%% DATA

Matrix = xlsread ("example_data.xlsx");

output = Matrix(:,1);

inputs = Matrix(:,2:end);

%% AUTOMATIC SIMDEC

[SI, FOE, SOE] = sensitivity_indices (output, inputs) % Calculate sensitivity indices

sum(SI) % shows what portion of the variance of the output is explained

% by the combined first- and second-order effects of the inputs

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI); % Build visualization

%% BOXPLOT

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'GraphType','boxplot'); % Boxplot alternative visualization

%% POLISHING: colors, names of variables

output_name = 'Output';

input_names = {'Input1','Input2','Input3','Input4'};

colors = {'#3F45D0','#DC267F','26DCD1'}; % HEX codes for the main colors

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'OutputName',output_name,'InputNames',input_names,'MainColors',colors);

%% TUNING: more variables, different state formation

dec_limit = 0.9; % minimum overall importance [sum(SI)] of chosen for decomposition input variables

boundary_type = 'interval-based'; % divides input ranges into states using equaly-spaced intervals rather than default same amount of observations in each state

% This would not make any difference for independent uniformly distributed inputs

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'DecompositionLimit',dec_limit,'BoundaryType',boundary_type);

%% CUSTOMIZATION: own order of variables, own states

manual_vars = [0 2 1 0]; % specify the order of variables for decomposition, use 0 to exclude, size (1, N_inputs)

% in this example we set that the third input variable is used first, and then the second one.

manual_states = [0 3 2 0]; % specify the number of states for each variable, size (1, N_inputs), the position corresponds to the original order of inputs

manual_boundaries = [ NaN min(inputs(:,2)) min(inputs(:,3)) NaN

NaN 100 657.5 NaN

NaN 650 max(inputs(:,3)) NaN

NaN max(inputs(:,2)) NaN NaN]; % specify numeric boundaries for every state, size(max(manual_states) 1, N_inputs).

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'OrderOfVariables',manual_vars,'NumberOfStates',manual_states,'StateBoundaries',manual_boundaries);

%% Two-output visualization

Matrix = xlsread ("example_data2.xlsx");

output = Matrix(:,1);

output2 = Matrix(:,2);

inputs = Matrix(:,3:end);

[SI, FOE, SOE] = sensitivity_indices (output, inputs)

outputname = 'Output1';

output2name = 'Output2';

share_of_data_shown = 0.005;

n_bins = 40;

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'OutputName',outputname...

,'Output2',output2,'Output2Name',output2name,'ScatterFraction',share_of_data_shown,'NumberOfBins',n_bins);

% Changing XY limits for the first histogram (scatter plot and the second histogram scale accordingly)

xlim_values = [1000 3000]; % affects x-axis of the top histogram and x-axis of the scatterplot.

ylim_values = [0 4]; % value in % for Probability y-axis of top histogram, does not affect the scatterplot.

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'OutputName',outputname...

,'Output2',output2,'Output2Name',output2name,'ScatterFraction',share_of_data_shown,'NumberOfBins',n_bins...

,'XLim',xlim_values,'YLim',ylim_values);

% Enforcing a desired combination of XY limits for the scatterplot

xlim_values = [1000 3000]; % affects x-axis of the top histogram and x-axis of the scatterplot.

xlim_values_2 = [0 1000]; % affects (rotated) x-axis of the right histogram and y-axis of the scatterplot.

[scenarios, scen_legend, boundaries] = simdec_visualization (output, inputs, SI,'OutputName',outputname...

,'Output2',output2,'Output2Name',output2name,'ScatterFraction',share_of_data_shown,'NumberOfBins',n_bins...

,'XLim',xlim_values,'XLim2',xlim_values_2);