
    else
        if file==1 %on first run, initialize variable for storing filter_step_ahead
            stock1 = zeros(n1,n2,B);
        end
        k = k(end)+(1:size(stock,3));
        stock1(:,:,k) = stock;
    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);
    if options_.estimation.moments_posterior_density.indicator
        Density_filter_step_ahead = zeros(options_.estimation.moments_posterior_density.gridpoints,2,filter_steps,nvar,n2);
    end
elseif filter_covar_indicator
    draw_dimension=4;
    oo_.FilterCovariance.Mean = squeeze(mean(stock1_filter_covar,draw_dimension));
    oo_.FilterCovariance.Median = squeeze(median(stock1_filter_covar,draw_dimension));
    oo_.FilterCovariance.var = squeeze(var(stock1_filter_covar,0,draw_dimension));
    if size(stock1_filter_covar,draw_dimension)>2
        hpd_interval = quantile(stock1_filter_covar,[(1-options_.mh_conf_sig)/2 (1-options_.mh_conf_sig)/2+options_.mh_conf_sig],draw_dimension);
    else
        size_matrix=size(stock1_filter_covar);
        hpd_interval=NaN([size_matrix(1:3),2]);
    end
    if size(stock1_filter_covar,draw_dimension)>9
        post_deciles =quantile(stock1_filter_covar,0.1:0.1:0.9,draw_dimension);
    else
        size_matrix=size(stock1_filter_covar);
        post_deciles=NaN([size_matrix(1:3),9]);
    end
    oo_.FilterCovariance.post_deciles=post_deciles;
    oo_.FilterCovariance.HPDinf=squeeze(hpd_interval(:,:,:,1));
    oo_.FilterCovariance.HPDsup=squeeze(hpd_interval(:,:,:,2));
    fprintf(['%s: ' tit1 ', done!\n'],dispString);
    return
elseif state_uncert_indicator
    draw_dimension=4;
    oo_.Smoother.State_uncertainty.Mean = squeeze(mean(stock1_state_uncert,draw_dimension));
    oo_.Smoother.State_uncertainty.Median = squeeze(median(stock1_state_uncert,draw_dimension));
    oo_.Smoother.State_uncertainty.var = squeeze(var(stock1_state_uncert,0,draw_dimension));
    if size(stock1_state_uncert,draw_dimension)>2
        hpd_interval = quantile(stock1_state_uncert,[(1-options_.mh_conf_sig)/2 (1-options_.mh_conf_sig)/2+options_.mh_conf_sig],draw_dimension);
    else
        size_matrix=size(stock1_state_uncert);
        hpd_interval=NaN([size_matrix(1:3),2]);
    end
    if size(stock1_state_uncert,draw_dimension)>9
        post_deciles =quantile(stock1_state_uncert,0.1:0.1:0.9,draw_dimension);
    else
        size_matrix=size(stock1_state_uncert);
        post_deciles=NaN([size_matrix(1:3),9]);
    end
    oo_.Smoother.State_uncertainty.post_deciles=post_deciles;
    oo_.Smoother.State_uncertainty.HPDinf=squeeze(hpd_interval(:,:,:,1));
    oo_.Smoother.State_uncertainty.HPDsup=squeeze(hpd_interval(:,:,:,2));
    fprintf(['%s: ' tit1 ', done!\n'],dispString);
    return
end

if strcmp(var_type,'_trend_coeff') %two dimensional arrays
    for i = 1:nvar
        if options_.estimation.moments_posterior_density.indicator
            [Mean(1,i),Median(1,i),Var(1,i),HPD(:,1,i),Distrib(:,1,i),Density(:,:,1,i)] = ...
                posterior_moments(squeeze(stock1(SelecVariables(i),:)),options_.mh_conf_sig,options_.estimation.moments_posterior_density);
        else
            [Mean(1,i),Median(1,i),Var(1,i),HPD(:,1,i),Distrib(:,1,i)] = ...
                posterior_moments(squeeze(stock1(SelecVariables(i),:)),options_.mh_conf_sig);
        end
    end
else %three dimensional arrays
    for i = 1:nvar
        for j = 1:n2
            if options_.estimation.moments_posterior_density.indicator
                [Mean(j,i),Median(j,i),Var(j,i),HPD(:,j,i),Distrib(:,j,i),Density(:,:,j,i)] = ...
                    posterior_moments(squeeze(stock1(SelecVariables(i),j,:)),options_.mh_conf_sig,options_.estimation.moments_posterior_density);
            else
                [Mean(j,i),Median(j,i),Var(j,i),HPD(:,j,i),Distrib(:,j,i)] = ...
                    posterior_moments(squeeze(stock1(SelecVariables(i),j,:)),options_.mh_conf_sig);
            end
            if filter_step_ahead_indicator
                if options_.estimation.moments_posterior_density.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),Density_filter_step_ahead(:,:,K_step,i,j) ] = ...
                            posterior_moments(squeeze(stock1_filter_step_ahead(SelecVariables(i),j,:,K_step)),options_.mh_conf_sig,options_.estimation.moments_posterior_density);
                    end
                else
                    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)),options_.mh_conf_sig);
                    end
                end
            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

if strcmp(var_type,'_trend_coeff') || strcmp(var_type,'_smoothed_trend') || strcmp(var_type,'_smoothed_trend')
    for i = 1:nvar
        name = deblank(names1{SelecVariables(i)});
        oo_.Smoother.(name3).Mean.(name) = Mean(:,i);
        oo_.Smoother.(name3).Median.(name) = Median(:,i);
        oo_.Smoother.(name3).Var.(name) = Var(:,i);
        oo_.Smoother.(name3).deciles.(name) = Distrib(:,:,i);
        oo_.Smoother.(name3).HPDinf.(name) = HPD(1,:,i)';
        oo_.Smoother.(name3).HPDsup.(name) = HPD(2,:,i)';
        if options_.estimation.moments_posterior_density.indicator
            oo_.Smoother.(name3).density.(name) = Density(:,:,:,i);
        end
    end
else
    for i = 1:nvar
        name = deblank(names1{SelecVariables(i)});
        oo_.(name3).Mean.(name) = Mean(:,i);
        oo_.(name3).Median.(name) = Median(:,i);
        oo_.(name3).Var.(name) = Var(:,i);
        oo_.(name3).deciles.(name) = Distrib(:,:,i);
        oo_.(name3).HPDinf.(name) = HPD(1,:,i)';
        oo_.(name3).HPDsup.(name) = HPD(2,:,i)';
        if options_.estimation.moments_posterior_density.indicator
            oo_.(name3).density.(name) = Density(:,:,:,i);
        end
        if filter_step_ahead_indicator
            for K_step = 1:length(options_.filter_step_ahead)
                name4=['Filtered_Variables_',num2str(options_.filter_step_ahead(K_step)),'_step_ahead'];
                oo_.(name4).Mean.(name) = squeeze(Mean_filter_step_ahead(K_step,i,:));
                oo_.(name4).Median.(name) = squeeze(Median_filter_step_ahead(K_step,i,:));
                oo_.(name4).Var.(name) = squeeze(Var_filter_step_ahead(K_step,i,:));
                oo_.(name4).deciles.(name) = squeeze(Distrib_filter_step_ahead(:,K_step,i,:));
                oo_.(name4).HPDinf.(name) = squeeze(HPD_filter_step_ahead(1,K_step,i,:));
                oo_.(name4).HPDsup.(name) = squeeze(HPD_filter_step_ahead(2,K_step,i,:));
                if options_.estimation.moments_posterior_density.indicator
                    oo_.(name4).density.(name) = squeeze(Density_filter_step_ahead(:,:,K_step,i,:));
                end
            end
        end
    end
end

if strcmp(var_type,'_trend_coeff') || all(all(isnan(Mean)))
    fprintf(['%s: ' tit1 ', done!\n'],dispString);
    return %not do plots
end
%%      Finally I build the plots.

if ~options_.nograph && ~options_.no_graph.posterior
    % 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;
    if options_.TeX
        localVars.varlist_TeX=varlist_TeX;
    end
    localVars.MaxNumberOfPlotsPerFigure=MaxNumberOfPlotsPerFigure;
    localVars.name3=name3;
    localVars.tit2=tit2;
    localVars.Mean=Mean;
    localVars.TeX=options_.TeX;
    localVars.nodisplay=options_.nodisplay;
    localVars.graph_format=options_.graph_format;
    localVars.dname=M_.dname;
    localVars.fname=M_.fname;

    % 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 = [];
                [~, nvar0] = masterParallel(options_.parallel, 1, nvar, [],'pm3_core', localVars,globalVars, options_.parallel_info);
            end
        end
    else
        % For the time being in Octave environment 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 && any(strcmp('eps',cellstr(options_.graph_format)))
        fidTeX = fopen([M_.dname '/graphs/' 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)
            nvar=nvar0(j);
            while i<nvar
                i=i+1;
                if ~all(isnan(Mean(:,i))) % must be consistent with condition used in pm3_core.m
                    subplotnum = subplotnum+1;
                end
                if subplotnum == MaxNumberOfPlotsPerFigure || i == nvar
                    fprintf(fidTeX,'\\begin{figure}[H]\n');
                    fprintf(fidTeX,'\\centering \n');
                    fprintf(fidTeX,['\\includegraphics[width=%2.2f\\textwidth]{%s/graphs/%s_' name3 '_%s}\n'],options_.figures.textwidth*min(subplotnum/nn,1),M_.dname,M_.fname, tit2{i});
                    fprintf(fidTeX,'\\label{Fig:%s:%s}\n',name3,tit2{i});
                    fprintf(fidTeX,'\\caption{%s}\n',tit1);
                    fprintf(fidTeX,'\\end{figure}\n');
                    fprintf(fidTeX,' \n');
                    subplotnum = 0;
                end
            end
        end
        fprintf(fidTeX,'%% End of TeX file.\n');
        fclose(fidTeX);
    end
end

fprintf(['%s: ' tit1 ', done!\n'],dispString);
