/*  t2z.cc

    Mark Woolrich & Mark Jenkinson, FMRIB Image Analysis Group

    Copyright (C) 1999-2000 University of Oxford  */

/*  Part of FSL - FMRIB's Software Library
    http://www.fmrib.ox.ac.uk/fsl
    fsl@fmrib.ox.ac.uk
    
    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
    LICENCE
    
    FMRIB Software Library, Release 5.0 (c) 2012, The University of
    Oxford (the "Software")
    
    The Software remains the property of the University of Oxford ("the
    University").
    
    The Software is distributed "AS IS" under this Licence solely for
    non-commercial use in the hope that it will be useful, but in order
    that the University as a charitable foundation protects its assets for
    the benefit of its educational and research purposes, the University
    makes clear that no condition is made or to be implied, nor is any
    warranty given or to be implied, as to the accuracy of the Software,
    or that it will be suitable for any particular purpose or for use
    under any specific conditions. Furthermore, the University disclaims
    all responsibility for the use which is made of the Software. It
    further disclaims any liability for the outcomes arising from using
    the Software.
    
    The Licensee agrees to indemnify the University and hold the
    University harmless from and against any and all claims, damages and
    liabilities asserted by third parties (including claims for
    negligence) which arise directly or indirectly from the use of the
    Software or the sale of any products based on the Software.
    
    No part of the Software may be reproduced, modified, transmitted or
    transferred in any form or by any means, electronic or mechanical,
    without the express permission of the University. The permission of
    the University is not required if the said reproduction, modification,
    transmission or transference is done without financial return, the
    conditions of this Licence are imposed upon the receiver of the
    product, and all original and amended source code is included in any
    transmitted product. You may be held legally responsible for any
    copyright infringement that is caused or encouraged by your failure to
    abide by these terms and conditions.
    
    You are not permitted under this Licence to use this Software
    commercially. Use for which any financial return is received shall be
    defined as commercial use, and includes (1) integration of all or part
    of the source code or the Software into a product for sale or license
    by or on behalf of Licensee to third parties or (2) use of the
    Software or any derivative of it for research with the final aim of
    developing software products for sale or license to a third party or
    (3) use of the Software or any derivative of it for research with the
    final aim of developing non-software products for sale or license to a
    third party, or (4) use of the Software to provide any service to an
    external organisation for which payment is received. If you are
    interested in using the Software commercially, please contact Isis
    Innovation Limited ("Isis"), the technology transfer company of the
    University, to negotiate a licence. Contact details are:
    innovation@isis.ox.ac.uk quoting reference DE/9564. */

#include <cmath>
#include "t2z.h"
#include "newmat.h"
#include "utils/tracer_plus.h"
#include "libprob.h"

using namespace NEWMAT;
using namespace Utilities;

namespace MISCMATHS {

  T2z* T2z::t2z = NULL;
  Z2t* Z2t::z2t = NULL;
 
  float Z2t::convert(float z, int dof) 
    {
      float t = 0.0;

      if(z>8)
	throw Exception("z is too large to convert to t");

      double p = MISCMATHS::ndtr(z);
      cerr << "p = " << p << endl;
      t = MISCMATHS::stdtri(dof,p);

      return t;
    }

  float T2z::larget2logp(float t, int dof)
  {
    //    static float logbeta[] = {   1.144729885849, 0.693147180560,
    //			 0.451582705289, 0.287682072452, 
    //			 0.163900632838, 0.064538521138,
    //			 -0.018420923956, -0.089612158690, 
    //			 -0.151952316581, -0.207395194346 } ;

    //static const float pi = 3.141592653590;
    //    static const float log2pi = log(2*pi);

    // Large T extrapolation routine for converting T to Z values
    //  written by Mark Jenkinson, March 2000
    //
    // It does T to Z via log(p) rather than p, since p becomes very
    //  small and underflows the arithmetic
    // Equations were derived by using integration by parts and give the
    //  following formulae:
    //  (1) T to log(p)    NB: n = DOF
    //       log(p) = -1/2*log(n) - log(beta(n/2,1/2)) - (n-1)/2*log(1+t*t/n)
    //                + log(1 - (n/(n+2))/(t*t) + 3*n*n/((n+2)*(n+4)*t*t*t*t)) 
    //  (2) Z to log(p)
    //       log(p) = -1/2*z*z - 1/2*log(2*pi) - log(z) 
    //                + log(1 - 1/(z*z) + 3/(z*z*z*z))
    // equation (2) is then solved by the recursion:
    //   z_0 = sqrt(2*(-log(p) - 1/2*log(2*pi)))
    //   z_{n+1} = sqrt(2*(-log(p) - 1/2*log(2*pi) - log(z_n) 
    //             + log(1 - 1/(zn*zn) + 3/(zn*zn*zn*zn))
    // In practice this recursion is quite accurate in 3 to 5 iterations
    // Equation (1) is accurate to 1 part in 10^3 for T>7.5  (any n)
    // Equation (2) is accurate to 1 part in 10^3 for Z>3.12  (3 iterations)


    if (t<0) {
      return larget2logp(-t,dof);
    }
  
    float logp, lbeta;

    if (dof<=0) { 
      cerr << "DOF cannot be zero or negative!" << endl;
      return 0.0; 
    }

    float n = (float) dof;

    // complete Beta function
    lbeta = this->logbeta(1/2.0,n/2.0);
    //if (dof<=10) {
    //lbeta = logbeta[dof-1];
    //} else {
    //lbeta = log2pi/2 - log(n)/2 + 1/(4*n);
    //}
    
    // log p from t value
    // logp = log( (1 - n/((n+2)*t*t) + 3*n*n/((n+2)*(n+4)*t*t*t*t))/(sqrt(n)*t))
    //          - ((n-1)/2)*log(1 + t*t/n) - lbeta;
    logp = log(( (3*n*n/((n+2)*(n+4)*t*t) - n/(n+2))/(t*t) + 1)/(sqrt(n)*t))
      - ((n-1)/2)*log(1 + t*t/n) - lbeta;

    return logp;
  }

  bool T2z::islarget(float t, int dof, float &logp) {
    // aymptotic formalae are valid if 
    //   log(p) < -14.5  (derived from Z-statistic approximation error)
    // For dof>=15, can guarantee that log(p)>-33 (p > 1e-14) if T<7.5
    //  and so in this region use conventional means, not asymptotic
    if ((dof>=15) && (fabs(t)<7.5)) { return false; }
    logp=larget2logp(t,dof);
    if (dof>=15)  return true;  // force asymptotic calc for all T>=7.5, D>=15
    return issmalllogp(logp);
  }

  bool T2z::issmalllogp(float logp) {
	// aymptotic formula accurate to 1 in 10^3 for Z>4.9
	// which corresponds to log(p)=-14.5
	return (logp < -14.5);
      }

  float T2z::convert(float t, int dof) {

      float z = 0.0, logp=0.0;
      
      if(!islarget(t,dof,logp)) {
	//	cerr << "t = " << t << endl;
	double p = MISCMATHS::stdtr(dof, t);
	//cerr << "p = " << p << endl;
	z = MISCMATHS::ndtri(p);
      }
      else {
	
	z = logp2largez(logp);

	//	cerr<<endl<<"logp="<<logp<<endl;

	if (t<0) z=-z;
      }

      return z;

    }


  float T2z::converttologp(float t, int dof) 
    {
      float logp=0.0;
      
      if(!islarget(t,dof,logp)) {
	logp = log(1-MISCMATHS::stdtr(dof, t));
      }
      else if(t<0) {
	// t < 0 and abs(t) is large enough to require asymptotic approx.
	// but t to logp is not available for negative t 
	// so just hardcode it to be -1e-12
	logp=-1e-12;
      }

//       cerr << "logp = " << logp << endl;
//       cerr << "exp(logp) = " << std::exp(logp) << endl;

      return logp;
    }

  void T2z::ComputePs(const ColumnVector& p_vars, const ColumnVector& p_cbs, int p_dof, ColumnVector& p_ps)
    {
      Tracer ts("T2z::ComputePs");

      int numTS = p_vars.Nrows();

      T2z& t2z = T2z::getInstance();

      p_ps.ReSize(numTS);

      for(int i = 1; i <= numTS; i++)
	{
	  //cerr << "var = " << p_vars(i) << " at index "<< i << endl;
	  //cerr << "cb = " << p_cbs(i) << " at index "<< i << endl;
	  if ( (p_vars(i) != 0.0) && (p_cbs(i) != 0.0) )
	    {
	      if(p_vars(i) < 0.0)
		{
		  //cerr << "var = " << p_vars(i) << " at index "<< i << endl;
		  p_ps(i) = 0.0;
		}
	      else
		{
		  p_ps(i) = t2z.converttologp(p_cbs(i)/sqrt(p_vars(i)),p_dof);
		  
		  //if(p_zs(i) == 0.0)
		  //cerr << " at index " << i << endl;
		}
	    }
	  else
	    p_ps(i) = 0.0;
	}
    }

  void T2z::ComputeZStats(const ColumnVector& p_vars, const ColumnVector& p_cbs, int p_dof, ColumnVector& p_zs)
    {
      ColumnVector dof = p_vars;
      dof = p_dof;
      ComputeZStats(p_vars,p_cbs,dof,p_zs);
    }

  void T2z::ComputeZStats(const ColumnVector& p_vars, const ColumnVector& p_cbs, const ColumnVector& p_dof, ColumnVector& p_zs)
    {
      Tracer ts("T2z::ComputeStats");

      int numTS = p_vars.Nrows();

      T2z& t2z = T2z::getInstance();

      p_zs.ReSize(numTS);

      for(int i = 1; i <= numTS; i++)
	{
	  //cerr << "var = " << p_vars(i) << " at index "<< i << endl;
	  //cerr << "cb = " << p_cbs(i) << " at index "<< i << endl;
	  if ( (p_vars(i) != 0.0) && (p_cbs(i) != 0.0) )
	    {
	      if(p_vars(i) < 0.0)
		{
		  //cerr << "var = " << p_vars(i) << " at index "<< i << endl;
		  p_zs(i) = 0.0;
		}
	      else
		{
		  p_zs(i) = t2z.convert(p_cbs(i)/sqrt(p_vars(i)),int(p_dof(i)));
		  
		  //if(p_zs(i) == 0.0)
		  //cerr << " at index " << i << endl;
		}
	    }
	  else
	    p_zs(i) = 0.0;
	}
    }
}