function independent_parallel(options)
% INDEPENDENT_PARALLEL is the command line version of the baps partition with
% independent models.
% Input: options is a struct generated by parallel.m

%--------------------------------------------------------------------------
%- Syntax check out
%--------------------------------------------------------------------------
outp = [options.outputMat '.txt'];
inp = options.dataFile;

if strcmp(options.fixedK, 'yes')
    fixedK = 1;
else
    fixedK = 0;
end

%--------------------------------------------------------------------------
%- Get data file location
%--------------------------------------------------------------------------
switch options.dataType
    case 'numeric'
        %------------------------------------------------------------------
        %- Get name and index file location
        %------------------------------------------------------------------
        try
            data = load(options.dataFile);
        catch
            disp('*** ERROR: Incorrect numerical text data.');
            return
        end
        ninds = testaaOnkoKunnollinenBapsData(data);  %TESTAUS
        if (ninds==0)
            disp('*** ERROR: Incorrect numerical text data.');
            return;
        end
        
        if ~isempty(options.nameFile) && ~isempty(options.indexFile)
            popnames = initPopNames(options.nameFile{1}, options.indexFile{1});
        else
            popnames = [];
        end
        
        [data, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = handleData(data);
        [Z,dist] = newGetDistances(data,rowsFromInd);
                   
    case 'genepop'
        kunnossa = testaaGenePopData(options.dataFile);
        if kunnossa == 0
            return
        end
        [data,popnames] = lueGenePopData(options.dataFile);
        [data, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = handleData(data);
        [Z,dist] = newGetDistances(data,rowsFromInd);
    case 'matlab'
        struct_array = load(options.dataFile);
        if isfield(struct_array,'c')  %Matlab versio
            c = struct_array.c;
            if ~isfield(c,'dist')
                disp('*** ERROR: Incorrect matlab format');
                return
            end
        elseif isfield(struct_array,'dist')  %Mideva versio
            c = struct_array;
        else
            disp('*** ERROR: Incorrect matlab format');
            return;
        end
        data = double(c.data); rowsFromInd = c.rowsFromInd; alleleCodes = c.alleleCodes;
            noalle = c.noalle; adjprior = c.adjprior; priorTerm = c.priorTerm;
            dist = c.dist; popnames = c.popnames; Z = c.Z;
            clear c;
    otherwise
        error('*** ERROR: data type is not specified or unknown.');
end

% ---------------------------------------------------------
% - Stochastic search algorithm
% ---------------------------------------------------------
global PARTITION; global COUNTS;
global SUMCOUNTS; global POP_LOGML;
clearGlobalVars;

c.data=data; 
c.noalle = noalle; c.adjprior = adjprior; c.priorTerm = priorTerm;
c.dist=dist; c.Z=Z; c.rowsFromInd = rowsFromInd;

ninds = length(unique(data(:,end)));
ekat = (1:rowsFromInd:ninds*rowsFromInd)';
c.rows = [ekat ekat+rowsFromInd-1];

npopstext = [];
npopstextExtra = options.initialK;
if length(npopstextExtra)>=255
    npopstextExtra = npopstextExtra(1:255);
    npopstext = [npopstext ' ' npopstextExtra];
    teksti = 'The input field length limit (255 characters) was reached. Input more values: ';
else
    % -----------------------------------------------------
    % Set the limit of the input value.
    % Modified by Jing Tang, 30.12.2005
    if max(npopstextExtra) > size(data,1)
        error('Initial K larger than the sample size are not accepted. ');
    else
        npopstext = [npopstext ' ' num2str(npopstextExtra)];
    end
end

clear teksti;
if isempty(npopstext) || length(npopstext)==1
    return
else
    npopsTaulu = str2num(npopstext);
    ykkoset = find(npopsTaulu==1);
    npopsTaulu(ykkoset) = [];   % Mikäli ykkösi?annettu ylärajaksi, ne poistetaan.
    if isempty(npopsTaulu)
        return
    end
    clear ykkoset;
end

if fixedK
    % Only the first value of npopsTaulu is used
    npops = npopsTaulu(1);
    nruns = length(npopsTaulu);
    [logml, npops, partitionSummary]=indMix_fixK(c,npops,nruns,1);
else
    [logml, npops, partitionSummary]=indMix(c,npopsTaulu,1);   
end

if logml==1
    return
end

data = noIndex(data,noalle);

changesInLogml = writeMixtureInfo(logml, rowsFromInd, data, adjprior, priorTerm, ...
    outp,inp,partitionSummary, popnames, fixedK);

viewMixPartition(PARTITION, popnames);

if exist('baps4_output.baps','file')
    copyfile('baps4_output.baps',outp)
    delete('baps4_output.baps')
end

% The logml is saved for parallel computing
c.logml = logml;
c.changesInLogml = changesInLogml; % this variable stores the change of likelihoods.

c.PARTITION = PARTITION; c.COUNTS = COUNTS; c.SUMCOUNTS = SUMCOUNTS;
c.alleleCodes = alleleCodes; c.adjprior = adjprior; c.popnames = popnames;
c.rowsFromInd = rowsFromInd; c.data = data; c.npops = npops;
c.noalle = noalle; c.mixtureType = 'mix';
fprintf(1,'Saving the result...')
try
%     save(options.outputMat, 'c');
    save(options.outputMat, 'c', '-v7.3'); % added by Lu Cheng, 08.06.2012
    fprintf(1,'Finished.\n');
catch
    display('*** ERROR in saving the result.');
end


% ---------------------------------------------------------
% - Subfunctions
% ---------------------------------------------------------

%-------------------------------------------------------------------------------------

function clearGlobalVars

global COUNTS; COUNTS = [];
global SUMCOUNTS; SUMCOUNTS = [];
global PARTITION; PARTITION = [];
global POP_LOGML; POP_LOGML = [];


%-------------------------------------------------------------------------------------


function rows = computeRows(rowsFromInd, inds, ninds)
% On annettu yksilöt inds. Funktio palauttaa vektorin, joka
% sisältää niiden rivien numerot, jotka sisältävät yksilöiden
% dataa.

rows = inds(:, ones(1,rowsFromInd));
rows = rows*rowsFromInd;
miinus = repmat(rowsFromInd-1 : -1 : 0, [ninds 1]);
rows = rows - miinus;
rows = reshape(rows', [1,rowsFromInd*ninds]);


%--------------------------------------------------------------------------


function [partitionSummary, added] = addToSummary(logml, partitionSummary, worstIndex)
% Tiedetään, ett?annettu logml on isompi kuin huonoin arvo
% partitionSummary taulukossa. Jos partitionSummary:ss?ei viel?ole
% annettua logml arvoa, niin lisätään worstIndex:in kohtaan uusi logml ja
% nykyist?partitiota vastaava nclusters:in arvo. Muutoin ei tehd?mitään.
global PARTITION;
apu = find(abs(partitionSummary(:,2)-logml)<1e-5);
if isempty(apu)
    % Nyt löydetty partitio ei ole viel?kirjattuna summaryyn.
    npops = length(unique(PARTITION));
    partitionSummary(worstIndex,1) = npops;
    partitionSummary(worstIndex,2) = logml;
    added = 1;
else
    added = 0;
end


%--------------------------------------------------------------------------


function [suurin, i2] = arvoSeuraavaTila(muutokset, logml)
% Suorittaa yksilön seuraavan tilan arvonnan

y = logml + muutokset;  % siirron jälkeiset logml:t
y = y - max(y);
y = exp(y);
summa = sum(y);
y = y/summa;
y = cumsum(y);

i2 = rand_disc(y);   % uusi kori
suurin = muutokset(i2);


%--------------------------------------------------------------------------------------


function svar=rand_disc(CDF)
%returns an index of a value from a discrete distribution using inversion method
slump=rand;
har=find(CDF>slump);
svar=har(1);


%-------------------------------------------------------------------------------------


function updateGlobalVariables(ind, i2, rowsFromInd, diffInCounts, ...
    adjprior, priorTerm)
% Suorittaa globaalien muuttujien muutokset, kun yksil?ind
% on siirretään koriin i2.

global PARTITION; 
global COUNTS; 
global SUMCOUNTS;
global POP_LOGML;

i1 = PARTITION(ind);
PARTITION(ind)=i2;

COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);

POP_LOGML([i1 i2]) = computePopulationLogml([i1 i2], adjprior, priorTerm);


%---------------------------------------------------------------------------------


function updateGlobalVariables2( ...
    i1, i2, rowsFromInd, diffInCounts, adjprior, priorTerm)
% Suorittaa globaalien muuttujien muutokset, kun kaikki
% korissa i1 olevat yksilöt siirretään koriin i2.

global PARTITION; 
global COUNTS; 
global SUMCOUNTS;
global POP_LOGML;

inds = find(PARTITION==i1);
PARTITION(inds) = i2;

COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);

POP_LOGML(i1) = 0;
POP_LOGML(i2) = computePopulationLogml(i2, adjprior, priorTerm);


%------------------------------------------------------------------------------------


function updateGlobalVariables3(muuttuvat, rowsFromInd, diffInCounts, ...
    adjprior, priorTerm, i2)
% Suorittaa globaalien muuttujien päivitykset, kun yksilöt 'muuttuvat'
% siirretään koriin i2. Ennen siirtoa yksilöiden on kuuluttava samaan
% koriin.

global PARTITION;
global COUNTS;
global SUMCOUNTS;
global POP_LOGML;

i1 = PARTITION(muuttuvat(1));
PARTITION(muuttuvat) = i2;

COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);

POP_LOGML([i1 i2]) = computePopulationLogml([i1 i2], adjprior, priorTerm);


%----------------------------------------------------------------------


function inds = returnInOrder(inds, pop, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa yksilöt järjestyksess?siten, ett?ensimmäisen?on
% se, jonka poistaminen populaatiosta pop nostaisi logml:n
% arvoa eniten.

global COUNTS;      global SUMCOUNTS;
ninds = length(inds);
apuTaulu = [inds, zeros(ninds,1)];

for i=1:ninds
    ind = inds(i);
    rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
    diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
    diffInSumCounts = sum(diffInCounts);

    COUNTS(:,:,pop) = COUNTS(:,:,pop)-diffInCounts;
    SUMCOUNTS(pop,:) = SUMCOUNTS(pop,:)-diffInSumCounts;
    apuTaulu(i, 2) = computePopulationLogml(pop, adjprior, priorTerm);
    COUNTS(:,:,pop) = COUNTS(:,:,pop)+diffInCounts;
    SUMCOUNTS(pop,:) = SUMCOUNTS(pop,:)+diffInSumCounts;
end
apuTaulu = sortrows(apuTaulu,2);
inds = apuTaulu(ninds:-1:1,1);

%------------------------------------------------------------------------------------


function [muutokset, diffInCounts] = ...
    laskeMuutokset(ind, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mikäli yksil?ind siirretään koriin i.
% diffInCounts on poistettava COUNTS:in siivusta i1 ja lisättäv?
% COUNTS:in siivuun i2, mikäli muutos toteutetaan.

global COUNTS;      global SUMCOUNTS;
global PARTITION;   global POP_LOGML;
npops = size(COUNTS,3);
muutokset = zeros(npops,1);

i1 = PARTITION(ind);
i1_logml = POP_LOGML(i1);

rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);

COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;

i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2);

COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm);
COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);

muutokset(i2) = new_i1_logml - i1_logml ...
    + new_i2_logml - i2_logml;


%------------------------------------------------------------------------------------


function [muutokset, diffInCounts] = laskeMuutokset2( ...
    i1, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mikäli korin i1 kaikki yksilöt siirretään
% koriin i. 

global COUNTS;      global SUMCOUNTS;
global PARTITION;   global POP_LOGML;
npops = size(COUNTS,3);
muutokset = zeros(npops,1);

i1_logml = POP_LOGML(i1);

inds = find(PARTITION==i1);
ninds = length(inds);

if ninds==0
    diffInCounts = zeros(size(COUNTS,1), size(COUNTS,2));
    return;
end

rows = computeRows(rowsFromInd, inds, ninds);

diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);

COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;

i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2);

COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm);
COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);

muutokset(i2) = new_i1_logml - i1_logml ...
    + new_i2_logml - i2_logml;



%------------------------------------------------------------------------------------


function muutokset = laskeMuutokset3(T2, inds2, rowsFromInd, ...
    data, adjprior, priorTerm, i1)
% Palauttaa length(unique(T2))*npops taulun, jossa (i,j):s alkio
% kertoo, mik?olisi muutos logml:ss? jos populaation i1 osapopulaatio
% inds2(find(T2==i)) siirretään koriin j.

global COUNTS;      global SUMCOUNTS;
global PARTITION;   global POP_LOGML;
npops = size(COUNTS,3);
npops2 = length(unique(T2));
muutokset = zeros(npops2, npops);

i1_logml = POP_LOGML(i1);

for pop2 = 1:npops2
    inds = inds2(find(T2==pop2));
    ninds = length(inds);
    if ninds>0
        rows = computeRows(rowsFromInd, inds, ninds);
        diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
        diffInSumCounts = sum(diffInCounts);

        COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
        SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
        new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
        COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
        SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;

        i2 = [1:i1-1 , i1+1:npops];
        i2_logml = POP_LOGML(i2)';
    
        COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
        SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
        new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm)';
        COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
        SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);

        muutokset(pop2,i2) = new_i1_logml - i1_logml ...
            + new_i2_logml - i2_logml;
    end    
end


%------------------------------------------------------------------------------------

function muutokset = laskeMuutokset5(inds, rowsFromInd, data, adjprior, ...
    priorTerm, i1, i2)

% Palauttaa length(inds)*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mikäli yksil?i vaihtaisi koria i1:n ja i2:n välill?
    
global COUNTS;      global SUMCOUNTS;
global PARTITION;   global POP_LOGML;

ninds = length(inds);
muutokset = zeros(ninds,1);

i1_logml = POP_LOGML(i1);
i2_logml = POP_LOGML(i2);

for i = 1:ninds
    ind = inds(i);
    if PARTITION(ind)==i1
        pop1 = i1;  %mist?
        pop2 = i2;  %mihin
    else
        pop1 = i2;
        pop2 = i1;
    end
    rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
    diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
    diffInSumCounts = sum(diffInCounts);

    COUNTS(:,:,pop1) = COUNTS(:,:,pop1)-diffInCounts;
    SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)-diffInSumCounts;
    COUNTS(:,:,pop2) = COUNTS(:,:,pop2)+diffInCounts;
    SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)+diffInSumCounts;
    
    PARTITION(ind) = pop2;
    
    new_logmls = computePopulationLogml([i1 i2], adjprior, priorTerm);
        
    muutokset(i) = sum(new_logmls);
    
    COUNTS(:,:,pop1) = COUNTS(:,:,pop1)+diffInCounts;
    SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)+diffInSumCounts;
    COUNTS(:,:,pop2) = COUNTS(:,:,pop2)-diffInCounts;
    SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts; 
    
    PARTITION(ind) = pop1;    
end

muutokset = muutokset - i1_logml - i2_logml;

%--------------------------------------------------------------------------



function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data)
% Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien
% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n 
% riveill?rows.

diffInCounts = zeros(max_noalle, nloci);
for i=rows
    row = data(i,:);
    notEmpty = find(row>=0);
    
    if length(notEmpty)>0
        diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) = ...
            diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) + 1;
    end
end    



%------------------------------------------------------------------------------------


function popLogml = computePopulationLogml(pops, adjprior, priorTerm)
% Palauttaa length(pops)*1 taulukon, jossa on laskettu korikohtaiset
% logml:t koreille, jotka on määritelty pops-muuttujalla.

global COUNTS;
global SUMCOUNTS;
x = size(COUNTS,1);
y = size(COUNTS,2);
z = length(pops);

popLogml = ...
    squeeze(sum(sum(reshape(...
    gammaln(repmat(adjprior,[1 1 length(pops)]) + COUNTS(:,:,pops)) ...
    ,[x y z]),1),2)) - sum(gammaln(1+SUMCOUNTS(pops,:)),2) - priorTerm;


%-----------------------------------------------------------------------------------


function npops = poistaTyhjatPopulaatiot(npops)
% Poistaa tyhjentyneet populaatiot COUNTS:ista ja 
% SUMCOUNTS:ista. Päivittää npops:in ja PARTITION:in.

global COUNTS;
global SUMCOUNTS;
global PARTITION;

notEmpty = find(any(SUMCOUNTS,2));
COUNTS = COUNTS(:,:,notEmpty);
SUMCOUNTS = SUMCOUNTS(notEmpty,:);

for n=1:length(notEmpty)
    apu = find(PARTITION==notEmpty(n));
    PARTITION(apu)=n;
end
npops = length(notEmpty);


%----------------------------------------------------------------------------------
%Seuraavat kolme funktiota liittyvat alkupartition muodostamiseen.

function initial_partition=admixture_initialization(data_matrix,nclusters,Z)
size_data=size(data_matrix);
nloci=size_data(2)-1;
n=max(data_matrix(:,end));
T=cluster_own(Z,nclusters);
initial_partition=zeros(size_data(1),1);
for i=1:n
    kori=T(i);
    here=find(data_matrix(:,end)==i);
    for j=1:length(here)
        initial_partition(here(j),1)=kori;
    end
end

function T = cluster_own(Z,nclust)
true=logical(1);
false=logical(0);
maxclust = nclust;
% Start of algorithm
m = size(Z,1)+1;
T = zeros(m,1);
   % maximum number of clusters based on inconsistency
   if m <= maxclust
      T = (1:m)';
   elseif maxclust==1
      T = ones(m,1);
   else
      clsnum = 1;
      for k = (m-maxclust+1):(m-1)
         i = Z(k,1); % left tree
         if i <= m % original node, no leafs
            T(i) = clsnum;
            clsnum = clsnum + 1;
         elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
            T = clusternum(Z, T, i-m, clsnum);
            clsnum = clsnum + 1;
         end
         i = Z(k,2); % right tree
         if i <= m  % original node, no leafs
            T(i) = clsnum;
            clsnum = clsnum + 1;
         elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
            T = clusternum(Z, T, i-m, clsnum);
            clsnum = clsnum + 1;
         end
      end
   end
   
function T = clusternum(X, T, k, c)
m = size(X,1)+1;
while(~isempty(k))
   % Get the children of nodes at this level
   children = X(k,1:2);
   children = children(:);

   % Assign this node number to leaf children
   t = (children<=m);
   T(children(t)) = c;
   
   % Move to next level
   k = children(~t) - m;
end


%---------------------------------------------------------------------------------------


function [newData, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = ...
    handleData(raw_data)
% Alkuperäisen datan viimeinen sarake kertoo, milt?yksilölt?
% kyseinen rivi on peräisin. Funktio tutkii ensin, ett?montako
% rivi?maksimissaan on peräisin yhdelt?yksilölt? jolloin saadaan
% tietää onko kyseess?haploidi, diploidi jne... Tämän jälkeen funktio
% lisää tyhji?rivej?niille yksilöille, joilta on peräisin vähemmän
% rivej?kuin maksimimäär?
%   Mikäli jonkin alleelin koodi on =0, funktio muuttaa tämän alleelin
% koodi pienimmäksi koodiksi, joka isompi kuin mikään käytöss?oleva koodi.
% Tämän jälkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen j
% koodit saavat arvoja välill?1,...,noalle(j).
data = raw_data;
nloci=size(raw_data,2)-1;

dataApu = data(:,1:nloci);
nollat = find(dataApu==0);
if ~isempty(nollat)
   isoinAlleeli = max(max(dataApu));
   dataApu(nollat) = isoinAlleeli+1;
   data(:,1:nloci) = dataApu;
end
dataApu = []; nollat = []; isoinAlleeli = [];

noalle=zeros(1,nloci);
alleelitLokuksessa = cell(nloci,1);
for i=1:nloci
    alleelitLokuksessaI = unique(data(:,i));
    alleelitLokuksessa{i,1} = alleelitLokuksessaI(find(alleelitLokuksessaI>=0));
    noalle(i) = length(alleelitLokuksessa{i,1});
end
alleleCodes = zeros(max(noalle),nloci);
for i=1:nloci
    alleelitLokuksessaI = alleelitLokuksessa{i,1};
    puuttuvia = max(noalle)-length(alleelitLokuksessaI);
    alleleCodes(:,i) = [alleelitLokuksessaI; zeros(puuttuvia,1)];
end

for loc = 1:nloci
    for all = 1:noalle(loc)
        data(find(data(:,loc)==alleleCodes(all,loc)), loc)=all;
    end;
end;

nind = max(data(:,end));
nrows = size(data,1);
ncols = size(data,2);
rowsFromInd = zeros(nind,1);
for i=1:nind
    rowsFromInd(i) = length(find(data(:,end)==i));
end
maxRowsFromInd = max(rowsFromInd);
a = -999;
emptyRow = repmat(a, 1, ncols);
lessThanMax = find(rowsFromInd < maxRowsFromInd);
missingRows = maxRowsFromInd*nind - nrows;
data = [data; zeros(missingRows, ncols)];
pointer = 1;
for ind=lessThanMax'    %Käy läpi ne yksilöt, joilta puuttuu rivej?
    miss = maxRowsFromInd-rowsFromInd(ind);  % Tält?yksilölt?puuttuvien lkm.
    for j=1:miss
        rowToBeAdded = emptyRow;
        rowToBeAdded(end) = ind;
        data(nrows+pointer, :) = rowToBeAdded;
        pointer = pointer+1;
    end
end
data = sortrows(data, ncols);   % Sorttaa yksilöiden mukaisesti
newData = data;
rowsFromInd = maxRowsFromInd;

adjprior = zeros(max(noalle),nloci);
priorTerm = 0;
for j=1:nloci
    adjprior(:,j) = [repmat(1/noalle(j), [noalle(j),1]) ; ones(max(noalle)-noalle(j),1)];
    priorTerm = priorTerm + noalle(j)*gammaln(1/noalle(j)); 
end


%----------------------------------------------------------------------------------------


function [Z, dist] = newGetDistances(data, rowsFromInd)

ninds = max(data(:,end));
nloci = size(data,2)-1;
riviLkm = nchoosek(ninds,2);

empties = find(data<0);
data(empties)=0;
data = uint8(data);   % max(noalle) oltava <256

pariTaulu = zeros(riviLkm,2);
aPointer=1;
for a=1:ninds-1
    pariTaulu(aPointer:aPointer+ninds-1-a,1) = ones(ninds-a,1)*a;
    pariTaulu(aPointer:aPointer+ninds-1-a,2) = (a+1:ninds)';
    aPointer = aPointer+ninds-a;
end 

eka = pariTaulu(:,ones(1,rowsFromInd));
eka = eka * rowsFromInd;
miinus = repmat(rowsFromInd-1 : -1 : 0, [riviLkm 1]);
eka = eka - miinus;

toka = pariTaulu(:,ones(1,rowsFromInd)*2);
toka = toka * rowsFromInd;
toka = toka - miinus;

%eka = uint16(eka);
%toka = uint16(toka);

summa = zeros(riviLkm,1);
vertailuja = zeros(riviLkm,1);

clear pariTaulu; clear miinus;

x = zeros(size(eka));    x = uint8(x);
y = zeros(size(toka));   y = uint8(y);

for j=1:nloci;
    
    for k=1:rowsFromInd
        x(:,k) = data(eka(:,k),j);
        y(:,k) = data(toka(:,k),j);
    end
        
    for a=1:rowsFromInd
        for b=1:rowsFromInd
            vertailutNyt = double(x(:,a)>0 & y(:,b)>0);
            vertailuja = vertailuja + vertailutNyt;
            lisays = (x(:,a)~=y(:,b) & vertailutNyt);
            summa = summa+double(lisays);
        end
    end
end

clear x;    clear y;   clear vertailutNyt;
nollat = find(vertailuja==0);
dist = zeros(length(vertailuja),1);
dist(nollat) = 1;
muut = find(vertailuja>0);
dist(muut) = summa(muut)./vertailuja(muut);
clear summa; clear vertailuja;

Z = linkage(dist');


%----------------------------------------------------------------------------------------


function [Z, distances]= getDistances(data_matrix,nclusters)

%finds initial admixture clustering solution with nclusters clusters, uses simple mean Hamming distance
%gives partition in 8-bit format
%allocates all alleles of a single individual into the same basket
%data_matrix contains #Loci+1 columns, last column indicate whose alleles are placed in each row,
%i.e. ranges from 1 to #individuals. For diploids there are 2 rows per individual, for haploids only a single row
%missing values are indicated by zeros in the partition and by negative integers in the data_matrix.

size_data=size(data_matrix);
nloci=size_data(2)-1;
n=max(data_matrix(:,end));
distances=zeros(nchoosek(n,2),1);
pointer=1;
for i=1:n-1
    i_data=data_matrix(find(data_matrix(:,end)==i),1:nloci);
    for j=i+1:n
        d_ij=0;
        j_data=data_matrix(find(data_matrix(:,end)==j),1:nloci);
        vertailuja = 0;
        for k=1:size(i_data,1)
            for l=1:size(j_data,1)
                here_i=find(i_data(k,:)>=0);
                here_j=find(j_data(l,:)>=0);
                here_joint=intersect(here_i,here_j);
                vertailuja = vertailuja + length(here_joint);
                d_ij = d_ij + length(find(i_data(k,here_joint)~=j_data(l,here_joint)));
            end
        end
        d_ij = d_ij / vertailuja;
        distances(pointer)=d_ij;
        pointer=pointer+1;
    end
end

Z=linkage(distances');



%----------------------------------------------------------------------------------------


function Z = linkage(Y, method)
[k, n] = size(Y);
m = (1+sqrt(1+8*n))/2;
if k ~= 1 || m ~= fix(m)
  error('The first input has to match the output of the PDIST function in size.');   
end
if nargin == 1 % set default switch to be 'co' 
   method = 'co';
end
method = lower(method(1:2)); % simplify the switch string.
monotonic = 1;
Z = zeros(m-1,3); % allocate the output matrix.
N = zeros(1,2*m-1);
N(1:m) = 1;
n = m; % since m is changing, we need to save m in n. 
R = 1:n;
for s = 1:(n-1)
   X = Y;
   [v, k] = min(X);
   i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
   j = k - (i-1)*(m-i/2)+i;
   Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A   
   I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
   U = [I1 I2 I3];
   I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
   J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
   
   switch method
   case 'si' %single linkage
      Y(I) = min(Y(I),Y(J));
   case 'av' % average linkage
      Y(I) = Y(I) + Y(J);
   case 'co' %complete linkage
      Y(I) = max(Y(I),Y(J));
   case 'ce' % centroid linkage
      K = N(R(i))+N(R(j));
      Y(I) = (N(R(i)).*Y(I)+N(R(j)).*Y(J)-(N(R(i)).*N(R(j))*v^2)./K)./K;
   case 'wa'
      Y(I) = ((N(R(U))+N(R(i))).*Y(I) + (N(R(U))+N(R(j))).*Y(J) - ...
	  N(R(U))*v)./(N(R(i))+N(R(j))+N(R(U)));
   end
   J = [J i*(m-(i+1)/2)-m+j];
   Y(J) = []; % no need for the cluster information about j.
   
   % update m, N, R
   m = m-1; 
   N(n+s) = N(R(i)) + N(R(j));
   R(i) = n+s;
   R(j:(n-1))=R((j+1):n); 
end


%-----------------------------------------------------------------------------------


function popnames = initPopNames(nameFile, indexFile)
%Palauttaa tyhjän, mikäli nimitiedosto ja indeksitiedosto
% eivät olleet yht?pitki?

popnames = [];
indices = load(indexFile);

fid = fopen(nameFile);
if fid == -1
    %File didn't exist
    msgbox('Loading of the population names was unsuccessful', ...
        'Error', 'error');
    return;
end;
line = fgetl(fid);
counter = 1;
while (line ~= -1) && ~isempty(line)
    names{counter} = line;
    line = fgetl(fid);
    counter = counter + 1;
end;
fclose(fid);

if length(names) ~= length(indices)
    disp('The number of population names must be equal to the number of ');
    disp('entries in the file specifying indices of the first individuals of ');
    disp('each population.');
    return;
end

popnames = cell(length(names), 2);
for i = 1:length(names)
    popnames{i,1} = names(i);
    popnames{i,2} = indices(i);
end


%-----------------------------------------------------------------------------------


function [sumcounts, counts, logml] = ...
    initialCounts(partition, data, npops, rowsFromInd, noalle)

nloci=size(data,2);
ninds = size(data,1)/rowsFromInd;

counts = zeros(max(noalle),nloci,npops);
sumcounts = zeros(npops,nloci);
for i=1:npops
    for j=1:nloci
        havainnotLokuksessa = find(partition==i & data(:,j)>=0);
        sumcounts(i,j) = length(havainnotLokuksessa);
        for k=1:noalle(j)
            alleleCode = k;
            N_ijk = length(find(data(havainnotLokuksessa,j)==alleleCode));
            counts(k,j,i) = N_ijk;
        end
    end
end

initializeGammaln(ninds, rowsFromInd, max(noalle));

logml = computeLogml(counts, sumcounts, noalle, data, rowsFromInd);


%-----------------------------------------------------------------------


function logml=computeLogml(counts, sumcounts, noalle, data, rowsFromInd)
nloci = size(counts,2);
npops = size(counts,3);
adjnoalle = zeros(max(noalle),nloci);
for j=1:nloci
    adjnoalle(1:noalle(j),j)=noalle(j);
    if noalle(j)<max(noalle)
        adjnoalle(noalle(j)+1:end,j)=1;
	end       
end

%logml2 = sum(sum(sum(gammaln(counts+repmat(adjprior,[1 1 npops]))))) ...
%    - npops*sum(sum(gammaln(adjprior))) - ...
%    sum(sum(gammaln(1+sumcounts)));
%logml = logml2;

global GAMMA_LN;
rowsInG = size(data,1)+rowsFromInd;

logml = sum(sum(sum(GAMMA_LN(counts+1 + repmat(rowsInG*(adjnoalle-1),[1 1 npops]))))) ...
    - npops*sum(sum(GAMMA_LN(1, adjnoalle))) ...
    -sum(sum(GAMMA_LN(sumcounts+1,1)));


%--------------------------------------------------------------------------


function initializeGammaln(ninds, rowsFromInd, maxAlleles)
%Alustaa GAMMALN muuttujan s.e. GAMMALN(i,j)=gammaln((i-1) + 1/j)
global GAMMA_LN;
GAMMA_LN = zeros((1+ninds)*rowsFromInd, maxAlleles);
for i=1:(ninds+1)*rowsFromInd
    for j=1:maxAlleles
        GAMMA_LN(i,j)=gammaln((i-1) + 1/j);
    end
end

%-------------------------------------------------------------------


function changesInLogml = writeMixtureInfo(logml, rowsFromInd, data, adjprior, ...
    priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK)

global PARTITION;
global COUNTS;
global SUMCOUNTS;
global LOGDIFF;

ninds = size(data,1)/rowsFromInd;
npops =  size(COUNTS,3);
names = (size(popnames,1) == ninds);    %Tarkistetaan ett?nimet viittaavat yksilöihin

if length(outPutFile)>0
    fid = fopen(outPutFile,'w');
else
    fid = -1;
    diary('baps4_output.baps'); % save in text anyway.
end

dispLine;
disp('RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:');
disp(['Data file: ' inputFile]);
disp(['Model: independent']);
disp(['Number of clustered individuals: ' ownNum2Str(ninds)]);
disp(['Number of groups in optimal partition: ' ownNum2Str(npops)]);
disp(['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
disp(' ');
if (fid ~= -1)
    fprintf(fid,'%s \n', ['RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:']); fprintf(fid,'\n');
    fprintf(fid,'%s \n', ['Data file: ' inputFile]); fprintf(fid,'\n');
    fprintf(fid,'%s \n', ['Number of clustered individuals: ' ownNum2Str(ninds)]); fprintf(fid,'\n');
    fprintf(fid,'%s \n', ['Number of groups in optimal partition: ' ownNum2Str(npops)]); fprintf(fid,'\n');
    fprintf(fid,'%s \n', ['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]); fprintf(fid,'\n');
end

cluster_count = length(unique(PARTITION));
disp(['Best Partition: ']);
if (fid ~= -1)
    fprintf(fid,'%s \n',['Best Partition: ']); fprintf(fid,'\n');
end
for m=1:cluster_count
    indsInM = find(PARTITION==m);
    length_of_beginning = 11 + floor(log10(m));
    cluster_size = length(indsInM);
    
    if names
        text = ['Cluster ' num2str(m) ': {' char(popnames{indsInM(1)})];
        for k = 2:cluster_size
            text = [text ', ' char(popnames{indsInM(k)})];
        end;    
    else
        text = ['Cluster ' num2str(m) ': {' num2str(indsInM(1))];
        for k = 2:cluster_size
            text = [text ', ' num2str(indsInM(k))];
        end;
    end
    text = [text '}'];
    while length(text)>58
        %Take one line and display it.
        new_line = takeLine(text,58);
        text = text(length(new_line)+1:end);
        disp(new_line);
        if (fid ~= -1)
            fprintf(fid,'%s \n',[new_line]);
            fprintf(fid,'\n');
        end
        if length(text)>0
            text = [blanks(length_of_beginning) text];
        else
            text = [];
        end;
    end;
    if ~isempty(text)
        disp(text);
        if (fid ~= -1)
            fprintf(fid,'%s \n',[text]);
            fprintf(fid,'\n');
        end
    end;
end

if npops > 1
    disp(' ');
    disp(' ');
    disp('Changes in log(marginal likelihood) if indvidual i is moved to group j:');
    if (fid ~= -1)
        fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
        fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
        fprintf(fid, '%s \n', ['Changes in log(marginal likelihood) if indvidual i is moved to group j:']); fprintf(fid, '\n');
    end

    if names
        nameSizes = zeros(ninds,1);
        for i = 1:ninds
            nimi = char(popnames{i});
            nameSizes(i) = length(nimi);
        end
        maxSize = max(nameSizes);
        maxSize = max(maxSize, 5);
        erotus = maxSize - 5;
        alku = blanks(erotus);
        ekarivi = [alku '  ind' blanks(6+erotus)];
    else
        ekarivi = '  ind      ';
    end
    for i = 1:cluster_count
        ekarivi = [ekarivi ownNum2Str(i) blanks(8-floor(log10(i)))];
    end
    disp(ekarivi);
    if (fid ~= -1)
        fprintf(fid, '%s \n', [ekarivi]); fprintf(fid, '\n');
    end

    %ninds = size(data,1)/rowsFromInd;
    changesInLogml = LOGDIFF';
    for ind = 1:ninds
        %[muutokset, diffInCounts] = laskeMuutokset(ind, rowsFromInd, data, ...
        %    adjprior, priorTerm);
        %changesInLogml(:,ind) = muutokset;
        muutokset = changesInLogml(:,ind);
        if names
            nimi = char(popnames{ind});
            rivi = [blanks(maxSize - length(nimi)) nimi ':'];
        else
            rivi = [blanks(4-floor(log10(ind))) ownNum2Str(ind) ':'];
        end
        for j = 1:npops
            rivi = [rivi '  ' logml2String(omaRound(muutokset(j)))];
        end
        disp(rivi);
        if (fid ~= -1)
            fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
        end
    end

    disp(' '); disp(' ');
    disp('KL-divergence matrix in PHYLIP format:');
    
    dist_mat = zeros(npops, npops);
    if (fid ~= -1)
        fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
        fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
        fprintf(fid, '%s \n', ['KL-divergence matrix in PHYLIP format:']); %fprintf(fid, '\n');
    end

    maxnoalle = size(COUNTS,1);
    nloci = size(COUNTS,2);
    d = zeros(maxnoalle, nloci, npops);
    prior = adjprior;
    prior(find(prior==1))=0;
    nollia = find(all(prior==0));  %Lokukset, joissa oli havaittu vain yht?alleelia.
    prior(1,nollia)=1;
    for pop1 = 1:npops
        d(:,:,pop1) = (squeeze(COUNTS(:,:,pop1))+prior) ./ repmat(sum(squeeze(COUNTS(:,:,pop1))+prior),maxnoalle,1);
        %dist1(pop1) = (squeeze(COUNTS(:,:,pop1))+adjprior) ./ repmat((SUMCOUNTS(pop1,:)+adjprior), maxnoalle, 1);
    end
%     ekarivi = blanks(7);
%     for pop = 1:npops
%         ekarivi = [ekarivi num2str(pop) blanks(7-floor(log10(pop)))];
%     end
    ekarivi = num2str(npops);
    disp(ekarivi);
    if (fid ~= -1)
        fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
    end

    for pop1 = 1:npops
        % rivi = [blanks(2-floor(log10(pop1))) num2str(pop1) '  '];
        for pop2 = 1:pop1-1
            dist1 = d(:,:,pop1); dist2 = d(:,:,pop2);
            div12 = sum(sum(dist1.*log2((dist1+10^-10) ./ (dist2+10^-10))))/nloci;
            div21 = sum(sum(dist2.*log2((dist2+10^-10) ./ (dist1+10^-10))))/nloci;
            div = (div12+div21)/2;
            % rivi = [rivi kldiv2str(div) '  '];
            dist_mat(pop1,pop2) = div;
        end
        % disp(rivi);
        % if (fid ~= -1)
        %    fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
        % end
    end
    
end
dist_mat = dist_mat + dist_mat'; % make it symmetric
for pop1 = 1:npops
    rivi = ['Cluster_' num2str(pop1) ' '];
    for pop2 = 1:npops
        rivi = [rivi kldiv2str(dist_mat(pop1,pop2)) ' '];
    end
    disp(rivi);
    if (fid ~= -1)
         fprintf(fid, '%s \n', [rivi]); %fprintf(fid, '\n');
    end
end

disp(' ');
disp(' ');
disp('List of sizes of 10 best visited partitions and corresponding log(ml) values');

if (fid ~= -1)
    fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
    fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
    fprintf(fid, '%s \n', ['List of sizes of 10 best visited partitions and corresponding log(ml) values']); fprintf(fid, '\n');
end

partitionSummary = sortrows(partitionSummary,2);
partitionSummary = partitionSummary(size(partitionSummary,1):-1:1 , :);
partitionSummary = partitionSummary(find(partitionSummary(:,2)>-1e49),:);
if size(partitionSummary,1)>10
    vikaPartitio = 10;
else
    vikaPartitio = size(partitionSummary,1);
end
for part = 1:vikaPartitio
    line = [num2str(partitionSummary(part,1)) '    ' num2str(partitionSummary(part,2))];
    disp(line);
    if (fid ~= -1)
        fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
    end
end

if ~fixedK
    disp(' ');
    disp(' ');
    disp('Probabilities for number of clusters');

    if (fid ~= -1)
        fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
        fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
        fprintf(fid, '%s \n', ['Probabilities for number of clusters']); fprintf(fid, '\n');
    end

    npopsTaulu = unique(partitionSummary(:,1));
    len = length(npopsTaulu);
    probs = zeros(len,1);
    partitionSummary(:,2) = partitionSummary(:,2)-max(partitionSummary(:,2));
    sumtn = sum(exp(partitionSummary(:,2)));
    for i=1:len
        npopstn = sum(exp(partitionSummary(find(partitionSummary(:,1)==npopsTaulu(i)),2)));
        probs(i) = npopstn / sumtn;
    end
    for i=1:len
        if probs(i)>1e-5
            line = [num2str(npopsTaulu(i)) '   ' num2str(probs(i))];
            disp(line);
            if (fid ~= -1)
                fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
            end
        end
    end
end

if (fid ~= -1)
    fclose(fid);
else
    diary off
end


%---------------------------------------------------------------


function dispLine
disp('---------------------------------------------------');

%--------------------------------------------------------------

function num2 = omaRound(num)
% Pyöristää luvun num 1 desimaalin tarkkuuteen
num = num*10;
num = round(num);
num2 = num/10;

%---------------------------------------------------------

function digit = palautaYks(num,yks)
% palauttaa luvun num 10^yks termin kertoimen
% string:in?
% yks täytyy olla kokonaisluku, joka on 
% vähintään -1:n suuruinen. Pienemmill?
% luvuilla tapahtuu jokin pyöristysvirhe.

if yks>=0
    digit = rem(num, 10^(yks+1));
    digit = floor(digit/(10^yks));
else
    digit = num*10;
    digit = floor(rem(digit,10));
end
digit = num2str(digit);


function mjono = kldiv2str(div)
mjono = '      ';
if abs(div)<100
    %Ei tarvita e-muotoa
    mjono(6) = num2str(rem(floor(div*1000),10));
    mjono(5) = num2str(rem(floor(div*100),10));
    mjono(4) = num2str(rem(floor(div*10),10));
    mjono(3) = '.';
    mjono(2) = num2str(rem(floor(div),10));
    arvo = rem(floor(div/10),10);
    if arvo>0
        mjono(1) = num2str(arvo);
    end
    
else
    suurinYks = floor(log10(div));
    mjono(6) = num2str(suurinYks);
    mjono(5) = 'e';
    mjono(4) = palautaYks(abs(div),suurinYks-1);
    mjono(3) = '.';
    mjono(2) = palautaYks(abs(div),suurinYks);
end

%--------------------------------------------------------------------


function newline = takeLine(description,width)
%Returns one line from the description: line ends to the first
%space after width:th mark.
newLine = description(1:width);
n = width+1;
while ~isspace(description(n)) && n<length(description)
    n = n+1;
end;
newline = description(1:n);

%-------------------------------------------------------

function pal = testaaPop(rivi)
% pal=1, mikäli rivi alkaa jollain seuraavista
% kirjainyhdistelmist? Pop, pop, POP. Kaikissa muissa
% tapauksissa pal=0.

if length(rivi)<3 
    pal = 0;
    return
end
if (all(rivi(1:3)=='Pop') || ...
    all(rivi(1:3)=='pop') || ...
    all(rivi(1:3)=='POP'))
    pal = 1;    
    return
else 
    pal = 0;
    return
end



%----------------------------------------------------------------------------


function dist2 = laskeOsaDist(inds2, dist, ninds)
% Muodostaa dist vektorista osavektorin, joka sisältää yksilöiden inds2
% väliset etäisyydet. ninds=kaikkien yksilöiden lukumäär?

ninds2 = length(inds2);
apu = zeros(nchoosek(ninds2,2),2);
rivi = 1;
for i=1:ninds2-1
    for j=i+1:ninds2
        apu(rivi, 1) = inds2(i);
        apu(rivi, 2) = inds2(j);
        rivi = rivi+1;
    end
end
apu = (apu(:,1)-1).*ninds - apu(:,1) ./ 2 .* (apu(:,1)-1) + (apu(:,2)-apu(:,1));
dist2 = dist(apu);

%--------------------------------------------------------

function ninds = testaaOnkoKunnollinenBapsData(data)
%Tarkastaa onko viimeisess?sarakkeessa kaikki
%luvut 1,2,...,n johonkin n:ään asti.
%Tarkastaa lisäksi, ett?on vähintään 2 saraketta.
if size(data,1)<2
    ninds = 0; return;
end
lastCol = data(:,end);
ninds = max(lastCol);
if ~isequal((1:ninds)',unique(lastCol))
    ninds = 0; return;
end

%--------------------------------------------------------------------------

function [emptyPop, pops] = findEmptyPop(npops)
% Palauttaa ensimmäisen tyhjän populaation indeksin. Jos tyhji?
% populaatioita ei ole, palauttaa -1:n.

global PARTITION;
pops = unique(PARTITION)';
if (length(pops) ==npops)
    emptyPop = -1;
else
    popDiff = diff([0 pops npops+1]);
    emptyPop = min(find(popDiff > 1));
end

%--------------------------------------------------------------------


function kunnossa = testaaGenePopData(tiedostonNimi)
% kunnossa == 0, jos data ei ole kelvollinen genePop data.
% Muussa tapauksessa kunnossa == 1.

kunnossa = 0;
fid = fopen(tiedostonNimi);
line1 = fgetl(fid);  %ensimmäinen rivi
line2 = fgetl(fid);  %toinen rivi
line3 = fgetl(fid);  %kolmas

if (isequal(line1,-1) || isequal(line2,-1) || isequal(line3,-1))
    disp('Incorrect file format 1168'); fclose(fid);
    return 
end
if (testaaPop(line1)==1 || testaaPop(line2)==1)
    disp('Incorrect file format 1172'); fclose(fid);
    return
end
if testaaPop(line3)==1
    %2 rivi tällöin lokusrivi
    nloci = rivinSisaltamienMjonojenLkm(line2);
    line4 = fgetl(fid);
    if isequal(line4,-1)
        disp('Incorrect file format 1180'); fclose(fid);
        return 
    end
    if ~any(line4==',')
        % Rivin nelj?täytyy sisältää pilkku.
        disp('Incorrect file format 1185'); fclose(fid);
        return
    end
    pointer = 1;
    while ~isequal(line4(pointer),',')  %Tiedetään, ett?pysähtyy
        pointer = pointer+1;
    end
    line4 = line4(pointer+1:end);  %pilkun jälkeinen osa
    nloci2 = rivinSisaltamienMjonojenLkm(line4);
    if (nloci2~=nloci)
        disp('Incorrect file format 1195'); fclose(fid);
        return
    end
else
    line = fgetl(fid);
    lineNumb = 4;
    while (testaaPop(line)~=1 && ~isequal(line,-1))
        line = fgetl(fid);
        lineNumb = lineNumb+1;    
    end
    if isequal(line,-1)
        disp('Incorrect file format 1206'); fclose(fid);
        return
    end
    nloci = lineNumb-2;
    line4 = fgetl(fid);  %Eka rivi pop sanan jälkeen
    if isequal(line4,-1)
        disp('Incorrect file format 1212'); fclose(fid);
        return 
    end
    if ~any(line4==',')
        % Rivin täytyy sisältää pilkku.
        disp('Incorrect file format 1217'); fclose(fid);
        return
    end
    pointer = 1;
    while ~isequal(line4(pointer),',')  %Tiedetään, ett?pysähtyy.
        pointer = pointer+1;
    end
 
    line4 = line4(pointer+1:end);  %pilkun jälkeinen osa
    nloci2 = rivinSisaltamienMjonojenLkm(line4);
    if (nloci2~=nloci)
        disp('Incorrect file format 1228'); fclose(fid);
        return
    end
end
kunnossa = 1;
fclose(fid);

%------------------------------------------------------
    

function [data, popnames] = lueGenePopData(tiedostonNimi)

fid = fopen(tiedostonNimi);
line = fgetl(fid);  %ensimmäinen rivi
line = fgetl(fid);  %toinen rivi
count = rivinSisaltamienMjonojenLkm(line);

line = fgetl(fid);
lokusRiveja = 1;
while (testaaPop(line)==0)
    lokusRiveja = lokusRiveja+1;
    line = fgetl(fid);
end

if lokusRiveja>1
    nloci = lokusRiveja;
else
    nloci = count;
end

popnames = cell(10,2);
data = zeros(100, nloci+1);
nimienLkm=0;
ninds=0;
poimiNimi=1;
digitFormat = -1;
while line ~= -1
    line = fgetl(fid);
    
    if poimiNimi==1  
        %Edellinen rivi oli 'pop'
        nimienLkm = nimienLkm+1;
        ninds = ninds+1;
        if nimienLkm>size(popnames,1);
            popnames = [popnames; cell(10,2)];
        end
        nimi = lueNimi(line);
        if digitFormat == -1
            digitFormat = selvitaDigitFormat(line);
            divider = 10^digitFormat;
        end
        popnames{nimienLkm, 1} = {nimi};   %Näin se on greedyMix:issäkin?!?
        popnames{nimienLkm, 2} = ninds;
        poimiNimi=0;
        
        data = addAlleles(data, ninds, line, divider);
        
    elseif testaaPop(line)
        poimiNimi = 1;
        
    elseif line ~= -1
        ninds = ninds+1;
        data = addAlleles(data, ninds, line, divider);
    end    
end

data = data(1:ninds*2,:);
popnames = popnames(1:nimienLkm,:);
fclose(fid);

%--------------------------------------------------------


function data = addAlleles(data, ind, line, divider)
% Lisaa BAPS-formaatissa olevaan datataulukkoon
% yksilöä ind vastaavat rivit. Yksilön alleelit
% luetaan genepop-formaatissa olevasta rivist?
% line. Jos data on 3 digit formaatissa on divider=1000.
% Jos data on 2 digit formaatissa on divider=100.

nloci = size(data,2)-1;
if size(data,1) < 2*ind
    data = [data; zeros(100,nloci+1)];
end

k=1;
merkki=line(k);
while ~isequal(merkki,',')
   k=k+1;
   merkki=line(k);
end
line = line(k+1:end);
clear k; clear merkki;

alleeliTaulu = sscanf(line,'%d');

if length(alleeliTaulu)~=nloci
    disp('Incorrect data format.');
end

for j=1:nloci
    ekaAlleeli = floor(alleeliTaulu(j)/divider);
    if ekaAlleeli==0 
        ekaAlleeli=-999; 
    end
    tokaAlleeli = rem(alleeliTaulu(j),divider);
    if tokaAlleeli==0 
        tokaAlleeli=-999; 
    end
    
    data(2*ind-1,j) = ekaAlleeli;
    data(2*ind,j) = tokaAlleeli;
end

data(2*ind-1,end) = ind;
data(2*ind,end) = ind;

%------------------------------------------------------


function count = rivinSisaltamienMjonojenLkm(line)
% Palauttaa line:n sisältämien mjonojen lukumäärän.
% Mjonojen väliss?täytyy olla välilyönti.
count = 0;
pit = length(line);
tila = 0;    %0, jos odotetaan välilyöntej? 1 jos odotetaan muita merkkej?
for i=1:pit
    merkki = line(i);
    if (isspace(merkki) && tila==0) 
        %Ei tehd?mitään.
    elseif (isspace(merkki) && tila==1)
        tila = 0;
    elseif (~isspace(merkki) && tila==0)
        tila = 1;
        count = count+1;
    elseif (~isspace(merkki) && tila==1)
        %Ei tehd?mitään
    end
end


%-------------------------------------------------------

function nimi = lueNimi(line)
%Palauttaa line:n alusta sen osan, joka on ennen pilkkua.
n = 1;
merkki = line(n);
nimi = '';
while ~isequal(merkki,',')
    nimi = [nimi merkki];
    n = n+1;
    merkki = line(n);
end

%-------------------------------------------------------

function df = selvitaDigitFormat(line)
% line on ensimmäinen pop-sanan jälkeinen rivi
% Genepop-formaatissa olevasta datasta. funktio selvittää
% rivin muodon perusteella, ovatko datan alleelit annettu
% 2 vai 3 numeron avulla.

n = 1;
merkki = line(n);
while ~isequal(merkki,',')
    n = n+1;
    merkki = line(n);
end

while ~any(merkki == '0123456789');
    n = n+1;
    merkki = line(n);
end
numeroja = 0;
while any(merkki == '0123456789');
    numeroja = numeroja+1;
    n = n+1;
    merkki = line(n);
end

df = numeroja/2;