load('Kylie_alone.mat'); % 419
load('all_clicks_together.mat'); % 1717

% Get only ones with db > 140
PotClicks = all_clicks_together([all_clicks_together.Amp]>140);
save('PotClicks.mat', 'PotClicks');

%% VALUES NEEDED
Fs = 500000;
ButtOrd = 6;
FFT = 512;


%% KYLIE clicks
% Alone
% 01 Nov 2017 = 72 (8 clicks per min)
% 19 Jul 2018 = 347 (16 clicks per min)
% Close
% 02 Nov 2016 = 367 (244 clicks per min)
% 03 Nov 2016 = 525
% 04 Sep 2017 = 348
% Together
% 02 Nov 2016 = 75
% 03 Nov 2016 = 39
% 04 Sep 2017 = 326

%% BOOTSTRAP HF
%% Proportion of HF 
% FR = 0 ; low frequency clicks 
% When alone 
LF_KY = bootstrp(5000, @nnz, [Kylie_alone.FR]);
CF_KYCIa = bootci(5000, @nnz, [Kylie_alone.FR]);
meanKFR = mean(LF_KY)/length(Kylie_alone); % 0.2144
sdKFR = std(LF_KY)/length(Kylie_alone); % 0.0202
% When in close proximity
LF_Prox = bootstrp(5000, @nnz, [KylieProx.FR]);
LF_ProxCI = bootci(5000, @nnz, [KylieProx.FR]);
meanProxFR = mean(LF_Prox)/length(KylieProx); % 0.4493 
sdProxFR = std(LF_Prox)/length(KylieProx); % 0.0139

%% TUKEY's TEST
KylieProx = all_clicks_together([all_clicks_together.Porps] == 0);
HFAlone = sum([Kylie_alone.FR] == 1); % 90 out of 419
HFClose = sum([KylieProx.FR] == 1); % 557 out of 1240 

X = [90 419; % alone
    557 1240]; % close
alpha = 0.05;
tmcomptest(X, alpha);

% Group proportions of the interested events are:
% -------------------------------------------
% Group          Proportions
% -------------------------------------------
%   1               0.2148
%   2               0.4492
% -------------------------------------------
%  
% Tukey's multiple comparisions test among proportions, k = 2
% -------------------------------------------------------------------------
%  Comparision      Difference*        SE        q        qc      Decision
% -------------------------------------------------------------------------
%     2  1           14.4283         1.144     12.611     2.772        S
% -------------------------------------------------------------------------
% With a given significance level of: 0.05
% The multiple comparisions can be significant (S) or not significant (NS).
% * After asin transformation of proportions.


%% ENERGY RATIO BETWEEN > AND < 100 KHZ
% Filter out LF
CutOffLLF = 100000;
CutOffRLF = (Fs/2)*0.99;
wnLF = [CutOffLLF CutOffRLF]/(Fs/2);   %design bandpass
[bLF, aLF] = butter(ButtOrd, wnLF);
ClickHF = filter(bLF, aLF, click); % filter the signal

% Filter out HF
CutOffLHF = 2000;
CutOffRHF = 100000;
wnHF = [CutOffLHF CutOffRHF]/(Fs/2);   %design bandpass
[bHF, aHF] = butter(ButtOrd, wnHF);
ClickLF = filter(bHF, aHF, click); % filter the signal
[PSDHF,~] = periodogram(ClickHF,[],FFT,Fs,'power');
[PSDLF,~] = periodogram(ClickLF,[],FFT,Fs,'power');
pwrLF = sum(PSDLF.^2);
pwrHF = sum(PSDHF.^2);


%% MEAN, MEDIAN, and SD (clicks with dB > 140)
Selected = readtable('SelectedClicks.csv');
MeanRatioPorps = mean(Selected.energyratio([Selected.Porps] == 1)); % 22937
MedianRatioPorps = median(Selected.energyratio([Selected.Porps] == 1)); % 4892.3
SDRatioPorps = std(Selected.energyratio([Selected.Porps] == 1)); % 45400

MeanRatioKylieHF = mean(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 1),1)); % 22.43
MedianRatioKylieHF = median(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 1),1)); % 11.04
SDRatioKylieHF = std(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 1),1)); % 35.63

MeanRatioKylieLF = mean(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 0),1)); % 0.438
MedianRatioKylieLF = median(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 0),1)); % 0.29
SDRatioKylieLF = std(Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 0),1)); % 0.432


%% SIMILARITY ANOVA
KYLF = Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 0),1)'; 
KYHF = Selected.energyratio(([Selected.Porps] == 0) & ([Selected.fr] == 1),1)';
Porps = Selected.energyratio([Selected.Porps] == 1,1)';

X1(1:85) = 1; % Kylie alone
X2(1:124) = 2; % Kylie with porpoise
X3(1:48) = 3; % Porpoise

[p,tbl, stats] = anova1([KYLF, KYHF, Porps], [X1, X2, X3]);
% Source       SS        df        MS          F       Prob>F   
% --------------------------------------------------------------
% Groups   2.05117e+10     2   1.02558e+10   26.89   2.55325e-11
% Error    9.68729e+10   254   3.81389e+08                      
% Total    1.17385e+11   256                                                                       

[c,~,~,gnames] = multcompare(stats);

% [1,2,-6467.24,-21.99,6423.24,0.99;
%  1,3,-31199.98,-22936.13,-14672.29,1.18e-09;
%  2,3,-30694.84,-22914.14,-15133.43,9.71e-10]