#ifndef COLA_H

#define COLA_H

#include <utility>

#include <iterator>

#include <vector>

#include <algorithm>

#include <cmath>

#include <iostream>

#include <cassert>

#include "shortest_paths.h"

#include "gradient_projection.h"

#include <libvpsc/generate-constraints.h>

#include "straightener.h"

typedef vector<unsigned> Cluster;

typedef vector<Cluster*> Clusters;

using vpsc::Rectangle;

namespace cola {

typedef pair<unsigned, unsigned> Edge;

// a graph component with a list of node_ids giving indices for some larger list of nodes

// for the nodes in this component, and a list of edges - node indices relative to this component

struct Component {

vector<unsigned> node_ids;

vector<Rectangle*> rects;

vector<Edge> edges;

SimpleConstraints scx, scy;

~Component() {

for(unsigned i=0;i<scx.size();i++) {

delete scx[i];

}

for(unsigned i=0;i<scy.size();i++) {

delete scy[i];

}

}

};

// for a graph of n nodes, return connected components

void connectedComponents(

const vector<Rectangle*> &rs,

const vector<Edge> &es,

const SimpleConstraints &scx,

const SimpleConstraints &scy,

vector<Component*> &components);

// defines references to three variables for which the goal function

// will be altered to prefer points u-b-v are in a linear arrangement

// such that b is placed at u+t(v-u).

struct LinearConstraint {

LinearConstraint(unsigned u, unsigned v, unsigned b, double w,

double frac_ub, double frac_bv,

double* X, double* Y)

: u(u),v(v),b(b),w(w),frac_ub(frac_ub),frac_bv(frac_bv),

tAtProjection(true)

{

assert(frac_ub<=1.0);

assert(frac_bv<=1.0);

assert(frac_ub>=0);

assert(frac_bv>=0);

if(tAtProjection) {

double uvx = X[v] - X[u],

uvy = Y[v] - Y[u],

vbx = X[b] - X[u],

vby = Y[b] - Y[u];

t = uvx * vbx + uvy * vby;

t/= uvx * uvx + uvy * uvy;

// p is the projection point of b on line uv

//double px = scalarProj * uvx + X[u];

//double py = scalarProj * uvy + Y[u];

// take t=|up|/|uv|

} else {

double numerator=X[b]-X[u];

double denominator=X[v]-X[u];

if(fabs(denominator)<0.001) {

// if line is close to vertical then use Y coords to compute T

numerator=Y[b]-Y[u];

denominator=Y[v]-Y[u];

}

if(fabs(denominator)<0.0001) {

denominator=1;

}

t=numerator/denominator;

}

duu=(1-t)*(1-t);

duv=t*(1-t);

dub=t-1;

dvv=t*t;

dvb=-t;

dbb=1;

//printf("New LC: t=%f\n",t);

}

unsigned u;

unsigned v;

unsigned b;

double w; // weight

double t;

// 2nd partial derivatives of the goal function

// (X[b] - (1-t) X[u] - t X[v])^2

double duu;

double duv;

double dub;

double dvv;

double dvb;

double dbb;

// Length of each segment as a fraction of the total edge length

double frac_ub;

double frac_bv;

bool tAtProjection;

};

typedef vector<LinearConstraint*> LinearConstraints;

class TestConvergence {

public:

double old_stress;

TestConvergence(const double& tolerance = 0.001, const unsigned maxiterations = 1000)

: old_stress(DBL_MAX),

tolerance(tolerance),

maxiterations(maxiterations),

iterations(0) { }

virtual ~TestConvergence() {}

virtual bool operator()(double new_stress, double* X, double* Y) {

//std::cout<<"iteration="<<iterations<<", new_stress="<<new_stress<<std::endl;

if (old_stress == DBL_MAX) {

old_stress = new_stress;

if(++iterations>=maxiterations) {;

return true;

} else {

return false;

}

}

bool converged =

fabs(new_stress - old_stress) / (new_stress + 1e-10) < tolerance

|| ++iterations > maxiterations;

old_stress = new_stress;

return converged;

}

private:

double tolerance;

unsigned maxiterations;

unsigned iterations;

};

static TestConvergence defaultTest(0.0001,100);

class ConstrainedMajorizationLayout {

public:

ConstrainedMajorizationLayout(

vector<Rectangle*>& rs,

vector<Edge>& es,

double* eweights,

double idealLength,

TestConvergence& done=defaultTest)

: constrainedLayout(false),

n(rs.size()),

lapSize(n), lap2(new double*[lapSize]),

Q(lap2), Dij(new double*[lapSize]),

tol(0.0001),

done(done),

X(new double[n]),

Y(new double[n]),

clusters(NULL),

linearConstraints(NULL),

gpX(NULL),

gpY(NULL),

straightenEdges(NULL)

{

assert(rs.size()==n);

boundingBoxes = new Rectangle*[rs.size()];

copy(rs.begin(),rs.end(),boundingBoxes);

double** D=new double*[n];

for(unsigned i=0;i<n;i++) {

D[i]=new double[n];

}

shortest_paths::johnsons(n,D,es,eweights);

edge_length = idealLength;

// Lij_{i!=j}=1/(Dij^2)

//

for(unsigned i = 0; i<n; i++) {

X[i]=rs[i]->getCentreX();

Y[i]=rs[i]->getCentreY();

double degree = 0;

lap2[i]=new double[n];

Dij[i]=new double[n];

for(unsigned j=0;j<n;j++) {

double w = edge_length * D[i][j];

Dij[i][j]=w;

if(i==j) continue;

degree+=lap2[i][j]=w>1e-30?1.f/(w*w):0;

}

lap2[i][i]=-degree;

delete [] D[i];

}

delete [] D;

}

void moveBoundingBoxes() {

for(unsigned i=0;i<lapSize;i++) {

boundingBoxes[i]->moveCentreX(X[i]);

boundingBoxes[i]->moveCentreY(Y[i]);

}

}

void setupConstraints(

AlignmentConstraints* acsx, AlignmentConstraints* acsy,

bool avoidOverlaps,

PageBoundaryConstraints* pbcx = NULL,

PageBoundaryConstraints* pbcy = NULL,

SimpleConstraints* scx = NULL,

SimpleConstraints* scy = NULL,

Clusters* cs = NULL,

vector<straightener::Edge*>* straightenEdges = NULL);

void addLinearConstraints(LinearConstraints* linearConstraints);

void setupDummyVars();

~ConstrainedMajorizationLayout() {

if(boundingBoxes) {

delete [] boundingBoxes;

}

if(constrainedLayout) {

delete gpX;

delete gpY;

}

for(unsigned i=0;i<lapSize;i++) {

delete [] lap2[i];

delete [] Dij[i];

}

delete [] lap2;

delete [] Dij;

delete [] X;

delete [] Y;

}

bool run();

void straighten(vector<straightener::Edge*>&, Dim);

bool avoidOverlaps;

bool constrainedLayout;

private:

double euclidean_distance(unsigned i, unsigned j) {

return sqrt(

(X[i] - X[j]) * (X[i] - X[j]) +

(Y[i] - Y[j]) * (Y[i] - Y[j]));

}

double compute_stress(double **Dij);

void majlayout(double** Dij,GradientProjection* gp, double* coords);

void majlayout(double** Dij,GradientProjection* gp, double* coords,

double* b);

unsigned n; // is lapSize + dummyVars

unsigned lapSize; // lapSize is the number of variables for actual nodes

double** lap2; // graph laplacian

double** Q; // quadratic terms matrix used in computations

double** Dij;

double tol;

TestConvergence& done;

Rectangle** boundingBoxes;

double *X, *Y;

Clusters* clusters;

double edge_length;

LinearConstraints *linearConstraints;

GradientProjection *gpX, *gpY;

vector<straightener::Edge*>* straightenEdges;

};

}

#endif // COLA_H