Plotting structure - introduced maps and fig script type

Author: fabriciocravo

.DS_Store

@@ -67,7 +67,7 @@ function calculate_power(varargin)
 
     %% Process each repetition file one by one to reduce memory usage
     rep_files = dir(fullfile(Params.save_directory, Params.output, '*.mat'));
-    
+
     % If no files were found output an error
     if isempty(rep_files)
         error('No files found.')

calculate_power.m

@@ -31,7 +31,7 @@
 % Params.data_dir = './data/s_abcd_fc_rosenblatt.mat';
 Params.data_dir = './data/s_hcp_fc_noble_tasks.mat';
 % Params.data_dir = './data/s_hcp_act_noble_1.mat';
-Params.output = 'tfce_cpp_speed_cmp';
+Params.output = 'tfce_power_comp';
 
 % Save specifications - if NaN output becomes dataset file name
 Params.save_directory = './power_calculator_results/';
@@ -56,10 +56,10 @@
 Params.other_scripts_dir='./NBS_benchmarking/support_scripts/';
 
 %%% Resampling parameters %%%
-Params.parallel = false; % run stuff sequentially or in parallel
-Params.n_workers = 10; % num parallel workers for parfor, best if # workers = # cores
+Params.parallel = true; % run stuff sequentially or in parallel
+Params.n_workers = 5; % num parallel workers for parfor, best if # workers = # cores
 Params.n_repetitions = 500;  % 500 recommended
-Params.batch_size = 10;
+Params.batch_size = 5;
 
 
 %% Skip some tests - change ranges or the function
@@ -72,16 +72,15 @@
                     % size of subset is full group size (N=n*2 for two sample t-test or N=n for one-sample)
 
                             % Current model (see above design matrix) only designed for t-test
-Params.force_permute = false;               
+Params.force_permute = true;               
 Params.n_perms = 1000;               % recommend n_perms=5000 to appreciably reduce uncertainty of p-value estimation (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/Theory)
 Params.tthresh_first_level = 3.1;    % t=3.1 corresponds with p=0.005-0.001 (DOF=10-1000)
                             % Only used if cluster_stat_type='Size'
 Params.pthresh_second_level = 0.05;  % FWER or FDR rate 
 Params.tpr_dthresh = 0; % Threshold for true positives vs negatives
 Params.save_significance_thresh = 0.15;
 % Params.all_cluster_stat_types = {'Parametric', 'Size_cpp', 'Fast_TFCE_cpp', 'Constrained_cpp', 'Omnibus_cNBS'};
-Params.all_cluster_stat_types = {'IC_TFCE_FC_cpp_dh1','IC_TFCE_FC_cpp_dh5','IC_TFCE_FC_cpp_dh10', ...
-    'IC_TFCE_FC_cpp_dh25', 'TFCE_cpp_dh1', 'TFCE_cpp_dh5', 'TFCE_cpp_dh10', 'TFCE_cpp_dh25'};
+Params.all_cluster_stat_types = {'Exact_FC_TFCE_cpp'};
 
 Params.all_submethods = {'FWER', 'FDR'};
 
@@ -93,9 +92,9 @@
 %%%%% DEVELOPERS ONLY %%%%%
 % Use a small subset of permutations for faster development -- inappropriate for inference
 
-Params.testing = true;
-Params.test_n_perms = 2;
-Params.test_n_repetitions = 2;
+Params.testing = false;
+Params.test_n_perms = 50;
+Params.test_n_repetitions = 5;
 Params.test_n_workers = 1;
 Params.test_disable_save = false;
 

config_files/setparams.m

@@ -0,0 +1,122 @@
+% TFCE Speed Comparison Analysis Script
+clear; clc;
+
+% Set directory path
+dir_path = ['/Users/f.cravogomes/Desktop/Cloned Repos/Power_Calculator/' ...
+    'power_calculator_results/tfce_cpp_speed_cmp'];
+
+% Get all .mat files
+mat_files = dir(fullfile(dir_path, '*.mat'));
+
+% Initialize storage - columns: n_subs, dh, ic_time, tfce_time
+results = [];
+dh_values = [1, 5, 10, 25];
+
+% Process each file
+fprintf('Processing %d files...\n', length(mat_files));
+
+row = 0;
+for f = 1:length(mat_files)
+    data = load(fullfile(dir_path, mat_files(f).name));
+    
+    if ~isfield(data, 'meta_data') || ~isfield(data.meta_data, 'n_subs')
+        continue;
+    end
+    
+    n_subs = data.meta_data.n_subs;
+    
+    for dh = dh_values
+        ic_field = sprintf('IC_TFCE_FC_cpp_dh%d', dh);
+        tfce_field = sprintf('TFCE_cpp_dh%d', dh);
+        
+        if isfield(data, ic_field) && isfield(data, tfce_field) && ...
+           isfield(data.(ic_field), 'total_time') && ...
+           isfield(data.(tfce_field), 'total_time')
+            
+            row = row + 1;
+            results(row, :) = [n_subs, dh, data.(ic_field).total_time, data.(tfce_field).total_time];
+        end
+    end
+end
+
+exact_tfce_results = [];
+% I am here
+for f = 1:length(mat_files)
+    data = load(fullfile(dir_path, mat_files(f).name));
+    
+    if ~isfield(data, 'meta_data') || ~isfield(data.meta_data, 'n_subs')
+        continue;
+    end
+    
+    n_subs = data.meta_data.n_subs;
+    
+    row = row + 1;
+    exact_tfce_results(row, :) = [n_subs, data.Exact_FC_TFCE_cpp.total_time];
+       
+end
+
+% Generate LaTeX tables
+unique_n_subs = unique(results(:,1));
+
+dh_results = struct();
+
+for ns = unique_n_subs'
+    ns_data = results(results(:,1) == ns, :);
+    
+    fprintf('\\begin{table}[h!]\n');
+    fprintf('\\centering\n');
+    fprintf('\\caption{TFCE Speed Comparison for n\\_subs = %d}\n', ns);
+    fprintf('\\begin{tabular}{|c|c|c|c|c|}\n');
+    fprintf('\\hline\n');
+    fprintf('dh & IC\\_TFCE (s) & TFCE (s) & Speedup & n\\_files \\\\\n');
+    fprintf('\\hline\n');
+    
+    for dh = dh_values
+        dh_data = ns_data(ns_data(:,2) == dh, 3:4);
+        
+        if ~isempty(dh_data)
+            mean_ic = mean(dh_data(:,1));
+            mean_tfce = mean(dh_data(:,2));
+            speedup = mean_tfce / mean_ic;
+            
+            fprintf('%d & %.4f & %.4f & %.2fx & %d \\\\\n', ...
+                dh, mean_ic, mean_tfce, speedup, size(dh_data, 1));
+        end
+
+        if dh == 1
+            ns_string = ['n_', num2str(ns)];
+            dh_results.(ns_string).ic_tfce = mean_ic;
+            dh_results.(ns_string).tfce = mean_tfce;
+        end
+    end
+    
+
+    fprintf('\\hline\n');
+    fprintf('\\end{tabular}\n');
+    fprintf('\\end{table}\n\n');
+end
+
+
+fprintf('\\begin{table}[h!]\n');
+fprintf('\\centering\n');
+fprintf('\\caption{Exact TFCE Results\n', ns);
+fprintf('\\begin{tabular}{|c|c|c|c|c|}\n');
+fprintf('\\hline\n');
+fprintf('dh & IC\\_TFCE (s) & TFCE (s) & Speedup & n\\_files \\\\\n');
+fprintf('\\hline\n');
+
+for ns = unique_n_subs'
+    exact_data = exact_tfce_results(exact_tfce_results(:,1) == ns, :);
+    mean_exact = mean(exact_data(:,2));
+
+    ns_string = ['n_', num2str(ns)];
+    speed_ic = dh_results.(ns_string).ic_tfce/mean_exact;
+    speed_tfce = dh_results.(ns_string).tfce/mean_exact;
+
+    fprintf('%d & %.4f & %.3fx & %.3fx & %d \\\\\n', ...
+        dh, mean_exact, speed_ic, speed_tfce, size(dh_data, 1));
+end
+
+fprintf('\\hline\n');
+fprintf('\\end{tabular}\n');
+fprintf('\\end{table}\n\n');

data_fit_scripts/.DS_Store

@@ -0,0 +1,33 @@
+function map = map_method_plot_name()
+    
+    map = struct();
+
+    % Map functions return maps that change names to display names
+    method_to_display = containers.Map();
+    method_to_display('Parametric_FWER') = 'edge';
+    method_to_display('Parametric_FDR') = 'edge (fdr)';
+    method_to_display('Size_cpp') = 'cluster';
+    method_to_display('Size') = 'cluster';
+    method_to_display('Fast_TFCE_cpp') = 'cluster tfce';
+    method_to_display('Fast_TFCE') = 'cluster tfce';
+    method_to_display('Constrained_cpp_FWER') = 'network';
+    method_to_display('Constrained_FWER') = 'network';
+    method_to_display('Constrained_cpp_FDR') = 'network (fdr)';
+    method_to_display('Constrained_FDR') = 'networkm (fdr)';
+    method_to_display('Omnibus_Multidimensional_cNBS') = 'whole brain';
+
+    map.display = method_to_display;
+
+     % Create mapping from internal method names to display order
+    map_display_order = containers.Map();
+    map_display_order('edge') = 1;
+    map_display_order('edge (fdr)') = 2;
+    map_display_order('cluster') = 3;
+    map_display_order('cluster tfce') = 4;
+    map_display_order('network') = 5;
+    map_display_order('network (fdr)') = 6;
+    map_display_order('whole brain') = 7;
+
+    map.order = map_display_order;
+
+end

data_fit_scripts/time_comparison_tfce.m

@@ -0,0 +1,20 @@
+function map = map_tfce_comp()
+    map = struct();
+    
+    % Map functions return maps that change names to display names
+    method_to_display = containers.Map();
+    method_to_display('IC_TFCE_FC_cpp_dh1') = 'IC-TFCE dh 0.01';
+    method_to_display('IC_TFCE_FC_cpp_dh5') = 'IC-TFCE dh 0.05';
+    method_to_display('IC_TFCE_FC_cpp_dh10') = 'IC-TFCE dh 0.1';
+    method_to_display('IC_TFCE_FC_cpp_dh25') = 'IC-TFCE dh 0.25';
+    map.display = method_to_display;
+    
+    % Color gradient for TFCE variants (green/teal gradient)
+    color_map = containers.Map();
+    color_map('IC-TFCE dh 0.01') = [0, 150, 136]/255;   % Dark teal
+    color_map('IC-TFCE dh 0.05') = [76, 175, 80]/255;   % Green
+    color_map('IC-TFCE dh 0.1') = [129, 199, 132]/255;  % Light green
+    color_map('IC-TFCE dh 0.25') = [165, 214, 167]/255; % Lighter green
+    map.color = color_map;
+
+end