MIT License
ParaMonte: plain powerful parallel Monte Carlo library.
Copyright (C) 2012-present, The Computational Data Science Lab
https://github.com/cdslaborg/paramonte

Making line plots with the ParaMonte visualization tools

NOTE
If you are viewing an HTML version of this MATLAB live script on the web, you can download the corresponding MATLAB live script visualization_line.mlx file to this HTML page at,
https://github.com/cdslaborg/paramontex/tree/main/MATLAB/mlx
Once you download the file, open it in MATLAB to view and interact with its contents, which is the same as what you see on this page.
First, let's clean up the MATLAB environment and make sure the path to the ParaMonte library is in MATLAB's path list.
clc;
clear all;
close all;
format compact; format long;
%%%%%%%%%%%% IMPORTANT %%%%%%%%%%%%%
% Set the path to the ParaMonte library:
% Change the following path to the ParaMonte library root directory,
% otherwise, make sure the path to the ParaMonte library is already added
% to MATLAB's path list.
pmlibRootDir = './';
addpath(genpath(pmlibRootDir),"-begin");
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% change MATLAB's working directory to the folder containing this script
% if MATLAB Live Scripts did not create a temporary folder, we would not
% have all of these problems!
try
setwdFilePath = websave("setwd.m","https://github.com/cdslaborg/paramontex/raw/main/MATLAB/mlx/setwd.m");
run(setwdFilePath); % This is a MATLAB script that you can download from the same GitHub location given in the above.
catch % alas, we will have to run the simulations in MATLAB Live Script's temporary folder
filePath = mfilename('fullpath');
[currentDir,fileName,fileExt] = fileparts(filePath); cd(currentDir);
cd(fileparts(mfilename('fullpath'))); % Change working directory to source code directory.
end

ParaMonte's default visualization tools

The ParaMonte library ships with several visualization tools that automate much of the MATLAB coding required to visualize the output of the simulations performed by the ParMonte library samplers.
By replacing the input dataFrame to these tools and following the conventions of the ParaMonte library, one can also use these visualization tools for any dataset that may not have been generated by the ParaMonte library.
Consider the following Markov chain on the web in compact format generated by the ParaDRAM sampler of the ParaMonte library to sample a MultiVariate Normal distribution. Since this is a chain file as inidicated by its suffix "_chain.txt" We will read this file via the ParaDRAM sampler's readChain() method.
pm = paramonte();
pmpd = pm.ParaDRAM();
url = "https://github.com/cdslaborg/paramontex/raw/main/MATLAB/mlx/sampling_multivariate_normal_distribution_via_paradram/out/mvn_serial_process_1_chain.txt";
pmpd.readChain(url); % read the chain file from the web
ParaDRAM - WARNING: The ParaDRAM input simulation specification `pmpd.spec.outputDelimiter` is not set. ParaDRAM - WARNING: This information is essential for successful reading of the requested chain file(s). ParaDRAM - WARNING: Proceeding with the default assumption of comma-delimited chain file contents... ParaDRAM - NOTE: 1 files detected matching the pattern: ParaDRAM - NOTE: "https://github.com/cdslaborg/paramontex/raw/main/MATLAB/mlx/sampling_multivariate_normal_distribution_via_paradram/out/mvn_serial_process_1_chain.txt" ParaDRAM - NOTE: processing file: "D:\Dropbox\Projects\20180101_ParaMonte\paramontex\MATLAB\mlx\visualization\temp_20201004_040738_701.txt" ParaDRAM - NOTE: reading the file contents... ParaDRAM - NOTE: done in 0.560250 seconds. ParaDRAM - NOTE: ndim = 4, count = 50000 ParaDRAM - NOTE: computing the sample correlation matrix... ParaDRAM - NOTE: creating the heatmap plot object from scratch... ParaDRAM - NOTE: done in 0.416550 seconds. ParaDRAM - NOTE: computing the sample covariance matrix... ParaDRAM - NOTE: creating the heatmap plot object from scratch... ParaDRAM - NOTE: done in 0.153320 seconds. ParaDRAM - NOTE: computing the sample autocorrelation... ParaDRAM - NOTE: creating the line plot object from scratch... ParaDRAM - NOTE: creating the scatter plot object from scratch... ParaDRAM - NOTE: creating the lineScatter plot object from scratch... ParaDRAM - NOTE: done in 0.835460 seconds. ParaDRAM - NOTE: adding the graphics tools... ParaDRAM - NOTE: creating the line plot object from scratch... ParaDRAM - NOTE: creating the scatter plot object from scratch... ParaDRAM - NOTE: creating the lineScatter plot object from scratch... ParaDRAM - NOTE: creating the line3 plot object from scratch... ParaDRAM - NOTE: creating the scatter3 plot object from scratch... ParaDRAM - NOTE: creating the lineScatter3 plot object from scratch... ParaDRAM - NOTE: creating the histogram plot object from scratch... ParaDRAM - NOTE: creating the histogram2 plot object from scratch... ParaDRAM - NOTE: creating the histfit plot object from scratch... ParaDRAM - NOTE: creating the contour plot object from scratch... ParaDRAM - NOTE: creating the contourf plot object from scratch... ParaDRAM - NOTE: creating the contour3 plot object from scratch... ParaDRAM - NOTE: creating the grid plot object from scratch... ParaDRAM - NOTE: The processed chain files are now stored in the newly-created component "pmpd.chainList" of the ParaDRAM - NOTE: ParaDRAM object as a cell array. For example, to access the contents of the first (or the only) chain ParaDRAM - NOTE: file, try: ParaDRAM - NOTE: ParaDRAM - NOTE: pmpd.chainList{1}.df ParaDRAM - NOTE: ParaDRAM - NOTE: To access the plotting tools, try: ParaDRAM - NOTE: ParaDRAM - NOTE: pmpd.chainList{1}.plot.<PRESS TAB TO SEE THE LIST OF PLOTS> ParaDRAM - NOTE: ParaDRAM - NOTE: For example, ParaDRAM - NOTE: ParaDRAM - NOTE: pmpd.chainList{1}.plot.line.make() % to make 2D line plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.scatter.make() % to make 2D scatter plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.lineScatter.make() % to make 2D line-scatter plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.line3.make() % to make 3D line plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.scatter3.make() % to make 3D scatter plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.lineScatter3.make() % to make 3D line-scatter plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.contour3.make() % to make 3D kernel-density contour plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.contourf.make() % to make 2D kernel-density filled-contour plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.contour.make() % to make 2D kernel-density plots. ParaDRAM - NOTE: pmpd.chainList{1}.plot.histogram2.make() % to make 2D histograms. ParaDRAM - NOTE: pmpd.chainList{1}.plot.histogram.make() % to make 1D histograms. ParaDRAM - NOTE: pmpd.chainList{1}.plot.grid.make() % to make GridPlot ParaDRAM - NOTE: ParaDRAM - NOTE: To plot or inspect the variable autocorrelations or the correlation/covariance matrices, try: ParaDRAM - NOTE: ParaDRAM - NOTE: pmpd.chainList{1}.stats.<PRESS TAB TO SEE THE LIST OF COMPONENTS> ParaDRAM - NOTE: ParaDRAM - NOTE: For more information and examples on the usage, visit: ParaDRAM - NOTE: ParaDRAM - NOTE: https://www.cdslab.org/paramonte
This method automatically generates a set of tools that can be used to visualize the contents of the compact chain file. Note that these visualization tools are not unique to this particular method of the ParaDRAM sampler or other ParaMonte samplers. For the sake of illustration however, we will create line / scatter plots using the above dataset read via readChain() method of the ParaDRAM routine.
chain = pmpd.chainList{1};
chain.plot.line.make();
Be default, the visualization tools are loaded with a set of predefined settings. For example, line plots are by default colored (unless mutiple variables are to be displayed). These however, can be readily changed. For example, to change the colormap,
chain.plot.line.colormap.values
ans = "winter"
chain.plot.line.colormap.values = autumn;
chain.plot.line.axes.kws.xscale = "log";
chain.plot.line.make()
To draw the colored line, the ParaMonte visualizer utilizes the surface() function of MATLAB. One can pass pairs of (key,value) properties to this MATLAB function by defining those keyword properties in the surface component of the plot object. There are a few properties defined already in this structure,
chain.plot.line.surface.kws
ans = struct with fields:
faceColor: "none" edgeColor: "flat" edgeAlpha: 0.500000000000000 lineStyle: "-" marker: "none" lineWidth: 1
For example, to make the lines thicker and to make it dotted,
chain.plot.line.axes.kws.xscale = "log";
chain.plot.line.colormap.values = "parula";
chain.plot.line.surface.kws.linewidth = 2;
chain.plot.line.surface.kws.linestyle = ":";
chain.plot.line.make();
To reset the properties of the line object to the default settings, try,
chain.plot.line.reset();
ParaDRAM - NOTE: resetting the properties of the line plot...
To reset the entire line object including reading the data again from the input dataFrame, try,
chain.plot.line.reset("hard");
ParaDRAM - NOTE: creating the line plot object from scratch...
Similarly, to change the properties of the colorbar, try,
chain.plot.line.colorbar.kws
ans = struct with fields: fontSize: []
chain.plot.line.colorbar.kws.fontSize = 15;
To change properties that do not exist, simple add them to the kws component, for example,
chain.plot.line.colorbar.kws.location = "northoutside";
chain.plot.line.make()
set(gca, "xscale","log")
chain.plot.line.colorbar.kws
ans = struct with fields:
fontSize: 15 location: "northoutside"
Remember that a handle to all objects in the plot is also stored in the currentFig component of the object. Most of the properties of the figure, axes, and the plots can be also changed directly via these handles. For example, to change the colorbar label, we could try,
chain.plot.line.currentFig.colorbar.Label.String
ans = 'SampleLogFunc'
chain.plot.line.reset();
ParaDRAM - NOTE: resetting the properties of the line plot...
chain.plot.line.axes.kws.xscale = "log";
chain.plot.line.make()
chain.plot.line.currentFig.colorbar.Label.FontSize = 11;
chain.plot.line.currentFig.colorbar.Label.Interpreter = "tex"; % set the interpreter for the colorbar
chain.plot.line.currentFig.colorbar.Label.String = "Log_e ( Probability Density function of MVN )";

Choosing a different column of data for colormap

By default, the column named SampleLogFunc is taken as the colormap values.
chain.plot.line.ccolumns
ans = "SampleLogFunc"
This can eb easily changed o any other column in data via the attribute ccolumns (standing for color-columns),
chain.plot.line.reset("hard")
ParaDRAM - NOTE: creating the line plot object from scratch...
chain.plot.line.ccolumns = "SampleVariable1";
chain.plot.line.axes.kws.xscale = "log";
chain.plot.line.make()

Unicolor plot

Turning the colormap off is very simple,
chain.plot.line.colormap.enabled = false; % make monocolor plot
chain.plot.line.axes.kws.xscale = "log";
chain.plot.line.make();

Plotting multiple columns of data in a single plot

The columns of data that are plotted are determined by the corresponding column names in xcolumns and ycolumns of the plot object:
chain.df.Properties.VariableNames
ans = 1×11 cell array {'ProcessID'} {'DelayedRejectionStage'} {'MeanAcceptanceRate'} {'AdaptationMeasure'} {'BurninLocation'} {'SampleWeight'} {'SampleLogFunc'} {'SampleVariable1'} {'SampleVariable2'} {'SampleVariable3'} {'SampleVariable4'}
chain.plot.line.xcolumns
ans = 0×0 empty cell array
chain.plot.line.ycolumns
ans = "SampleVariable1"
To make plots multiple columns of data in a single plot, simply add the column names to the corresponding component, or more simply,
chain.plot.line.make("ycolumns", [8,"SampleVariable4", 9:10]) % notice the ability to mix column number with column names, or simply pass column ranges
Or make bivariate plot of variables against each other,
chain.plot.line.axes.kws.xscale = "linear";
chain.plot.line.make("xcolumns", "SampleVariable2", "ycolumns", 10)
or, multiple variables against a single variable,
chain.plot.line.legend.enabled = true;
chain.plot.line.make("xcolumns", "SampleVariable2", "ycolumns", [10,11])

Plotting specific rows of data

Selected rows of data can be also plotted, if not all data observations have to be included. For example, we can exclude the burnin episode as determined by the ParaMonte sampler,
chain.plot.line.reset();
ParaDRAM - NOTE: resetting the properties of the line plot...
chain.plot.line.colormap.enabled = false;
chain.plot.line.legend.enabled = true;
burnin = chain.df.BurninLocation(end); % get the inferred burning location at the end of the chain
chain.plot.line.rows = burnin:10:chain.count; % plot every one out of 10 data rows, starting from the burnin location to the end of the chain.
chain.plot.line.make("xcolumns", "SampleVariable2", "ycolumns", [10,11]);
or we could plot a logarithmically-spaced selection of rows of the dataFrame,
chain.plot.line.rows = chain.plot.line.getLogLinSpace ( 1.1 ... base of the logarithm
, 1.0 ... the skip size
... , 1 ... lower limit of the generated range (optional, default is one)
... , 1 ... upper limit of the generated range (optional, default is the total number of rows)
);
chain.plot.line.make();

Adding targets to the plot

One can also add target values to the plots, use the target() component of the plot object. For example, to add the location of the burnin of the chain to the plot, one could try,
chain.plot.line.reset("hard");
ParaDRAM - NOTE: creating the line plot object from scratch...
chain.plot.line.make()
chain.plot.line.target.values = [ burnin, chain.df.SampleVariable1(burnin) ];
chain.plot.line.target.make();
set(gca,"xscale","log");