Split txtdump.*, part 1

[gromacs.git] / src / gromacs / mdlib / expanded.cpp

/*
 * This file is part of the GROMACS molecular simulation package.
 *
 * Copyright (c) 2012,2013,2014,2015, by the GROMACS development team, led by
 * Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
 * and including many others, as listed in the AUTHORS file in the
 * top-level source directory and at http://www.gromacs.org.
 *
 * GROMACS is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public License
 * as published by the Free Software Foundation; either version 2.1
 * of the License, or (at your option) any later version.
 *
 * GROMACS is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with GROMACS; if not, see
 * http://www.gnu.org/licenses, or write to the Free Software Foundation,
 * Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA.
 *
 * If you want to redistribute modifications to GROMACS, please
 * consider that scientific software is very special. Version
 * control is crucial - bugs must be traceable. We will be happy to
 * consider code for inclusion in the official distribution, but
 * derived work must not be called official GROMACS. Details are found
 * in the README & COPYING files - if they are missing, get the
 * official version at http://www.gromacs.org.
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 * To help us fund GROMACS development, we humbly ask that you cite
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 */
#include "gmxpre.h"

#include <stdio.h>

#include <cmath>

#include <algorithm>

#include "gromacs/domdec/domdec.h"
#include "gromacs/fileio/confio.h"
#include "gromacs/fileio/gmxfio.h"
#include "gromacs/fileio/xtcio.h"
#include "gromacs/gmxlib/chargegroup.h"
#include "gromacs/gmxlib/disre.h"
#include "gromacs/gmxlib/network.h"
#include "gromacs/gmxlib/nrnb.h"
#include "gromacs/gmxlib/orires.h"
#include "gromacs/math/units.h"
#include "gromacs/math/vec.h"
#include "gromacs/mdlib/calcmu.h"
#include "gromacs/mdlib/constr.h"
#include "gromacs/mdlib/force.h"
#include "gromacs/mdlib/mdrun.h"
#include "gromacs/mdlib/update.h"
#include "gromacs/mdtypes/inputrec.h"
#include "gromacs/mdtypes/md_enums.h"
#include "gromacs/random/random.h"
#include "gromacs/timing/wallcycle.h"
#include "gromacs/utility/fatalerror.h"
#include "gromacs/utility/gmxmpi.h"
#include "gromacs/utility/smalloc.h"

static void init_df_history_weights(df_history_t *dfhist, t_expanded *expand, int nlim)
{
    int i;
    dfhist->wl_delta = expand->init_wl_delta;
    for (i = 0; i < nlim; i++)
    {
        dfhist->sum_weights[i] = expand->init_lambda_weights[i];
        dfhist->sum_dg[i]      = expand->init_lambda_weights[i];
    }
}

/* Eventually should contain all the functions needed to initialize expanded ensemble
   before the md loop starts */
extern void init_expanded_ensemble(gmx_bool bStateFromCP, t_inputrec *ir, df_history_t *dfhist)
{
    if (!bStateFromCP)
    {
        init_df_history_weights(dfhist, ir->expandedvals, ir->fepvals->n_lambda);
    }
}

static void GenerateGibbsProbabilities(real *ene, double *p_k, double *pks, int minfep, int maxfep)
{

    int  i;
    real maxene;

    *pks   = 0.0;
    maxene = ene[minfep];
    /* find the maximum value */
    for (i = minfep; i <= maxfep; i++)
    {
        if (ene[i] > maxene)
        {
            maxene = ene[i];
        }
    }
    /* find the denominator */
    for (i = minfep; i <= maxfep; i++)
    {
        *pks += std::exp(ene[i]-maxene);
    }
    /*numerators*/
    for (i = minfep; i <= maxfep; i++)
    {
        p_k[i] = std::exp(ene[i]-maxene) / *pks;
    }
}

static void GenerateWeightedGibbsProbabilities(real *ene, double *p_k, double *pks, int nlim, real *nvals, real delta)
{

    int   i;
    real  maxene;
    real *nene;
    *pks = 0.0;

    snew(nene, nlim);
    for (i = 0; i < nlim; i++)
    {
        if (nvals[i] == 0)
        {
            /* add the delta, since we need to make sure it's greater than zero, and
               we need a non-arbitrary number? */
            nene[i] = ene[i] + std::log(nvals[i]+delta);
        }
        else
        {
            nene[i] = ene[i] + std::log(nvals[i]);
        }
    }

    /* find the maximum value */
    maxene = nene[0];
    for (i = 0; i < nlim; i++)
    {
        if (nene[i] > maxene)
        {
            maxene = nene[i];
        }
    }

    /* subtract off the maximum, avoiding overflow */
    for (i = 0; i < nlim; i++)
    {
        nene[i] -= maxene;
    }

    /* find the denominator */
    for (i = 0; i < nlim; i++)
    {
        *pks += std::exp(nene[i]);
    }

    /*numerators*/
    for (i = 0; i < nlim; i++)
    {
        p_k[i] = std::exp(nene[i]) / *pks;
    }
    sfree(nene);
}

real do_logsum(int N, real *a_n)
{

    /*     RETURN VALUE */
    /* log(\sum_{i=0}^(N-1) exp[a_n]) */
    real maxarg;
    real sum;
    int  i;
    real logsum;
    /*     compute maximum argument to exp(.) */

    maxarg = a_n[0];
    for (i = 1; i < N; i++)
    {
        maxarg = std::max(maxarg, a_n[i]);
    }

    /* compute sum of exp(a_n - maxarg) */
    sum = 0.0;
    for (i = 0; i < N; i++)
    {
        sum = sum + std::exp(a_n[i] - maxarg);
    }

    /*     compute log sum */
    logsum = std::log(sum) + maxarg;
    return logsum;
}

int FindMinimum(real *min_metric, int N)
{

    real min_val;
    int  min_nval, nval;

    min_nval = 0;
    min_val  = min_metric[0];

    for (nval = 0; nval < N; nval++)
    {
        if (min_metric[nval] < min_val)
        {
            min_val  = min_metric[nval];
            min_nval = nval;
        }
    }
    return min_nval;
}

static gmx_bool CheckHistogramRatios(int nhisto, real *histo, real ratio)
{

    int      i;
    real     nmean;
    gmx_bool bIfFlat;

    nmean = 0;
    for (i = 0; i < nhisto; i++)
    {
        nmean += histo[i];
    }

    if (nmean == 0)
    {
        /* no samples! is bad!*/
        bIfFlat = FALSE;
        return bIfFlat;
    }
    nmean /= (real)nhisto;

    bIfFlat = TRUE;
    for (i = 0; i < nhisto; i++)
    {
        /* make sure that all points are in the ratio < x <  1/ratio range  */
        if (!((histo[i]/nmean < 1.0/ratio) && (histo[i]/nmean > ratio)))
        {
            bIfFlat = FALSE;
            break;
        }
    }
    return bIfFlat;
}

static gmx_bool CheckIfDoneEquilibrating(int nlim, t_expanded *expand, df_history_t *dfhist, gmx_int64_t step)
{

    int      i, totalsamples;
    gmx_bool bDoneEquilibrating = TRUE;
    gmx_bool bIfFlat;

    /* If we are doing slow growth to get initial values, we haven't finished equilibrating */
    if (expand->lmc_forced_nstart > 0)
    {
        for (i = 0; i < nlim; i++)
        {
            if (dfhist->n_at_lam[i] < expand->lmc_forced_nstart) /* we are still doing the initial sweep, so we're definitely not
                                                                    done equilibrating*/
            {
                bDoneEquilibrating = FALSE;
                break;
            }
        }
    }
    else
    {
        /* assume we have equilibrated the weights, then check to see if any of the conditions are not met */
        bDoneEquilibrating = TRUE;

        /* calculate the total number of samples */
        switch (expand->elmceq)
        {
            case elmceqNO:
                /* We have not equilibrated, and won't, ever. */
                bDoneEquilibrating = FALSE;
                break;
            case elmceqYES:
                /* we have equilibrated -- we're done */
                bDoneEquilibrating = TRUE;
                break;
            case elmceqSTEPS:
                /* first, check if we are equilibrating by steps, if we're still under */
                if (step < expand->equil_steps)
                {
                    bDoneEquilibrating = FALSE;
                }
                break;
            case elmceqSAMPLES:
                totalsamples = 0;
                for (i = 0; i < nlim; i++)
                {
                    totalsamples += dfhist->n_at_lam[i];
                }
                if (totalsamples < expand->equil_samples)
                {
                    bDoneEquilibrating = FALSE;
                }
                break;
            case elmceqNUMATLAM:
                for (i = 0; i < nlim; i++)
                {
                    if (dfhist->n_at_lam[i] < expand->equil_n_at_lam) /* we are still doing the initial sweep, so we're definitely not
                                                                         done equilibrating*/
                    {
                        bDoneEquilibrating  = FALSE;
                        break;
                    }
                }
                break;
            case elmceqWLDELTA:
                if (EWL(expand->elamstats)) /* This check is in readir as well, but
                                               just to be sure */
                {
                    if (dfhist->wl_delta > expand->equil_wl_delta)
                    {
                        bDoneEquilibrating = FALSE;
                    }
                }
                break;
            case elmceqRATIO:
                /* we can use the flatness as a judge of good weights, as long as
                   we're not doing minvar, or Wang-Landau.
                   But turn off for now until we figure out exactly how we do this.
                 */

                if (!(EWL(expand->elamstats) || expand->elamstats == elamstatsMINVAR))
                {
                    /* we want to use flatness -avoiding- the forced-through samples.  Plus, we need to convert to
                       floats for this histogram function. */

                    real *modhisto;
                    snew(modhisto, nlim);
                    for (i = 0; i < nlim; i++)
                    {
                        modhisto[i] = 1.0*(dfhist->n_at_lam[i]-expand->lmc_forced_nstart);
                    }
                    bIfFlat = CheckHistogramRatios(nlim, modhisto, expand->equil_ratio);
                    sfree(modhisto);
                    if (!bIfFlat)
                    {
                        bDoneEquilibrating = FALSE;
                    }
                }
                break;
            default:
                bDoneEquilibrating = TRUE;
                break;
        }
    }
    return bDoneEquilibrating;
}

static gmx_bool UpdateWeights(int nlim, t_expanded *expand, df_history_t *dfhist,
                              int fep_state, real *scaled_lamee, real *weighted_lamee, gmx_int64_t step)
{
    gmx_bool  bSufficientSamples;
    int       i;
    int       n0, np1, nm1, nval, min_nvalm, min_nvalp, maxc;
    real      omega_m1_0, omega_p1_m1, omega_m1_p1, omega_p1_0, clam_osum;
    real      de, de_function;
    real      cnval, zero_sum_weights;
    real     *omegam_array, *weightsm_array, *omegap_array, *weightsp_array, *varm_array, *varp_array, *dwp_array, *dwm_array;
    real      clam_varm, clam_varp, clam_weightsm, clam_weightsp, clam_minvar;
    real     *lam_variance, *lam_dg;
    double   *p_k;
    double    pks = 0;
    real      chi_m1_0, chi_p1_0, chi_m2_0, chi_p2_0, chi_p1_m1, chi_p2_m1, chi_m1_p1, chi_m2_p1;

    /* if we have equilibrated the weights, exit now */
    if (dfhist->bEquil)
    {
        return FALSE;
    }

    if (CheckIfDoneEquilibrating(nlim, expand, dfhist, step))
    {
        dfhist->bEquil = TRUE;
        /* zero out the visited states so we know how many equilibrated states we have
           from here on out.*/
        for (i = 0; i < nlim; i++)
        {
            dfhist->n_at_lam[i] = 0;
        }
        return TRUE;
    }

    /* If we reached this far, we have not equilibrated yet, keep on
       going resetting the weights */

    if (EWL(expand->elamstats))
    {
        if (expand->elamstats == elamstatsWL)  /* Standard Wang-Landau */
        {
            dfhist->sum_weights[fep_state] -= dfhist->wl_delta;
            dfhist->wl_histo[fep_state]    += 1.0;
        }
        else if (expand->elamstats == elamstatsWWL) /* Weighted Wang-Landau */
        {
            snew(p_k, nlim);

            /* first increment count */
            GenerateGibbsProbabilities(weighted_lamee, p_k, &pks, 0, nlim-1);
            for (i = 0; i < nlim; i++)
            {
                dfhist->wl_histo[i] += (real)p_k[i];
            }

            /* then increment weights (uses count) */
            pks = 0.0;
            GenerateWeightedGibbsProbabilities(weighted_lamee, p_k, &pks, nlim, dfhist->wl_histo, dfhist->wl_delta);

            for (i = 0; i < nlim; i++)
            {
                dfhist->sum_weights[i] -= dfhist->wl_delta*(real)p_k[i];
            }
            /* Alternate definition, using logarithms. Shouldn't make very much difference! */
            /*
               real di;
               for (i=0;i<nlim;i++)
               {
                di = (real)1.0 + dfhist->wl_delta*(real)p_k[i];
                dfhist->sum_weights[i] -= log(di);
               }
             */
            sfree(p_k);
        }

        zero_sum_weights =  dfhist->sum_weights[0];
        for (i = 0; i < nlim; i++)
        {
            dfhist->sum_weights[i] -= zero_sum_weights;
        }
    }

    if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMETROPOLIS || expand->elamstats == elamstatsMINVAR)
    {

        de_function = 0;  /* to get rid of warnings, but this value will not be used because of the logic */
        maxc        = 2*expand->c_range+1;

        snew(lam_dg, nlim);
        snew(lam_variance, nlim);

        snew(omegap_array, maxc);
        snew(weightsp_array, maxc);
        snew(varp_array, maxc);
        snew(dwp_array, maxc);

        snew(omegam_array, maxc);
        snew(weightsm_array, maxc);
        snew(varm_array, maxc);
        snew(dwm_array, maxc);

        /* unpack the current lambdas -- we will only update 2 of these */

        for (i = 0; i < nlim-1; i++)
        {   /* only through the second to last */
            lam_dg[i]       = dfhist->sum_dg[i+1] - dfhist->sum_dg[i];
            lam_variance[i] = sqr(dfhist->sum_variance[i+1]) - sqr(dfhist->sum_variance[i]);
        }

        /* accumulate running averages */
        for (nval = 0; nval < maxc; nval++)
        {
            /* constants for later use */
            cnval = (real)(nval-expand->c_range);
            /* actually, should be able to rewrite it w/o exponential, for better numerical stability */
            if (fep_state > 0)
            {
                de = std::exp(cnval - (scaled_lamee[fep_state]-scaled_lamee[fep_state-1]));
                if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMINVAR)
                {
                    de_function = 1.0/(1.0+de);
                }
                else if (expand->elamstats == elamstatsMETROPOLIS)
                {
                    if (de < 1.0)
                    {
                        de_function = 1.0;
                    }
                    else
                    {
                        de_function = 1.0/de;
                    }
                }
                dfhist->accum_m[fep_state][nval]  += de_function;
                dfhist->accum_m2[fep_state][nval] += de_function*de_function;
            }

            if (fep_state < nlim-1)
            {
                de = std::exp(-cnval + (scaled_lamee[fep_state+1]-scaled_lamee[fep_state]));
                if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMINVAR)
                {
                    de_function = 1.0/(1.0+de);
                }
                else if (expand->elamstats == elamstatsMETROPOLIS)
                {
                    if (de < 1.0)
                    {
                        de_function = 1.0;
                    }
                    else
                    {
                        de_function = 1.0/de;
                    }
                }
                dfhist->accum_p[fep_state][nval]  += de_function;
                dfhist->accum_p2[fep_state][nval] += de_function*de_function;
            }

            /* Metropolis transition and Barker transition (unoptimized Bennett) acceptance weight determination */

            n0  = dfhist->n_at_lam[fep_state];
            if (fep_state > 0)
            {
                nm1 = dfhist->n_at_lam[fep_state-1];
            }
            else
            {
                nm1 = 0;
            }
            if (fep_state < nlim-1)
            {
                np1 = dfhist->n_at_lam[fep_state+1];
            }
            else
            {
                np1 = 0;
            }

            /* logic SHOULD keep these all set correctly whatever the logic, but apparently it can't figure it out. */
            chi_m1_0 = chi_p1_0 = chi_m2_0 = chi_p2_0 = chi_p1_m1 = chi_p2_m1 = chi_m1_p1 = chi_m2_p1 = 0;

            if (n0 > 0)
            {
                chi_m1_0 = dfhist->accum_m[fep_state][nval]/n0;
                chi_p1_0 = dfhist->accum_p[fep_state][nval]/n0;
                chi_m2_0 = dfhist->accum_m2[fep_state][nval]/n0;
                chi_p2_0 = dfhist->accum_p2[fep_state][nval]/n0;
            }

            if ((fep_state > 0 ) && (nm1 > 0))
            {
                chi_p1_m1 = dfhist->accum_p[fep_state-1][nval]/nm1;
                chi_p2_m1 = dfhist->accum_p2[fep_state-1][nval]/nm1;
            }

            if ((fep_state < nlim-1) && (np1 > 0))
            {
                chi_m1_p1 = dfhist->accum_m[fep_state+1][nval]/np1;
                chi_m2_p1 = dfhist->accum_m2[fep_state+1][nval]/np1;
            }

            omega_m1_0    = 0;
            omega_p1_0    = 0;
            clam_weightsm = 0;
            clam_weightsp = 0;
            clam_varm     = 0;
            clam_varp     = 0;

            if (fep_state > 0)
            {
                if (n0 > 0)
                {
                    omega_m1_0 = chi_m2_0/(chi_m1_0*chi_m1_0) - 1.0;
                }
                if (nm1 > 0)
                {
                    omega_p1_m1 = chi_p2_m1/(chi_p1_m1*chi_p1_m1) - 1.0;
                }
                if ((n0 > 0) && (nm1 > 0))
                {
                    clam_weightsm = (std::log(chi_m1_0) - std::log(chi_p1_m1)) + cnval;
                    clam_varm     = (1.0/n0)*(omega_m1_0) + (1.0/nm1)*(omega_p1_m1);
                }
            }

            if (fep_state < nlim-1)
            {
                if (n0 > 0)
                {
                    omega_p1_0 = chi_p2_0/(chi_p1_0*chi_p1_0) - 1.0;
                }
                if (np1 > 0)
                {
                    omega_m1_p1 = chi_m2_p1/(chi_m1_p1*chi_m1_p1) - 1.0;
                }
                if ((n0 > 0) && (np1 > 0))
                {
                    clam_weightsp = (std::log(chi_m1_p1) - std::log(chi_p1_0)) + cnval;
                    clam_varp     = (1.0/np1)*(omega_m1_p1) + (1.0/n0)*(omega_p1_0);
                }
            }

            if (n0 > 0)
            {
                omegam_array[nval]             = omega_m1_0;
            }
            else
            {
                omegam_array[nval]             = 0;
            }
            weightsm_array[nval]           = clam_weightsm;
            varm_array[nval]               = clam_varm;
            if (nm1 > 0)
            {
                dwm_array[nval]  = fabs( (cnval + std::log((1.0*n0)/nm1)) - lam_dg[fep_state-1] );
            }
            else
            {
                dwm_array[nval]  = fabs( cnval - lam_dg[fep_state-1] );
            }

            if (n0 > 0)
            {
                omegap_array[nval]             = omega_p1_0;
            }
            else
            {
                omegap_array[nval]             = 0;
            }
            weightsp_array[nval]           = clam_weightsp;
            varp_array[nval]               = clam_varp;
            if ((np1 > 0) && (n0 > 0))
            {
                dwp_array[nval]  = fabs( (cnval + std::log((1.0*np1)/n0)) - lam_dg[fep_state] );
            }
            else
            {
                dwp_array[nval]  = fabs( cnval - lam_dg[fep_state] );
            }

        }

        /* find the C's closest to the old weights value */

        min_nvalm     = FindMinimum(dwm_array, maxc);
        omega_m1_0    = omegam_array[min_nvalm];
        clam_weightsm = weightsm_array[min_nvalm];
        clam_varm     = varm_array[min_nvalm];

        min_nvalp     = FindMinimum(dwp_array, maxc);
        omega_p1_0    = omegap_array[min_nvalp];
        clam_weightsp = weightsp_array[min_nvalp];
        clam_varp     = varp_array[min_nvalp];

        clam_osum   = omega_m1_0 + omega_p1_0;
        clam_minvar = 0;
        if (clam_osum > 0)
        {
            clam_minvar = 0.5*std::log(clam_osum);
        }

        if (fep_state > 0)
        {
            lam_dg[fep_state-1]       = clam_weightsm;
            lam_variance[fep_state-1] = clam_varm;
        }

        if (fep_state < nlim-1)
        {
            lam_dg[fep_state]       = clam_weightsp;
            lam_variance[fep_state] = clam_varp;
        }

        if (expand->elamstats == elamstatsMINVAR)
        {
            bSufficientSamples = TRUE;
            /* make sure they are all past a threshold */
            for (i = 0; i < nlim; i++)
            {
                if (dfhist->n_at_lam[i] < expand->minvarmin)
                {
                    bSufficientSamples = FALSE;
                }
            }
            if (bSufficientSamples)
            {
                dfhist->sum_minvar[fep_state] = clam_minvar;
                if (fep_state == 0)
                {
                    for (i = 0; i < nlim; i++)
                    {
                        dfhist->sum_minvar[i] += (expand->minvar_const-clam_minvar);
                    }
                    expand->minvar_const          = clam_minvar;
                    dfhist->sum_minvar[fep_state] = 0.0;
                }
                else
                {
                    dfhist->sum_minvar[fep_state] -= expand->minvar_const;
                }
            }
        }

        /* we need to rezero minvar now, since it could change at fep_state = 0 */
        dfhist->sum_dg[0]       = 0.0;
        dfhist->sum_variance[0] = 0.0;
        dfhist->sum_weights[0]  = dfhist->sum_dg[0] + dfhist->sum_minvar[0]; /* should be zero */

        for (i = 1; i < nlim; i++)
        {
            dfhist->sum_dg[i]       = lam_dg[i-1] + dfhist->sum_dg[i-1];
            dfhist->sum_variance[i] = std::sqrt(lam_variance[i-1] + sqr(dfhist->sum_variance[i-1]));
            dfhist->sum_weights[i]  = dfhist->sum_dg[i] + dfhist->sum_minvar[i];
        }

        sfree(lam_dg);
        sfree(lam_variance);

        sfree(omegam_array);
        sfree(weightsm_array);
        sfree(varm_array);
        sfree(dwm_array);

        sfree(omegap_array);
        sfree(weightsp_array);
        sfree(varp_array);
        sfree(dwp_array);
    }
    return FALSE;
}

static int ChooseNewLambda(int nlim, t_expanded *expand, df_history_t *dfhist, int fep_state, real *weighted_lamee, double *p_k,
                           gmx_int64_t seed, gmx_int64_t step)
{
    /* Choose new lambda value, and update transition matrix */

    int      i, ifep, minfep, maxfep, lamnew, lamtrial, starting_fep_state;
    real     r1, r2, de, trialprob, tprob = 0;
    double  *propose, *accept, *remainder;
    double   pks;
    real     pnorm;

    starting_fep_state = fep_state;
    lamnew             = fep_state; /* so that there is a default setting -- stays the same */

    if (!EWL(expand->elamstats))    /* ignore equilibrating the weights if using WL */
    {
        if ((expand->lmc_forced_nstart > 0) && (dfhist->n_at_lam[nlim-1] <= expand->lmc_forced_nstart))
        {
            /* Use a marching method to run through the lambdas and get preliminary free energy data,
               before starting 'free' sampling.  We start free sampling when we have enough at each lambda */

            /* if we have enough at this lambda, move on to the next one */

            if (dfhist->n_at_lam[fep_state] == expand->lmc_forced_nstart)
            {
                lamnew = fep_state+1;
                if (lamnew == nlim)  /* whoops, stepped too far! */
                {
                    lamnew -= 1;
                }
            }
            else
            {
                lamnew = fep_state;
            }
            return lamnew;
        }
    }

    snew(propose, nlim);
    snew(accept, nlim);
    snew(remainder, nlim);

    for (i = 0; i < expand->lmc_repeats; i++)
    {
        double rnd[2];

        gmx_rng_cycle_2uniform(step, i, seed, RND_SEED_EXPANDED, rnd);

        for (ifep = 0; ifep < nlim; ifep++)
        {
            propose[ifep] = 0;
            accept[ifep]  = 0;
        }

        if ((expand->elmcmove == elmcmoveGIBBS) || (expand->elmcmove == elmcmoveMETGIBBS))
        {
            /* use the Gibbs sampler, with restricted range */
            if (expand->gibbsdeltalam < 0)
            {
                minfep      = 0;
                maxfep      = nlim-1;
            }
            else
            {
                minfep = fep_state - expand->gibbsdeltalam;
                maxfep = fep_state + expand->gibbsdeltalam;
                if (minfep < 0)
                {
                    minfep = 0;
                }
                if (maxfep > nlim-1)
                {
                    maxfep = nlim-1;
                }
            }

            GenerateGibbsProbabilities(weighted_lamee, p_k, &pks, minfep, maxfep);

            if (expand->elmcmove == elmcmoveGIBBS)
            {
                for (ifep = minfep; ifep <= maxfep; ifep++)
                {
                    propose[ifep] = p_k[ifep];
                    accept[ifep]  = 1.0;
                }
                /* Gibbs sampling */
                r1 = rnd[0];
                for (lamnew = minfep; lamnew <= maxfep; lamnew++)
                {
                    if (r1 <= p_k[lamnew])
                    {
                        break;
                    }
                    r1 -= p_k[lamnew];
                }
            }
            else if (expand->elmcmove == elmcmoveMETGIBBS)
            {

                /* Metropolized Gibbs sampling */
                for (ifep = minfep; ifep <= maxfep; ifep++)
                {
                    remainder[ifep] = 1 - p_k[ifep];
                }

                /* find the proposal probabilities */

                if (remainder[fep_state] == 0)
                {
                    /* only the current state has any probability */
                    /* we have to stay at the current state */
                    lamnew = fep_state;
                }
                else
                {
                    for (ifep = minfep; ifep <= maxfep; ifep++)
                    {
                        if (ifep != fep_state)
                        {
                            propose[ifep] = p_k[ifep]/remainder[fep_state];
                        }
                        else
                        {
                            propose[ifep] = 0;
                        }
                    }

                    r1 = rnd[0];
                    for (lamtrial = minfep; lamtrial <= maxfep; lamtrial++)
                    {
                        pnorm = p_k[lamtrial]/remainder[fep_state];
                        if (lamtrial != fep_state)
                        {
                            if (r1 <= pnorm)
                            {
                                break;
                            }
                            r1 -= pnorm;
                        }
                    }

                    /* we have now selected lamtrial according to p(lamtrial)/1-p(fep_state) */
                    tprob = 1.0;
                    /* trial probability is min{1,\frac{1 - p(old)}{1-p(new)} MRS 1/8/2008 */
                    trialprob = (remainder[fep_state])/(remainder[lamtrial]);
                    if (trialprob < tprob)
                    {
                        tprob = trialprob;
                    }
                    r2 = rnd[1];
                    if (r2 < tprob)
                    {
                        lamnew = lamtrial;
                    }
                    else
                    {
                        lamnew = fep_state;
                    }
                }

                /* now figure out the acceptance probability for each */
                for (ifep = minfep; ifep <= maxfep; ifep++)
                {
                    tprob = 1.0;
                    if (remainder[ifep] != 0)
                    {
                        trialprob = (remainder[fep_state])/(remainder[ifep]);
                    }
                    else
                    {
                        trialprob = 1.0; /* this state is the only choice! */
                    }
                    if (trialprob < tprob)
                    {
                        tprob = trialprob;
                    }
                    /* probability for fep_state=0, but that's fine, it's never proposed! */
                    accept[ifep] = tprob;
                }
            }

            if (lamnew > maxfep)
            {
                /* it's possible some rounding is failing */
                if (gmx_within_tol(remainder[fep_state], 0, 50*GMX_DOUBLE_EPS))
                {
                    /* numerical rounding error -- no state other than the original has weight */
                    lamnew = fep_state;
                }
                else
                {
                    /* probably not a numerical issue */
                    int   loc    = 0;
                    int   nerror = 200+(maxfep-minfep+1)*60;
                    char *errorstr;
                    snew(errorstr, nerror);
                    /* if its greater than maxfep, then something went wrong -- probably underflow in the calculation
                       of sum weights. Generated detailed info for failure */
                    loc += sprintf(errorstr, "Something wrong in choosing new lambda state with a Gibbs move -- probably underflow in weight determination.\nDenominator is: %3d%17.10e\n  i                dE        numerator          weights\n", 0, pks);
                    for (ifep = minfep; ifep <= maxfep; ifep++)
                    {
                        loc += sprintf(&errorstr[loc], "%3d %17.10e%17.10e%17.10e\n", ifep, weighted_lamee[ifep], p_k[ifep], dfhist->sum_weights[ifep]);
                    }
                    gmx_fatal(FARGS, errorstr);
                }
            }
        }
        else if ((expand->elmcmove == elmcmoveMETROPOLIS) || (expand->elmcmove == elmcmoveBARKER))
        {
            /* use the metropolis sampler with trial +/- 1 */
            r1 = rnd[0];
            if (r1 < 0.5)
            {
                if (fep_state == 0)
                {
                    lamtrial = fep_state;
                }
                else
                {
                    lamtrial = fep_state-1;
                }
            }
            else
            {
                if (fep_state == nlim-1)
                {
                    lamtrial = fep_state;
                }
                else
                {
                    lamtrial = fep_state+1;
                }
            }

            de = weighted_lamee[lamtrial] - weighted_lamee[fep_state];
            if (expand->elmcmove == elmcmoveMETROPOLIS)
            {
                tprob     = 1.0;
                trialprob = std::exp(de);
                if (trialprob < tprob)
                {
                    tprob = trialprob;
                }
                propose[fep_state] = 0;
                propose[lamtrial]  = 1.0; /* note that this overwrites the above line if fep_state = ntrial, which only occurs at the ends */
                accept[fep_state]  = 1.0; /* doesn't actually matter, never proposed unless fep_state = ntrial, in which case it's 1.0 anyway */
                accept[lamtrial]   = tprob;

            }
            else if (expand->elmcmove == elmcmoveBARKER)
            {
                tprob = 1.0/(1.0+std::exp(-de));

                propose[fep_state] = (1-tprob);
                propose[lamtrial] += tprob; /* we add, to account for the fact that at the end, they might be the same point */
                accept[fep_state]  = 1.0;
                accept[lamtrial]   = 1.0;
            }

            r2 = rnd[1];
            if (r2 < tprob)
            {
                lamnew = lamtrial;
            }
            else
            {
                lamnew = fep_state;
            }
        }

        for (ifep = 0; ifep < nlim; ifep++)
        {
            dfhist->Tij[fep_state][ifep]      += propose[ifep]*accept[ifep];
            dfhist->Tij[fep_state][fep_state] += propose[ifep]*(1.0-accept[ifep]);
        }
        fep_state = lamnew;
    }

    dfhist->Tij_empirical[starting_fep_state][lamnew] += 1.0;

    sfree(propose);
    sfree(accept);
    sfree(remainder);

    return lamnew;
}

/* print out the weights to the log, along with current state */
extern void PrintFreeEnergyInfoToFile(FILE *outfile, t_lambda *fep, t_expanded *expand, t_simtemp *simtemp, df_history_t *dfhist,
                                      int fep_state, int frequency, gmx_int64_t step)
{
    int         nlim, i, ifep, jfep;
    real        dw, dg, dv, Tprint;
    const char *print_names[efptNR] = {" FEPL", "MassL", "CoulL", " VdwL", "BondL", "RestT", "Temp.(K)"};
    gmx_bool    bSimTemp            = FALSE;

    nlim = fep->n_lambda;
    if (simtemp != NULL)
    {
        bSimTemp = TRUE;
    }

    if (step % frequency == 0)
    {
        fprintf(outfile, "             MC-lambda information\n");
        if (EWL(expand->elamstats) && (!(dfhist->bEquil)))
        {
            fprintf(outfile, "  Wang-Landau incrementor is: %11.5g\n", dfhist->wl_delta);
        }
        fprintf(outfile, "  N");
        for (i = 0; i < efptNR; i++)
        {
            if (fep->separate_dvdl[i])
            {
                fprintf(outfile, "%7s", print_names[i]);
            }
            else if ((i == efptTEMPERATURE) && bSimTemp)
            {
                fprintf(outfile, "%10s", print_names[i]); /* more space for temperature formats */
            }
        }
        fprintf(outfile, "    Count   ");
        if (expand->elamstats == elamstatsMINVAR)
        {
            fprintf(outfile, "W(in kT)   G(in kT)  dG(in kT)  dV(in kT)\n");
        }
        else
        {
            fprintf(outfile, "G(in kT)  dG(in kT)\n");
        }
        for (ifep = 0; ifep < nlim; ifep++)
        {
            if (ifep == nlim-1)
            {
                dw = 0.0;
                dg = 0.0;
                dv = 0.0;
            }
            else
            {
                dw = dfhist->sum_weights[ifep+1] - dfhist->sum_weights[ifep];
                dg = dfhist->sum_dg[ifep+1] - dfhist->sum_dg[ifep];
                dv = std::sqrt(sqr(dfhist->sum_variance[ifep+1]) - sqr(dfhist->sum_variance[ifep]));
            }
            fprintf(outfile, "%3d", (ifep+1));
            for (i = 0; i < efptNR; i++)
            {
                if (fep->separate_dvdl[i])
                {
                    fprintf(outfile, "%7.3f", fep->all_lambda[i][ifep]);
                }
                else if (i == efptTEMPERATURE && bSimTemp)
                {
                    fprintf(outfile, "%9.3f", simtemp->temperatures[ifep]);
                }
            }
            if (EWL(expand->elamstats) && (!(dfhist->bEquil)))  /* if performing WL and still haven't equilibrated */
            {
                if (expand->elamstats == elamstatsWL)
                {
                    fprintf(outfile, " %8d", (int)dfhist->wl_histo[ifep]);
                }
                else
                {
                    fprintf(outfile, " %8.3f", dfhist->wl_histo[ifep]);
                }
            }
            else   /* we have equilibrated weights */
            {
                fprintf(outfile, " %8d", dfhist->n_at_lam[ifep]);
            }
            if (expand->elamstats == elamstatsMINVAR)
            {
                fprintf(outfile, " %10.5f %10.5f %10.5f %10.5f", dfhist->sum_weights[ifep], dfhist->sum_dg[ifep], dg, dv);
            }
            else
            {
                fprintf(outfile, " %10.5f %10.5f", dfhist->sum_weights[ifep], dw);
            }
            if (ifep == fep_state)
            {
                fprintf(outfile, " <<\n");
            }
            else
            {
                fprintf(outfile, "   \n");
            }
        }
        fprintf(outfile, "\n");

        if ((step % expand->nstTij == 0) && (expand->nstTij > 0) && (step > 0))
        {
            fprintf(outfile, "                     Transition Matrix\n");
            for (ifep = 0; ifep < nlim; ifep++)
            {
                fprintf(outfile, "%12d", (ifep+1));
            }
            fprintf(outfile, "\n");
            for (ifep = 0; ifep < nlim; ifep++)
            {
                for (jfep = 0; jfep < nlim; jfep++)
                {
                    if (dfhist->n_at_lam[ifep] > 0)
                    {
                        if (expand->bSymmetrizedTMatrix)
                        {
                            Tprint = (dfhist->Tij[ifep][jfep]+dfhist->Tij[jfep][ifep])/(dfhist->n_at_lam[ifep]+dfhist->n_at_lam[jfep]);
                        }
                        else
                        {
                            Tprint = (dfhist->Tij[ifep][jfep])/(dfhist->n_at_lam[ifep]);
                        }
                    }
                    else
                    {
                        Tprint = 0.0;
                    }
                    fprintf(outfile, "%12.8f", Tprint);
                }
                fprintf(outfile, "%3d\n", (ifep+1));
            }

            fprintf(outfile, "                  Empirical Transition Matrix\n");
            for (ifep = 0; ifep < nlim; ifep++)
            {
                fprintf(outfile, "%12d", (ifep+1));
            }
            fprintf(outfile, "\n");
            for (ifep = 0; ifep < nlim; ifep++)
            {
                for (jfep = 0; jfep < nlim; jfep++)
                {
                    if (dfhist->n_at_lam[ifep] > 0)
                    {
                        if (expand->bSymmetrizedTMatrix)
                        {
                            Tprint = (dfhist->Tij_empirical[ifep][jfep]+dfhist->Tij_empirical[jfep][ifep])/(dfhist->n_at_lam[ifep]+dfhist->n_at_lam[jfep]);
                        }
                        else
                        {
                            Tprint = dfhist->Tij_empirical[ifep][jfep]/(dfhist->n_at_lam[ifep]);
                        }
                    }
                    else
                    {
                        Tprint = 0.0;
                    }
                    fprintf(outfile, "%12.8f", Tprint);
                }
                fprintf(outfile, "%3d\n", (ifep+1));
            }
        }
    }
}

extern int ExpandedEnsembleDynamics(FILE *log, t_inputrec *ir, gmx_enerdata_t *enerd,
                                    t_state *state, t_extmass *MassQ, int fep_state, df_history_t *dfhist,
                                    gmx_int64_t step,
                                    rvec *v, t_mdatoms *mdatoms)
/* Note that the state variable is only needed for simulated tempering, not
   Hamiltonian expanded ensemble.  May be able to remove it after integrator refactoring. */
{
    real       *pfep_lamee, *scaled_lamee, *weighted_lamee;
    double     *p_k;
    int         i, nlim, lamnew, totalsamples;
    real        oneovert, maxscaled = 0, maxweighted = 0;
    t_expanded *expand;
    t_simtemp  *simtemp;
    gmx_bool    bIfReset, bSwitchtoOneOverT, bDoneEquilibrating = FALSE;

    expand  = ir->expandedvals;
    simtemp = ir->simtempvals;
    nlim    = ir->fepvals->n_lambda;

    snew(scaled_lamee, nlim);
    snew(weighted_lamee, nlim);
    snew(pfep_lamee, nlim);
    snew(p_k, nlim);

    /* update the count at the current lambda*/
    dfhist->n_at_lam[fep_state]++;

    /* need to calculate the PV term somewhere, but not needed here? Not until there's a lambda state that's
       pressure controlled.*/
    /*
       pVTerm = 0;
       where does this PV term go?
       for (i=0;i<nlim;i++)
       {
       fep_lamee[i] += pVTerm;
       }
     */

    /* determine the minimum value to avoid overflow.  Probably a better way to do this */
    /* we don't need to include the pressure term, since the volume is the same between the two.
       is there some term we are neglecting, however? */

    if (ir->efep != efepNO)
    {
        for (i = 0; i < nlim; i++)
        {
            if (ir->bSimTemp)
            {
                /* Note -- this assumes no mass changes, since kinetic energy is not added  . . . */
                scaled_lamee[i] = (enerd->enerpart_lambda[i+1]-enerd->enerpart_lambda[0])/(simtemp->temperatures[i]*BOLTZ)
                    + enerd->term[F_EPOT]*(1.0/(simtemp->temperatures[i])- 1.0/(simtemp->temperatures[fep_state]))/BOLTZ;
            }
            else
            {
                scaled_lamee[i] = (enerd->enerpart_lambda[i+1]-enerd->enerpart_lambda[0])/(expand->mc_temp*BOLTZ);
                /* mc_temp is currently set to the system reft unless otherwise defined */
            }

            /* save these energies for printing, so they don't get overwritten by the next step */
            /* they aren't overwritten in the non-free energy case, but we always print with these
               for simplicity */
        }
    }
    else
    {
        if (ir->bSimTemp)
        {
            for (i = 0; i < nlim; i++)
            {
                scaled_lamee[i] = enerd->term[F_EPOT]*(1.0/simtemp->temperatures[i] - 1.0/simtemp->temperatures[fep_state])/BOLTZ;
            }
        }
    }

    for (i = 0; i < nlim; i++)
    {
        pfep_lamee[i] = scaled_lamee[i];

        weighted_lamee[i] = dfhist->sum_weights[i] - scaled_lamee[i];
        if (i == 0)
        {
            maxscaled   = scaled_lamee[i];
            maxweighted = weighted_lamee[i];
        }
        else
        {
            if (scaled_lamee[i] > maxscaled)
            {
                maxscaled = scaled_lamee[i];
            }
            if (weighted_lamee[i] > maxweighted)
            {
                maxweighted = weighted_lamee[i];
            }
        }
    }

    for (i = 0; i < nlim; i++)
    {
        scaled_lamee[i]   -= maxscaled;
        weighted_lamee[i] -= maxweighted;
    }

    /* update weights - we decide whether or not to actually do this inside */

    bDoneEquilibrating = UpdateWeights(nlim, expand, dfhist, fep_state, scaled_lamee, weighted_lamee, step);
    if (bDoneEquilibrating)
    {
        if (log)
        {
            fprintf(log, "\nStep %d: Weights have equilibrated, using criteria: %s\n", (int)step, elmceq_names[expand->elmceq]);
        }
    }

    lamnew = ChooseNewLambda(nlim, expand, dfhist, fep_state, weighted_lamee, p_k,
                             ir->expandedvals->lmc_seed, step);
    /* if using simulated tempering, we need to adjust the temperatures */
    if (ir->bSimTemp && (lamnew != fep_state)) /* only need to change the temperatures if we change the state */
    {
        int   i, j, n, d;
        real *buf_ngtc;
        real  told;
        int   nstart, nend, gt;

        snew(buf_ngtc, ir->opts.ngtc);

        for (i = 0; i < ir->opts.ngtc; i++)
        {
            if (ir->opts.ref_t[i] > 0)
            {
                told              = ir->opts.ref_t[i];
                ir->opts.ref_t[i] =  simtemp->temperatures[lamnew];
                buf_ngtc[i]       = std::sqrt(ir->opts.ref_t[i]/told); /* using the buffer as temperature scaling */
            }
        }

        /* we don't need to manipulate the ekind information, as it isn't due to be reset until the next step anyway */

        nstart = 0;
        nend   = mdatoms->homenr;
        for (n = nstart; n < nend; n++)
        {
            gt = 0;
            if (mdatoms->cTC)
            {
                gt = mdatoms->cTC[n];
            }
            for (d = 0; d < DIM; d++)
            {
                v[n][d] *= buf_ngtc[gt];
            }
        }

        if (inputrecNptTrotter(ir) || inputrecNphTrotter(ir) || inputrecNvtTrotter(ir))
        {
            /* we need to recalculate the masses if the temperature has changed */
            init_npt_masses(ir, state, MassQ, FALSE);
            for (i = 0; i < state->nnhpres; i++)
            {
                for (j = 0; j < ir->opts.nhchainlength; j++)
                {
                    state->nhpres_vxi[i+j] *= buf_ngtc[i];
                }
            }
            for (i = 0; i < ir->opts.ngtc; i++)
            {
                for (j = 0; j < ir->opts.nhchainlength; j++)
                {
                    state->nosehoover_vxi[i+j] *= buf_ngtc[i];
                }
            }
        }
        sfree(buf_ngtc);
    }

    /* now check on the Wang-Landau updating critera */

    if (EWL(expand->elamstats))
    {
        bSwitchtoOneOverT = FALSE;
        if (expand->bWLoneovert)
        {
            totalsamples = 0;
            for (i = 0; i < nlim; i++)
            {
                totalsamples += dfhist->n_at_lam[i];
            }
            oneovert = (1.0*nlim)/totalsamples;
            /* oneovert has decreasd by a bit since last time, so we actually make sure its within one of this number */
            /* switch to 1/t incrementing when wl_delta has decreased at least once, and wl_delta is now less than 1/t */
            if ((dfhist->wl_delta <= ((totalsamples)/(totalsamples-1.00001))*oneovert) &&
                (dfhist->wl_delta < expand->init_wl_delta))
            {
                bSwitchtoOneOverT = TRUE;
            }
        }
        if (bSwitchtoOneOverT)
        {
            dfhist->wl_delta = oneovert; /* now we reduce by this each time, instead of only at flatness */
        }
        else
        {
            bIfReset = CheckHistogramRatios(nlim, dfhist->wl_histo, expand->wl_ratio);
            if (bIfReset)
            {
                for (i = 0; i < nlim; i++)
                {
                    dfhist->wl_histo[i] = 0;
                }
                dfhist->wl_delta *= expand->wl_scale;
                if (log)
                {
                    fprintf(log, "\nStep %d: weights are now:", (int)step);
                    for (i = 0; i < nlim; i++)
                    {
                        fprintf(log, " %.5f", dfhist->sum_weights[i]);
                    }
                    fprintf(log, "\n");
                }
            }
        }
    }
    sfree(pfep_lamee);
    sfree(scaled_lamee);
    sfree(weighted_lamee);
    sfree(p_k);

    return lamnew;
}