analyze_body_motion_energy_distributions.m

% analyze_body_motion_energy_distributions.m
%
% Description:
%   This script compares the distributions of motion energy between two trial 
%   types, RVT and RV, across multiple probe recordings. For each probe, it 
%   aggregates motion energy values during periods of active motion, enabling 
%   comparison of the motion profiles between different conditions.
%
% Analysis Workflow:
%   1. **Data Loading and Selection**:
%      - Loads motion energy data for each probe across two trial types: 
%        RVT (and RVT with gain up) and RV (and RV with gain up).
%      - Extracts motion energy values during treadmill motion periods within 
%        each trial type.
%
%   2. **Distribution Comparison**:
%      - Aggregates motion energy data across all trials within each trial type 
%        for each probe.
%      - Plots histograms of motion energy distributions for RVT and RV trial 
%        types side-by-side, enabling visual comparison.
%      - Computes and displays the median motion energy for each trial type 
%        within each probe.
%
% Outputs:
%   - Histograms for each probe, comparing the motion energy distributions 
%     for RVT and RV trial types.
%   - Printed summary statistics showing the median motion energy for RVT and 
%     RV trials per probe, providing a concise numerical comparison.
%
% Usage:
%   Specify the `trial_types` and `probe_ids` variables as needed, then run 
%   the script to analyze and visualize motion energy distributions for each 
%   probe across conditions.


% trial_types                 = {'RVT_gain_up', 'RV_gain_up'}; %{'RVT', 'RV'};
trial_types                 = {{'RVT', 'RVT_gain_up'}, {'RV', 'RV_gain_up'}};

ctl                         = RC2Analysis();
% probe_ids                   = ctl.get_probe_ids('mismatch_nov20', 'mismatch_jul21');
probe_ids                   = ctl.get_probe_ids('visual_flow', 'mismatch_nov20', 'mismatch_jul21');

figure(1)
for probe_i = 1 : length(probe_ids)
    data                    = ctl.load_formatted_data(probe_ids{probe_i});

    distribution_RVT = [];
    trials = data.get_trials_with_trial_group_label(trial_types{1});
    for trial_i = 1 : length(trials)
        trial = trials{trial_i}.to_aligned;
        distribution_RVT = cat(1, distribution_RVT, trial.camera1(trial.motion_mask));
    end

    distribution_RV = [];
    trials = data.get_trials_with_trial_group_label(trial_types{2});
    for trial_i = 1 : length(trials)
        trial = trials{trial_i}.to_aligned;
        distribution_RV = cat(1, distribution_RV, trial.camera1(trial.motion_mask));
    end

    subplot(length(probe_ids), 1, probe_i)
    
    histogram(distribution_RVT)
    hold on
    histogram(distribution_RV)
    legend('RVT', 'RV')
    title(sprintf(probe_ids{probe_i}))
    
    sprintf('Probe: %s, Median RVT: %.2f', probe_ids{probe_i}, median(distribution_RVT))
    sprintf('Median RV: %.2f', median(distribution_RV))   
end