FileContentsSample.m

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%>  \brief
%>  This is the base class for generating objects
%>  that contain the contents of a sample/chain file
%>  generated by a ParaMonte sampler.<br>
%>  This class is meant to be primarily internally
%>  used by the ParaMonte MATLAB library samplers.<br>
%>
%>  \note
%>  See the documentation of the class constructor.<br>
%>
%>  \note
%>  See below for information on the attributes (properties).<br>
%>
%>  \note
%>  See below for information on the methods.<br>
%>
%>  \final{FileContentsSample}
%>
%>  \author
%>  \AmirShahmoradi, 3:31 PM Friday, November 8, 2024, Dallas, TX<br>
%>  \FatemehBagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.<br>
classdef FileContentsSample < pm.io.FileContentsTabular
 
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    properties(Access = public)
        %>
        %>  ``ndim``
        %>
        %>  The scalar MATLAB integer representing the number of
        %>  dimensions of the domain of the objective function sampled.<br>
        %>  This integer is also the number of columns in the file that
        %>  correspond that contain the sampled states from the domain
        %>  of the mathematical objective function.<br>
        %>
        ndim = 0;
        %>
        %>  ``slfc``
        %>
        %>  The scalar MATLAB integer representing the column
        %>  index of the dataframe component ``df`` that contains
        %>  the natural logarithm of the objective function values
        %>  corresponding to the sampled states next to this column,
        %>  such that the following relationship holds.<br>
        %>
        %>  \code{.m}
        %>      FileContentsSample.ndim = FileContentsSample.ncol - FileContentsSample.slfc;
        %>  \endcode
        %>
        %>  While this column index can be readily inferred by exploring
        %>  the contents of the dataframe component, this column index is also
        %>  computed and explicitly offered to conveniently slice the values of
        %>  the sampled states and their corresponding log-function values.<br>
        %>
        slfc = 0;
        %>
        %>  ``stats``
        %>
        %>  The scalar MATLAB object containing the set of
        %>  computed properties of the contents of the file.<br>
        %>  This component is populated by the subclasses automatically.<br>
        %>  It can also be manually constructed by calling the ``Hidden`` class method
        %>  [pm.sampling.FileContentsSample::setstats](@ref FileContentsSample::setstats).<br>
        %>
        stats = [];
        %>
        %>  ``vis``
        %>
        %>  The scalar MATLAB ``struct`` containing the set of
        %>  predefined visualizations for the output data.<br>
        %>  This component is populated by the subclasses automatically.<br>
        %>  It can also be manually constructed by calling the ``Hidden`` class method
        %>  [pm.sampling.FileContentsSample::setvis](@ref FileContentsSample::setvis).<br>
        vis = [];
    end
 
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    properties(Hidden)
        %>
        %>  ``slfcname``
        %>
        %>  The ``Hidden`` scalar MATLAB string representing
        %>  the column name of the dataframe component ``df`` that
        %>  contains the natural logarithm of the objective function
        %>  values corresponding to the sampled states next to this column.<br>
        %>
        %>  \warning
        %>  This is an internal ``Hidden`` class attribute
        %>  that is inaccessible to the end users.<br>
        %>
        slfcname = "sampleLogFunc";
    end
 
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    methods(Access = public)
 
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
        %>  \brief
        %>  Return a scalar object of class [pm.sampling.FileContentsSample](@ref FileContentsSample).<br>
        %>
        %>  \details
        %>  This is the constructor of the class [pm.sampling.FileContentsSample](@ref FileContentsSample).<br>
        %>
        %>  \param[in]  file    :   The input scalar MATLAB string containing the path to an external file.<br>
        %>  \param[in]  silent  :   See the corresponding argument of [pm.sampling.FileContentsRestart](@ref FileContentsRestart) class.<br>
        %>                          (**optional**. The default is set by [pm.sampling.FileContentsRestart](@ref FileContentsRestart).)
        %>  \param[in]  sep     :   The input scalar MATLAB string containing the field separator used in the file.<br>
        %>                          (**optional**, default = ``","``)
        %>
        %>  \return
        %>  ``self``            :   The output scalar object of class [pm.sampling.FileContentsSample](@ref FileContentsSample).<br>
        %>
        %>  \interface{FileContentsSample}
        %>  \code{.m}
        %>
        %>      contents = pm.sampling.FileContentsSample(file)
        %>      contents = pm.sampling.FileContentsSample(file, [])
        %>      contents = pm.sampling.FileContentsSample(file, silent)
        %>      contents = pm.sampling.FileContentsSample(file, [], [])
        %>      contents = pm.sampling.FileContentsSample(file, [], sep)
        %>      contents = pm.sampling.FileContentsSample(file, silent, [])
        %>      contents = pm.sampling.FileContentsSample(file, silent, sep)
        %>
        %>  \endcode
        %>
        %>  \note
        %>  See the documentations of the subclasses of this class for example usage.<br>
        %>
        %>  \final{FileContentsSample}
        %>
        %>  \author
        %>  \JoshuaOsborne, May 21 2024, 3:36 AM, University of Texas at Arlington<br>
        %>  \FatemehBagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.<br>
        %>  \AmirShahmoradi, May 16 2016, 9:03 AM, Oden Institute for Computational Engineering and Sciences (ICES), UT Austin<br>
        function self = FileContentsSample(file, silent, sep)
 
            if nargin < 3
                sep = [];
            end
            if nargin < 2
                silent = [];
            end
 
            self = self@pm.io.FileContentsTabular(file, silent, sep);
 
            for icol = 1 : self.ncol
                if strcmpi(self.df.Properties.VariableNames{icol}, self.slfcname)
                    break;
                end
            end
 
            self.slfc = icol;
            self.ndim = self.ncol - self.slfc;
            if  self.nrow <= self.ndim
                warning ( newline ...
                        + "There are insufficient number of states in the specified file:" + newline ...
                        + newline ...
                        + pm.io.tab() + self.file + newline ...
                        + newline ...
                        + "for computing the covariance/correlation matrices. Skipping..." + newline ...
                        + newline ...
                        );
                return;
            end
 
            %%%%
            %%%% statistics (actual computations are all done in the ``setstats`` method and respective subclasses).
            %%%%
 
            %self.setstats();
 
            %%%%
            %%%% visualization (actual visualization setup are all done in ``setvis`` method and the respective subclasses).
            %%%%
 
            %self.setvis();
 
        end
 
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    end
 
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    methods(Access = public, Hidden)
 
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
        %>  \brief
        %>  Compute the statistics of the parent object of class [pm.sampling.FileContentsSample](@ref FileContentsSample)
        %>  and store the results in the respective fields of the ``stats`` attribute of the parent object.<br>
        %>
        %>  \brief
        %>  This is a dynamic ``Hidden`` method of class [pm.sampling.FileContentsSample](@ref FileContentsSample).<br>
        %>  It is **inaccessible** to the end users of the library.<br>
        %>
        %>  \param[in]  self    :   The input parent object of class [pm.sampling.FileContentsSample](@ref FileContentsSample)
        %>                          which is **implicitly** passed to this dynamic method (not by the user).<br>
        %>
        %>  \interface{setstats}
        %>  \code{.m}
        %>
        %>      contents = pm.sampling.FileContentsSample();
        %>
        %>      contents.setstats();
        %>
        %>  \endcode
        %>
        %>  \final{setstats}
        %>
        %>  \author
        %>  \AmirShahmoradi, 2:11 PM Friday, November 8, 2024, Dallas, TX<br>
        %>  \FatemehBagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.<br>
        function setstats(self)
 
            self.stats = struct();
 
            self.checkpoint("adding the stats components...", false);
 
            %%%%
            %%%% Add chain cormat.
            %%%%
 
            self.checkpoint("computing the sample correlation matrix...", false);
            self.stats.cor = pm.stats.Cor(self.df(:, self.slfc + 1 : end));
            self.checkpoint();
 
            %%%%
            %%%% Add chain covmat.
            %%%%
 
            self.checkpoint("computing the sample covariance matrix...", false);
            self.stats.cov = pm.stats.Cov(self.df(:, self.slfc + 1 : end));
            self.checkpoint();
 
            %%%%
            %%%% Add chain acf.
            %%%%
 
            self.checkpoint("computing the sample autocorrelation...", false);
            self.stats.acf = pm.stats.AutoCorr(self.df(:, self.slfc : end));
            self.checkpoint();
 
            self.stats.max = struct("val", [], "loc", []);
            self.stats.min = struct("val", [], "loc", []);
 
            %%%%
            %%%% The `{:,:}` slice is essential in MATLAB ~2020a.
            %%%%
 
            [self.stats.max.val, self.stats.max.loc] = max(self.df{:,:});
            [self.stats.min.val, self.stats.min.loc] = min(self.df{:,:});
            self.stats.avg = mean(self.df{:,:});
            self.stats.std = std(self.df{:,:});
 
            %%%%
            %%%% Add the ACF stats information.
            %%%%
 
            self.stats.acf = pm.stats.AutoCorr(@()self.df);
 
            %%%%
            %%%% Add the correlation/covariance matrix information.
            %%%%
 
            self.stats.cor = pm.stats.Cor(@()self.df);
            self.stats.cov = pm.stats.Cov(@()self.df);
 
            self.checkpoint();
 
        end
 
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
        %>  \brief
        %>  Compute the statistics of the parent object of class [pm.sampling.FileContentsSample](@ref FileContentsSample)
        %>  and store the results in the respective fields of the ``stats`` attribute of the parent object.<br>
        %>
        %>  \brief
        %>  This is a dynamic ``Hidden`` method of class [pm.sampling.FileContentsSample](@ref FileContentsSample).<br>
        %>  It is **inaccessible** to the end users of the library.<br>
        %>
        %>  \param[in]  self    :   The input parent object of class [pm.sampling.FileContentsSample](@ref FileContentsSample)
        %>                          which is **implicitly** passed to this dynamic method (not by the user).<br>
        %>
        %>  \interface{setvis}
        %>  \code{.m}
        %>
        %>      contents = pm.sampling.FileContentsSample();
        %>
        %>      contents.setvis();
        %>
        %>  \endcode
        %>
        %>  \final{setvis}
        %>
        %>  \author
        %>  \AmirShahmoradi, 2:12 PM Friday, November 8, 2024, Dallas, TX<br>
        %>  \FatemehBagheri, May 20 2024, 1:25 PM, NASA Goddard Space Flight Center (GSFC), Washington, D.C.<br>
        function setvis(self)
 
            self.vis = struct();
 
            self.checkpoint("adding the visualization components...", false);
 
            colf = self.slfc;
            cols = self.slfc + [1 : self.ndim];
            silent_kws = {"silent", self.silent};
 
            self.vis.cascade = struct();
 
            self.vis.cascade.line = pm.vis.CascadeLine(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.cascade.scatter = pm.vis.CascadeScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.cascade.lineScatter = pm.vis.CascadeLineScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
 
            self.vis.cascade.line3 = pm.vis.CascadeLine3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
            self.vis.cascade.scatter3 = pm.vis.CascadeScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
            self.vis.cascade.lineScatter3 = pm.vis.CascadeLineScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
 
            self.vis.cascade.histfit = pm.vis.CascadeHistfit(@()self.df, "colx", cols, silent_kws{:});
            self.vis.cascade.histogram = pm.vis.CascadeHistogram(@()self.df, "colx", cols, silent_kws{:});
            self.vis.cascade.histogram2 = pm.vis.CascadeHistogram2(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
 
            self.vis.cascade.contour = pm.vis.CascadeContour(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.cascade.contourf = pm.vis.CascadeContourf(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.cascade.contour3 = pm.vis.CascadeContour3(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
 
            self.vis.plot = struct();
 
            self.vis.plot.line = pm.vis.PlotLine(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.plot.scatter = pm.vis.PlotScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.plot.lineScatter = pm.vis.PlotLineScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
 
            self.vis.plot.line3 = pm.vis.PlotLine3(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.plot.scatter3 = pm.vis.PlotScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
            self.vis.plot.lineScatter3 = pm.vis.PlotLineScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
 
            self.vis.plot.histfit = pm.vis.PlotHistfit(@()self.df, "colx", cols, silent_kws{:});
            self.vis.plot.histogram = pm.vis.PlotHistogram(@()self.df, "colx", cols, silent_kws{:});
            self.vis.plot.histogram2 = pm.vis.PlotHistogram2(@()self.df, "colx", colf, "coly", cols, silent_kws{:});
 
            self.vis.plot.contour = pm.vis.PlotContour(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.plot.contourf = pm.vis.PlotContourf(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.plot.contour3 = pm.vis.PlotContour3(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
 
            self.vis.tile = struct();
 
            self.vis.tile.line = pm.vis.TileLine(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.tile.scatter = pm.vis.TileScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.tile.lineScatter = pm.vis.TileLineScatter(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
 
            self.vis.tile.line3 = pm.vis.TileLine3(@()self.df, "coly", cols, "colc", colf, silent_kws{:});
            self.vis.tile.scatter3 = pm.vis.TileScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
            self.vis.tile.lineScatter3 = pm.vis.TileLineScatter3(@()self.df, "coly", cols, "colz", colf, "colc", colf, silent_kws{:});
 
            self.vis.tile.histfit = pm.vis.TileHistfit(@()self.df, "colx", cols, silent_kws{:});
            self.vis.tile.histogram = pm.vis.TileHistogram(@()self.df, "colx", cols, silent_kws{:});
            self.vis.tile.histogram2 = pm.vis.TileHistogram2(@()self.df, "colx", colf, "coly", cols, silent_kws{:});
 
            self.vis.tile.contour = pm.vis.TileContour(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.tile.contourf = pm.vis.TileContourf(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
            self.vis.tile.contour3 = pm.vis.TileContour3(@()self.df, "colx", cols, "coly", colf, silent_kws{:});
 
            self.vis.triplex.lshc2 = pm.vis.Triplex ( pm.vis.SubplotLineScatter(@()self.df, "colx", cols, "coly", cols, "colc", colf, "colorbar", {"enabled", false}) ...
                                                    , pm.vis.SubplotHistogram(@()self.df, "colx", cols) ...
                                                    , pm.vis.SubplotContour(@()self.df, "colx", cols, "coly", cols, "colorbar", {"enabled", false}) ...
                                                    , silent_kws{:} ...
                                                    );
 
            self.vis.triplex.lshcf = pm.vis.Triplex ( pm.vis.SubplotLineScatter(@()self.df, "colx", cols, "coly", cols, "colc", colf, "colorbar", {"enabled", false}) ...
                                                    , pm.vis.SubplotHistogram(@()self.df, "colx", cols) ...
                                                    , pm.vis.SubplotContourf(@()self.df, "colx", cols, "coly", cols, "colorbar", {"enabled", false}) ...
                                                    , silent_kws{:} ...
                                                    );
 
            self.vis.triplex.lshc3 = pm.vis.Triplex ( pm.vis.SubplotLineScatter(@()self.df, "colx", cols, "coly", cols, "colc", colf, "colorbar", {"enabled", false}) ...
                                                    , pm.vis.SubplotHistogram(@()self.df, "colx", cols) ...
                                                    , pm.vis.SubplotContour3(@()self.df, "colx", cols, "coly", cols, "colorbar", {"enabled", false}) ...
                                                    , silent_kws{:} ...
                                                    );
 
            %if  5 < ndim
            %    self.vis.triplex.layout.tiling.position = [.1, .1, nan, nan];
            %    self.vis.triplex.layout.cbarh.position = [];
            %    self.vis.triplex.layout.cbarv.position = [];
            %    self.vis.triplex.layout.tiling.tile.width = [];
            %end
 
            self.checkpoint();
 
        end
 
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    end
 
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end