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expanded.c

Expanded Ensemble with AIM function - Christopher Mirabzadeh, 12/15/2015 06:27 PM

 
1
/*
2
 * This file is part of the GROMACS molecular simulation package.
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 *
4
 * Copyright (c) 2012,2013,2014, by the GROMACS development team, led by
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 * Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
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 * and including many others, as listed in the AUTHORS file in the
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 * top-level source directory and at http://www.gromacs.org.
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 *
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 * GROMACS is free software; you can redistribute it and/or
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 * modify it under the terms of the GNU Lesser General Public License
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 * as published by the Free Software Foundation; either version 2.1
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 * of the License, or (at your option) any later version.
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 *
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 * GROMACS is distributed in the hope that it will be useful,
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 * but WITHOUT ANY WARRANTY; without even the implied warranty of
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 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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 * Lesser General Public License for more details.
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 *
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 * You should have received a copy of the GNU Lesser General Public
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 * License along with GROMACS; if not, see
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 * http://www.gnu.org/licenses, or write to the Free Software Foundation,
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 * Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA.
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 *
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 * If you want to redistribute modifications to GROMACS, please
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 * consider that scientific software is very special. Version
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 * control is crucial - bugs must be traceable. We will be happy to
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 * consider code for inclusion in the official distribution, but
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 * derived work must not be called official GROMACS. Details are found
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 * in the README & COPYING files - if they are missing, get the
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 * official version at http://www.gromacs.org.
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 *
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 * To help us fund GROMACS development, we humbly ask that you cite
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 * the research papers on the package. Check out http://www.gromacs.org.
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 */
35
#ifdef HAVE_CONFIG_H
36
#include <config.h>
37
#endif
38

    
39
#include <stdio.h>
40
#include <math.h>
41
#include "typedefs.h"
42
#include "gromacs/utility/smalloc.h"
43
#include "names.h"
44
#include "gromacs/fileio/confio.h"
45
#include "txtdump.h"
46
#include "pbc.h"
47
#include "chargegroup.h"
48
#include "vec.h"
49
#include "nrnb.h"
50
#include "mshift.h"
51
#include "mdrun.h"
52
#include "update.h"
53
#include "physics.h"
54
#include "main.h"
55
#include "mdatoms.h"
56
#include "force.h"
57
#include "bondf.h"
58
#include "pme.h"
59
#include "disre.h"
60
#include "orires.h"
61
#include "network.h"
62
#include "calcmu.h"
63
#include "constr.h"
64
#include "xvgr.h"
65
#include "gromacs/random/random.h"
66
#include "domdec.h"
67
#include "macros.h"
68

    
69
#include "gromacs/fileio/confio.h"
70
#include "gromacs/fileio/gmxfio.h"
71
#include "gromacs/fileio/trnio.h"
72
#include "gromacs/fileio/xtcio.h"
73
#include "gromacs/timing/wallcycle.h"
74
#include "gmx_fatal.h"
75
#include "gromacs/utility/gmxmpi.h"
76

    
77
static void init_df_history_weights(df_history_t *dfhist, t_expanded *expand, int nlim)
78
{
79
    int i;
80
    dfhist->wl_delta = expand->init_wl_delta;
81
    for (i = 0; i < nlim; i++)
82
    {
83
        dfhist->sum_weights[i] = expand->init_lambda_weights[i];
84
        dfhist->sum_dg[i]      = expand->init_lambda_weights[i];
85
    }
86
}
87

    
88
/* Eventually should contain all the functions needed to initialize expanded ensemble
89
   before the md loop starts */
90
extern void init_expanded_ensemble(gmx_bool bStateFromCP, t_inputrec *ir, df_history_t *dfhist)
91
{
92
    if (!bStateFromCP)
93
    {
94
        init_df_history_weights(dfhist, ir->expandedvals, ir->fepvals->n_lambda);
95
    }
96
}
97

    
98
static void GenerateGibbsProbabilities(real *ene, double *p_k, double *pks, int minfep, int maxfep)
99
{
100

    
101
    int  i;
102
    real maxene;
103

    
104
    *pks   = 0.0;
105
    maxene = ene[minfep];
106
    /* find the maximum value */
107
    for (i = minfep; i <= maxfep; i++)
108
    {
109
        if (ene[i] > maxene)
110
        {
111
            maxene = ene[i];
112
        }
113
    }
114
    /* find the denominator */
115
    for (i = minfep; i <= maxfep; i++)
116
    {
117
        *pks += exp(ene[i]-maxene);
118
    }
119
    /*numerators*/
120
    for (i = minfep; i <= maxfep; i++)
121
    {
122
        p_k[i] = exp(ene[i]-maxene) / *pks;
123
    }
124
}
125

    
126
static void GenerateWeightedGibbsProbabilities(real *ene, double *p_k, double *pks, int nlim, real *nvals, real delta)
127
{
128

    
129
    int   i;
130
    real  maxene;
131
    real *nene;
132
    *pks = 0.0;
133

    
134
    snew(nene, nlim);
135
    for (i = 0; i < nlim; i++)
136
    {
137
        if (nvals[i] == 0)
138
        {
139
            /* add the delta, since we need to make sure it's greater than zero, and
140
               we need a non-arbitrary number? */
141
            nene[i] = ene[i] + log(nvals[i]+delta);
142
        }
143
        else
144
        {
145
            nene[i] = ene[i] + log(nvals[i]);
146
        }
147
    }
148

    
149
    /* find the maximum value */
150
    maxene = nene[0];
151
    for (i = 0; i < nlim; i++)
152
    {
153
        if (nene[i] > maxene)
154
        {
155
            maxene = nene[i];
156
        }
157
    }
158

    
159
    /* subtract off the maximum, avoiding overflow */
160
    for (i = 0; i < nlim; i++)
161
    {
162
        nene[i] -= maxene;
163
    }
164

    
165
    /* find the denominator */
166
    for (i = 0; i < nlim; i++)
167
    {
168
        *pks += exp(nene[i]);
169
    }
170

    
171
    /*numerators*/
172
    for (i = 0; i < nlim; i++)
173
    {
174
        p_k[i] = exp(nene[i]) / *pks;
175
    }
176
    sfree(nene);
177
}
178

    
179
real do_logsum(int N, real *a_n)
180
{
181

    
182
    /*     RETURN VALUE */
183
    /* log(\sum_{i=0}^(N-1) exp[a_n]) */
184
    real maxarg;
185
    real sum;
186
    int  i;
187
    real logsum;
188
    /*     compute maximum argument to exp(.) */
189

    
190
    maxarg = a_n[0];
191
    for (i = 1; i < N; i++)
192
    {
193
        maxarg = max(maxarg, a_n[i]);
194
    }
195

    
196
    /* compute sum of exp(a_n - maxarg) */
197
    sum = 0.0;
198
    for (i = 0; i < N; i++)
199
    {
200
        sum = sum + exp(a_n[i] - maxarg);
201
    }
202

    
203
    /*     compute log sum */
204
    logsum = log(sum) + maxarg;
205
    return logsum;
206
}
207

    
208
int FindMinimum(real *min_metric, int N)
209
{
210

    
211
    real min_val;
212
    int  min_nval, nval;
213

    
214
    min_nval = 0;
215
    min_val  = min_metric[0];
216

    
217
    for (nval = 0; nval < N; nval++)
218
    {
219
        if (min_metric[nval] < min_val)
220
        {
221
            min_val  = min_metric[nval];
222
            min_nval = nval;
223
        }
224
    }
225
    return min_nval;
226
}
227

    
228
static gmx_bool CheckHistogramRatios(int nhisto, real *histo, real ratio)
229
{
230

    
231
    int      i;
232
    real     nmean;
233
    gmx_bool bIfFlat;
234

    
235
    nmean = 0;
236
    for (i = 0; i < nhisto; i++)
237
    {
238
        nmean += histo[i];
239
    }
240

    
241
    if (nmean == 0)
242
    {
243
        /* no samples! is bad!*/
244
        bIfFlat = FALSE;
245
        return bIfFlat;
246
    }
247
    nmean /= (real)nhisto;
248

    
249
    bIfFlat = TRUE;
250
    for (i = 0; i < nhisto; i++)
251
    {
252
        /* make sure that all points are in the ratio < x <  1/ratio range  */
253
        if (!((histo[i]/nmean < 1.0/ratio) && (histo[i]/nmean > ratio)))
254
        {
255
            bIfFlat = FALSE;
256
            break;
257
        }
258
    }
259
    return bIfFlat;
260
}
261

    
262
static gmx_bool CheckIfDoneEquilibrating(int nlim, t_expanded *expand, df_history_t *dfhist, gmx_int64_t step)
263
{
264

    
265
    int      i, totalsamples;
266
    gmx_bool bDoneEquilibrating = TRUE;
267
    gmx_bool bIfFlat;
268

    
269
    /* assume we have equilibrated the weights, then check to see if any of the conditions are not met */
270

    
271
    /* calculate the total number of samples */
272
    switch (expand->elmceq)
273
    {
274
        case elmceqNO:
275
            /* We have not equilibrated, and won't, ever. */
276
            return FALSE;
277
        case elmceqYES:
278
            /* we have equilibrated -- we're done */
279
            return TRUE;
280
        case elmceqSTEPS:
281
            /* first, check if we are equilibrating by steps, if we're still under */
282
            if (step < expand->equil_steps)
283
            {
284
                bDoneEquilibrating = FALSE;
285
            }
286
            break;
287
        case elmceqSAMPLES:
288
            totalsamples = 0;
289
            for (i = 0; i < nlim; i++)
290
            {
291
                totalsamples += dfhist->n_at_lam[i];
292
            }
293
            if (totalsamples < expand->equil_samples)
294
            {
295
                bDoneEquilibrating = FALSE;
296
            }
297
            break;
298
        case elmceqNUMATLAM:
299
            for (i = 0; i < nlim; i++)
300
            {
301
                if (dfhist->n_at_lam[i] < expand->equil_n_at_lam) /* we are still doing the initial sweep, so we're definitely not
302
                                                                     done equilibrating*/
303
                {
304
                    bDoneEquilibrating  = FALSE;
305
                    break;
306
                }
307
            }
308
            break;
309
        case elmceqWLDELTA:
310
            if (EWL(expand->elamstats)) /* This check is in readir as well, but
311
                                           just to be sure */
312
            {
313
                if (dfhist->wl_delta > expand->equil_wl_delta)
314
                {
315
                    bDoneEquilibrating = FALSE;
316
                }
317
            }
318
            break;
319
        case elmceqRATIO:
320
            /* we can use the flatness as a judge of good weights, as long as
321
               we're not doing minvar, or Wang-Landau.
322
               But turn off for now until we figure out exactly how we do this.
323
             */
324

    
325
            if (!(EWL(expand->elamstats) || expand->elamstats == elamstatsMINVAR))
326
            {
327
                /* we want to use flatness -avoiding- the forced-through samples.  Plus, we need to convert to
328
                   floats for this histogram function. */
329

    
330
                real *modhisto;
331
                snew(modhisto, nlim);
332
                for (i = 0; i < nlim; i++)
333
                {
334
                    modhisto[i] = 1.0*(dfhist->n_at_lam[i]-expand->lmc_forced_nstart);
335
                }
336
                bIfFlat = CheckHistogramRatios(nlim, modhisto, expand->equil_ratio);
337
                sfree(modhisto);
338
                if (!bIfFlat)
339
                {
340
                    bDoneEquilibrating = FALSE;
341
                }
342
            }
343
        default:
344
            bDoneEquilibrating = TRUE;
345
    }
346
    /* one last case to go though, if we are doing slow growth to get initial values, we haven't finished equilibrating */
347

    
348
    if (expand->lmc_forced_nstart > 0)
349
    {
350
        for (i = 0; i < nlim; i++)
351
        {
352
            if (dfhist->n_at_lam[i] < expand->lmc_forced_nstart) /* we are still doing the initial sweep, so we're definitely not
353
                                                                    done equilibrating*/
354
            {
355
                bDoneEquilibrating = FALSE;
356
                break;
357
            }
358
        }
359
    }
360
    return bDoneEquilibrating;
361
}
362

    
363
static gmx_bool UpdateWeights(int nlim, t_expanded *expand, df_history_t *dfhist,
364
                              int fep_state, real *scaled_lamee, real *weighted_lamee, gmx_int64_t step)
365
{
366
    real     maxdiff = 0.000000001;
367
    gmx_bool bSufficientSamples;
368
    int      i, k, n, nz, indexi, indexk, min_n, max_n, totali;
369
    int      n0, np1, nm1, nval, min_nvalm, min_nvalp, maxc;
370
    real     omega_m1_0, omega_p1_m1, omega_m1_p1, omega_p1_0, clam_osum;
371
    real     de, de_function, dr, denom, maxdr;
372
    real     min_val, cnval, zero_sum_weights;
373
    real    *omegam_array, *weightsm_array, *omegap_array, *weightsp_array, *varm_array, *varp_array, *dwp_array, *dwm_array;
374
    real     clam_varm, clam_varp, clam_weightsm, clam_weightsp, clam_minvar;
375
    real    *lam_weights, *lam_minvar_corr, *lam_variance, *lam_dg;
376
    double  *p_k;
377
    double   pks = 0;
378
    real    *numweighted_lamee, *logfrac;
379
    int     *nonzero;
380
    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;
381

    
382
    /* if we have equilibrated the weights, exit now */
383
    if (dfhist->bEquil)
384
    {
385
        return FALSE;
386
    }
387

    
388
    if (CheckIfDoneEquilibrating(nlim, expand, dfhist, step))
389
    {
390
        dfhist->bEquil = TRUE;
391
        /* zero out the visited states so we know how many equilibrated states we have
392
           from here on out.*/
393
        for (i = 0; i < nlim; i++)
394
        {
395
            dfhist->n_at_lam[i] = 0;
396
        }
397
        return TRUE;
398
    }
399

    
400
    /* If we reached this far, we have not equilibrated yet, keep on
401
       going resetting the weights */
402

    
403
    if (EWL(expand->elamstats))
404
    {
405
        if (expand->elamstats == elamstatsWL)  /* Standard Wang-Landau */
406
        {
407
            dfhist->sum_weights[fep_state] -= dfhist->wl_delta;
408
            dfhist->wl_histo[fep_state]    += 1.0;
409
        }
410
        else if (expand->elamstats == elamstatsWWL) /* Weighted Wang-Landau */
411
        {
412
            snew(p_k, nlim);
413

    
414
            /* first increment count */
415
            GenerateGibbsProbabilities(weighted_lamee, p_k, &pks, 0, nlim-1);
416
            for (i = 0; i < nlim; i++)
417
            {
418
                dfhist->wl_histo[i] += (real)p_k[i];
419
            }
420

    
421
            /* then increment weights (uses count) */
422
            pks = 0.0;
423
            GenerateWeightedGibbsProbabilities(weighted_lamee, p_k, &pks, nlim, dfhist->wl_histo, dfhist->wl_delta);
424

    
425
            for (i = 0; i < nlim; i++)
426
            {
427
                dfhist->sum_weights[i] -= dfhist->wl_delta*(real)p_k[i];
428
            }
429
            /* Alternate definition, using logarithms. Shouldn't make very much difference! */
430
            /*
431
               real di;
432
               for (i=0;i<nlim;i++)
433
               {
434
                di = (real)1.0 + dfhist->wl_delta*(real)p_k[i];
435
                dfhist->sum_weights[i] -= log(di);
436
               }
437
             */
438
            sfree(p_k);
439
        }
440

    
441
        zero_sum_weights =  dfhist->sum_weights[0];
442
        for (i = 0; i < nlim; i++)
443
        {
444
            dfhist->sum_weights[i] -= zero_sum_weights;
445
        }
446
    }
447

    
448
    if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMETROPOLIS || expand->elamstats == elamstatsMINVAR)
449
    {
450

    
451
        de_function = 0;  /* to get rid of warnings, but this value will not be used because of the logic */
452
        maxc        = 2*expand->c_range+1;
453

    
454
        snew(lam_dg, nlim);
455
        snew(lam_variance, nlim);
456

    
457
        snew(omegap_array, maxc);
458
        snew(weightsp_array, maxc);
459
        snew(varp_array, maxc);
460
        snew(dwp_array, maxc);
461

    
462
        snew(omegam_array, maxc);
463
        snew(weightsm_array, maxc);
464
        snew(varm_array, maxc);
465
        snew(dwm_array, maxc);
466

    
467
        /* unpack the current lambdas -- we will only update 2 of these */
468

    
469
        for (i = 0; i < nlim-1; i++)
470
        {   /* only through the second to last */
471
            lam_dg[i]       = dfhist->sum_dg[i+1] - dfhist->sum_dg[i];
472
            lam_variance[i] = pow(dfhist->sum_variance[i+1], 2) - pow(dfhist->sum_variance[i], 2);
473
        }
474

    
475
        /* accumulate running averages */
476
        for (nval = 0; nval < maxc; nval++)
477
        {
478
            /* constants for later use */
479
            cnval = (real)(nval-expand->c_range);
480
            /* actually, should be able to rewrite it w/o exponential, for better numerical stability */
481
            if (fep_state > 0)
482
            {
483
                de = exp(cnval - (scaled_lamee[fep_state]-scaled_lamee[fep_state-1]));
484
                if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMINVAR)
485
                {
486
                    de_function = 1.0/(1.0+de);
487
                }
488
                else if (expand->elamstats == elamstatsMETROPOLIS)
489
                {
490
                    if (de < 1.0)
491
                    {
492
                        de_function = 1.0;
493
                    }
494
                    else
495
                    {
496
                        de_function = 1.0/de;
497
                    }
498
                }
499
                dfhist->accum_m[fep_state][nval]  += de_function;
500
                dfhist->accum_m2[fep_state][nval] += de_function*de_function;
501
            }
502

    
503
            if (fep_state < nlim-1)
504
            {
505
                de = exp(-cnval + (scaled_lamee[fep_state+1]-scaled_lamee[fep_state]));
506
                if (expand->elamstats == elamstatsBARKER || expand->elamstats == elamstatsMINVAR)
507
                {
508
                    de_function = 1.0/(1.0+de);
509
                }
510
                else if (expand->elamstats == elamstatsMETROPOLIS)
511
                {
512
                    if (de < 1.0)
513
                    {
514
                        de_function = 1.0;
515
                    }
516
                    else
517
                    {
518
                        de_function = 1.0/de;
519
                    }
520
                }
521
                dfhist->accum_p[fep_state][nval]  += de_function;
522
                dfhist->accum_p2[fep_state][nval] += de_function*de_function;
523
            }
524

    
525
            /* Metropolis transition and Barker transition (unoptimized Bennett) acceptance weight determination */
526

    
527
            n0  = dfhist->n_at_lam[fep_state];
528
            if (fep_state > 0)
529
            {
530
                nm1 = dfhist->n_at_lam[fep_state-1];
531
            }
532
            else
533
            {
534
                nm1 = 0;
535
            }
536
            if (fep_state < nlim-1)
537
            {
538
                np1 = dfhist->n_at_lam[fep_state+1];
539
            }
540
            else
541
            {
542
                np1 = 0;
543
            }
544

    
545
            /* logic SHOULD keep these all set correctly whatever the logic, but apparently it can't figure it out. */
546
            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;
547

    
548
            if (n0 > 0)
549
            {
550
                chi_m1_0 = dfhist->accum_m[fep_state][nval]/n0;
551
                chi_p1_0 = dfhist->accum_p[fep_state][nval]/n0;
552
                chi_m2_0 = dfhist->accum_m2[fep_state][nval]/n0;
553
                chi_p2_0 = dfhist->accum_p2[fep_state][nval]/n0;
554
            }
555

    
556
            if ((fep_state > 0 ) && (nm1 > 0))
557
            {
558
                chi_p1_m1 = dfhist->accum_p[fep_state-1][nval]/nm1;
559
                chi_p2_m1 = dfhist->accum_p2[fep_state-1][nval]/nm1;
560
            }
561

    
562
            if ((fep_state < nlim-1) && (np1 > 0))
563
            {
564
                chi_m1_p1 = dfhist->accum_m[fep_state+1][nval]/np1;
565
                chi_m2_p1 = dfhist->accum_m2[fep_state+1][nval]/np1;
566
            }
567

    
568
            omega_m1_0    = 0;
569
            omega_p1_0    = 0;
570
            clam_weightsm = 0;
571
            clam_weightsp = 0;
572
            clam_varm     = 0;
573
            clam_varp     = 0;
574

    
575
            if (fep_state > 0)
576
            {
577
                if (n0 > 0)
578
                {
579
                    omega_m1_0 = chi_m2_0/(chi_m1_0*chi_m1_0) - 1.0;
580
                }
581
                if (nm1 > 0)
582
                {
583
                    omega_p1_m1 = chi_p2_m1/(chi_p1_m1*chi_p1_m1) - 1.0;
584
                }
585
                if ((n0 > 0) && (nm1 > 0))
586
                {
587
                    clam_weightsm = (log(chi_m1_0) - log(chi_p1_m1)) + cnval;
588
                    clam_varm     = (1.0/n0)*(omega_m1_0) + (1.0/nm1)*(omega_p1_m1);
589
                }
590
            }
591

    
592
            if (fep_state < nlim-1)
593
            {
594
                if (n0 > 0)
595
                {
596
                    omega_p1_0 = chi_p2_0/(chi_p1_0*chi_p1_0) - 1.0;
597
                }
598
                if (np1 > 0)
599
                {
600
                    omega_m1_p1 = chi_m2_p1/(chi_m1_p1*chi_m1_p1) - 1.0;
601
                }
602
                if ((n0 > 0) && (np1 > 0))
603
                {
604
                    clam_weightsp = (log(chi_m1_p1) - log(chi_p1_0)) + cnval;
605
                    clam_varp     = (1.0/np1)*(omega_m1_p1) + (1.0/n0)*(omega_p1_0);
606
                }
607
            }
608

    
609
            if (n0 > 0)
610
            {
611
                omegam_array[nval]             = omega_m1_0;
612
            }
613
            else
614
            {
615
                omegam_array[nval]             = 0;
616
            }
617
            weightsm_array[nval]           = clam_weightsm;
618
            varm_array[nval]               = clam_varm;
619
            if (nm1 > 0)
620
            {
621
                dwm_array[nval]  = fabs( (cnval + log((1.0*n0)/nm1)) - lam_dg[fep_state-1] );
622
            }
623
            else
624
            {
625
                dwm_array[nval]  = fabs( cnval - lam_dg[fep_state-1] );
626
            }
627

    
628
            if (n0 > 0)
629
            {
630
                omegap_array[nval]             = omega_p1_0;
631
            }
632
            else
633
            {
634
                omegap_array[nval]             = 0;
635
            }
636
            weightsp_array[nval]           = clam_weightsp;
637
            varp_array[nval]               = clam_varp;
638
            if ((np1 > 0) && (n0 > 0))
639
            {
640
                dwp_array[nval]  = fabs( (cnval + log((1.0*np1)/n0)) - lam_dg[fep_state] );
641
            }
642
            else
643
            {
644
                dwp_array[nval]  = fabs( cnval - lam_dg[fep_state] );
645
            }
646

    
647
        }
648

    
649
        /* find the C's closest to the old weights value */
650

    
651
        min_nvalm     = FindMinimum(dwm_array, maxc);
652
        omega_m1_0    = omegam_array[min_nvalm];
653
        clam_weightsm = weightsm_array[min_nvalm];
654
        clam_varm     = varm_array[min_nvalm];
655

    
656
        min_nvalp     = FindMinimum(dwp_array, maxc);
657
        omega_p1_0    = omegap_array[min_nvalp];
658
        clam_weightsp = weightsp_array[min_nvalp];
659
        clam_varp     = varp_array[min_nvalp];
660

    
661
        clam_osum   = omega_m1_0 + omega_p1_0;
662
        clam_minvar = 0;
663
        if (clam_osum > 0)
664
        {
665
            clam_minvar = 0.5*log(clam_osum);
666
        }
667

    
668
        if (fep_state > 0)
669
        {
670
            lam_dg[fep_state-1]       = clam_weightsm;
671
            lam_variance[fep_state-1] = clam_varm;
672
        }
673

    
674
        if (fep_state < nlim-1)
675
        {
676
            lam_dg[fep_state]       = clam_weightsp;
677
            lam_variance[fep_state] = clam_varp;
678
        }
679

    
680
        if (expand->elamstats == elamstatsMINVAR)
681
        {
682
            bSufficientSamples = TRUE;
683
            /* make sure they are all past a threshold */
684
            for (i = 0; i < nlim; i++)
685
            {
686
                if (dfhist->n_at_lam[i] < expand->minvarmin)
687
                {
688
                    bSufficientSamples = FALSE;
689
                }
690
            }
691
            if (bSufficientSamples)
692
            {
693
                dfhist->sum_minvar[fep_state] = clam_minvar;
694
                if (fep_state == 0)
695
                {
696
                    for (i = 0; i < nlim; i++)
697
                    {
698
                        dfhist->sum_minvar[i] += (expand->minvar_const-clam_minvar);
699
                    }
700
                    expand->minvar_const          = clam_minvar;
701
                    dfhist->sum_minvar[fep_state] = 0.0;
702
                }
703
                else
704
                {
705
                    dfhist->sum_minvar[fep_state] -= expand->minvar_const;
706
                }
707
            }
708
        }
709

    
710
        /* we need to rezero minvar now, since it could change at fep_state = 0 */
711
        dfhist->sum_dg[0]       = 0.0;
712
        dfhist->sum_variance[0] = 0.0;
713
        dfhist->sum_weights[0]  = dfhist->sum_dg[0] + dfhist->sum_minvar[0]; /* should be zero */
714

    
715
        for (i = 1; i < nlim; i++)
716
        {
717
            dfhist->sum_dg[i]       = lam_dg[i-1] + dfhist->sum_dg[i-1];
718
            dfhist->sum_variance[i] = sqrt(lam_variance[i-1] + pow(dfhist->sum_variance[i-1], 2));
719
            dfhist->sum_weights[i]  = dfhist->sum_dg[i] + dfhist->sum_minvar[i];
720
        }
721

    
722
        sfree(lam_dg);
723
        sfree(lam_variance);
724

    
725
        sfree(omegam_array);
726
        sfree(weightsm_array);
727
        sfree(varm_array);
728
        sfree(dwm_array);
729

    
730
        sfree(omegap_array);
731
        sfree(weightsp_array);
732
        sfree(varp_array);
733
        sfree(dwp_array);
734
    }
735
    return FALSE;
736
}
737

    
738

    
739
static int AIMChooseNewLambda(int nlim, t_expanded *expand, df_history_t *dfhist, int fep_state, real *weighted_lamee,
740
                           gmx_int64_t seed, gmx_int64_t step, gmx_enerdata_t *enerd)
741
{
742
    /* Choose new lambda value, and update transition matrix */
743

    
744
    int      i, j, ifep, jfep, minfep, maxfep, lamnew, lamtrial, starting_fep_state;
745
    real     r1, r2, avg, delta, de, df, trialprob, tprob, beta;
746
    real   **Tij;
747
    double  *propose, *accept, *remainder;
748
    double   pks;
749
    real     sum, pnorm;
750
    gmx_bool bRestricted;
751

    
752
    starting_fep_state = fep_state; /* so we can track lambda change */
753
    
754
    snew(propose, nlim);
755
    snew(accept, nlim);
756
    snew(remainder, nlim);
757
  
758
    /* let's store the current potential energy in case we want 
759
     * to use it for de */ 
760
    dfhist->store_fepot[fep_state] = enerd->term[F_EPOT];
761

    
762
    double rnd[2];
763

    
764
    gmx_rng_cycle_2uniform(step, i, seed, RND_SEED_EXPANDED, rnd);
765

    
766
     /* use the metropolis sampler with trial +/- 1 */
767
    r1 = rnd[0];
768
    if (r1 < 0.5)
769
    {
770
        if (fep_state == 0)
771
        {
772
            lamtrial = fep_state;
773
        }
774
        else
775
        {
776
            lamtrial = fep_state-1;
777
        }
778
    }
779
    else
780
    {
781
        if (fep_state == nlim-1)
782
        {
783
            lamtrial = fep_state;
784
        }
785
        else
786
        {
787
            lamtrial = fep_state+1;
788
        }
789
    }
790

    
791

    
792
    // AIM step
793
    // de = U(x)_lambdaNew - U(x)_lambdaOld
794
    //
795
    // this de is used by metropolis mover which is why i think it's
796
    // actually the correct value
797
    de = weighted_lamee[lamtrial]-weighted_lamee[fep_state];
798

    
799
    // df = estimate of the integral from lambdaOld to lambdaNew of the average
800
    // dU/dlambda
801
    //
802
    // the dfavg[] array is updated with the enerd->term[F_DVDL] term
803
    // that I have asked about in the developer list and have found in mdebin.c.
804
    // That term is expected to be dhdl.
805
    // if using weighted lamee we need to scale by 1/kT
806
    df = (0.5*(lamtrial-fep_state)*(1.0/(nlim-1.0))*(dfhist->dfavg[lamtrial]+dfhist->dfavg[fep_state]))/(expand->mc_temp*BOLTZ);
807

    
808

    
809
    // the acceptance probability is min{1.0,exp(-beta*de)*exp(beta*df)
810
    // de is already negative and scaled by beta 
811
    // df is already scaled by beta
812
    trialprob = exp(de+df);
813
 
814

    
815
    // testing that trialprob is less than 1 seems redundant but it's the 
816
    // convention of the other lmcmovers so we'll do it this way for 
817
    // consistency.
818
    tprob = 1.0; 
819
    if (trialprob < tprob)
820
    {
821
        tprob = trialprob;
822
    }
823

    
824
    /* randomly accept the new trial move in the fashion of Metropolis  */
825
    r2 = rnd[1];
826
    if (r2 < tprob)
827
    {
828
        lamnew = lamtrial;
829
        //dfhist->laccept[lamtrial]++; //update the acceptance count at lambda new
830
    }
831
    else
832
    {
833
        lamnew = fep_state;
834
    }
835

    
836
    
837
    /* At this point we either have a new value in lamnew or it's the old value,
838
     * either way we'll update the fep_state term but we could also use
839
     * the lamnew term just as easily */
840
    fep_state = lamnew;
841
    // update the count at fep_state
842
    dfhist->aim_at_lam[fep_state] ++;
843
    
844
    /* Free up any memory tha we no longer need */
845
    sfree(propose);
846
    sfree(accept);
847
    sfree(remainder);
848

    
849
    /* Use a running average of dhdl to smooth out any short term fluctuations */
850
    delta = enerd->term[F_DVDL]-dfhist->dfavg[fep_state];
851
    /* Store the average in dfavg[] in order to be used for the calculation
852
     * of df  */
853
    dfhist->dfavg[fep_state] += delta/dfhist->aim_at_lam[fep_state];
854
       
855
    // end program, return the value of lamnew which is convention
856

    
857
    return lamnew;
858
}
859

    
860
static int ChooseNewLambda(int nlim, t_expanded *expand, df_history_t *dfhist, int fep_state, real *weighted_lamee, double *p_k,
861
                           gmx_int64_t seed, gmx_int64_t step)
862
{
863
    /* Choose new lambda value, and update transition matrix */
864

    
865
    int      i, ifep, jfep, minfep, maxfep, lamnew, lamtrial, starting_fep_state;
866
    real     r1, r2, de_old, de_new, de, trialprob, tprob = 0;
867
    real   **Tij;
868
    double  *propose, *accept, *remainder;
869
    double   pks;
870
    real     sum, pnorm;
871
    gmx_bool bRestricted;
872

    
873
    starting_fep_state = fep_state;
874
    lamnew             = fep_state; /* so that there is a default setting -- stays the same */
875

    
876
    if (!EWL(expand->elamstats))    /* ignore equilibrating the weights if using WL */
877
    {
878
        if ((expand->lmc_forced_nstart > 0) && (dfhist->n_at_lam[nlim-1] <= expand->lmc_forced_nstart))
879
        {
880
            /* Use a marching method to run through the lambdas and get preliminary free energy data,
881
               before starting 'free' sampling.  We start free sampling when we have enough at each lambda */
882

    
883
            /* if we have enough at this lambda, move on to the next one */
884

    
885
            if (dfhist->n_at_lam[fep_state] == expand->lmc_forced_nstart)
886
            {
887
                lamnew = fep_state+1;
888
                if (lamnew == nlim)  /* whoops, stepped too far! */
889
                {
890
                    lamnew -= 1;
891
                }
892
            }
893
            else
894
            {
895
                lamnew = fep_state;
896
            }
897
            return lamnew;
898
        }
899
    }
900

    
901
    snew(propose, nlim);
902
    snew(accept, nlim);
903
    snew(remainder, nlim);
904

    
905
    for (i = 0; i < expand->lmc_repeats; i++)
906
    {
907
        double rnd[2];
908

    
909
        gmx_rng_cycle_2uniform(step, i, seed, RND_SEED_EXPANDED, rnd);
910

    
911
        for (ifep = 0; ifep < nlim; ifep++)
912
        {
913
            propose[ifep] = 0;
914
            accept[ifep]  = 0;
915
        }
916

    
917
        if ((expand->elmcmove == elmcmoveGIBBS) || (expand->elmcmove == elmcmoveMETGIBBS))
918
        {
919
            bRestricted = TRUE;
920
            /* use the Gibbs sampler, with restricted range */
921
            if (expand->gibbsdeltalam < 0)
922
            {
923
                minfep      = 0;
924
                maxfep      = nlim-1;
925
                bRestricted = FALSE;
926
            }
927
            else
928
            {
929
                minfep = fep_state - expand->gibbsdeltalam;
930
                maxfep = fep_state + expand->gibbsdeltalam;
931
                if (minfep < 0)
932
                {
933
                    minfep = 0;
934
                }
935
                if (maxfep > nlim-1)
936
                {
937
                    maxfep = nlim-1;
938
                }
939
            }
940

    
941
            GenerateGibbsProbabilities(weighted_lamee, p_k, &pks, minfep, maxfep);
942

    
943
            if (expand->elmcmove == elmcmoveGIBBS)
944
            {
945
                for (ifep = minfep; ifep <= maxfep; ifep++)
946
                {
947
                    propose[ifep] = p_k[ifep];
948
                    accept[ifep]  = 1.0;
949
                }
950
                /* Gibbs sampling */
951
                r1 = rnd[0];
952
                for (lamnew = minfep; lamnew <= maxfep; lamnew++)
953
                {
954
                    if (r1 <= p_k[lamnew])
955
                    {
956
                        break;
957
                    }
958
                    r1 -= p_k[lamnew];
959
                }
960
            }
961
            else if (expand->elmcmove == elmcmoveMETGIBBS)
962
            {
963

    
964
                /* Metropolized Gibbs sampling */
965
                for (ifep = minfep; ifep <= maxfep; ifep++)
966
                {
967
                    remainder[ifep] = 1 - p_k[ifep];
968
                }
969

    
970
                /* find the proposal probabilities */
971

    
972
                if (remainder[fep_state] == 0)
973
                {
974
                    /* only the current state has any probability */
975
                    /* we have to stay at the current state */
976
                    lamnew = fep_state;
977
                }
978
                else
979
                {
980
                    for (ifep = minfep; ifep <= maxfep; ifep++)
981
                    {
982
                        if (ifep != fep_state)
983
                        {
984
                            propose[ifep] = p_k[ifep]/remainder[fep_state];
985
                        }
986
                        else
987
                        {
988
                            propose[ifep] = 0;
989
                        }
990
                    }
991

    
992
                    r1 = rnd[0];
993
                    for (lamtrial = minfep; lamtrial <= maxfep; lamtrial++)
994
                    {
995
                        pnorm = p_k[lamtrial]/remainder[fep_state];
996
                        if (lamtrial != fep_state)
997
                        {
998
                            if (r1 <= pnorm)
999
                            {
1000
                                break;
1001
                            }
1002
                            r1 -= pnorm;
1003
                        }
1004
                    }
1005

    
1006
                    /* we have now selected lamtrial according to p(lamtrial)/1-p(fep_state) */
1007
                    tprob = 1.0;
1008
                    /* trial probability is min{1,\frac{1 - p(old)}{1-p(new)} MRS 1/8/2008 */
1009
                    trialprob = (remainder[fep_state])/(remainder[lamtrial]);
1010
                    if (trialprob < tprob)
1011
                    {
1012
                        tprob = trialprob;
1013
                    }
1014
                    r2 = rnd[1];
1015
                    if (r2 < tprob)
1016
                    {
1017
                        lamnew = lamtrial;
1018
                    }
1019
                    else
1020
                    {
1021
                        lamnew = fep_state;
1022
                    }
1023
                }
1024

    
1025
                /* now figure out the acceptance probability for each */
1026
                for (ifep = minfep; ifep <= maxfep; ifep++)
1027
                {
1028
                    tprob = 1.0;
1029
                    if (remainder[ifep] != 0)
1030
                    {
1031
                        trialprob = (remainder[fep_state])/(remainder[ifep]);
1032
                    }
1033
                    else
1034
                    {
1035
                        trialprob = 1.0; /* this state is the only choice! */
1036
                    }
1037
                    if (trialprob < tprob)
1038
                    {
1039
                        tprob = trialprob;
1040
                    }
1041
                    /* probability for fep_state=0, but that's fine, it's never proposed! */
1042
                    accept[ifep] = tprob;
1043
                }
1044
            }
1045

    
1046
            if (lamnew > maxfep)
1047
            {
1048
                /* it's possible some rounding is failing */
1049
                if (gmx_within_tol(remainder[fep_state], 0, 50*GMX_DOUBLE_EPS))
1050
                {
1051
                    /* numerical rounding error -- no state other than the original has weight */
1052
                    lamnew = fep_state;
1053
                }
1054
                else
1055
                {
1056
                    /* probably not a numerical issue */
1057
                    int   loc    = 0;
1058
                    int   nerror = 200+(maxfep-minfep+1)*60;
1059
                    char *errorstr;
1060
                    snew(errorstr, nerror);
1061
                    /* if its greater than maxfep, then something went wrong -- probably underflow in the calculation
1062
                       of sum weights. Generated detailed info for failure */
1063
                    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);
1064
                    for (ifep = minfep; ifep <= maxfep; ifep++)
1065
                    {
1066
                        loc += sprintf(&errorstr[loc], "%3d %17.10e%17.10e%17.10e\n", ifep, weighted_lamee[ifep], p_k[ifep], dfhist->sum_weights[ifep]);
1067
                    }
1068
                    gmx_fatal(FARGS, errorstr);
1069
                }
1070
            }
1071
        }
1072
        else if ((expand->elmcmove == elmcmoveMETROPOLIS) || (expand->elmcmove == elmcmoveBARKER))
1073
        {
1074
            /* use the metropolis sampler with trial +/- 1 */
1075
            r1 = rnd[0];
1076
            if (r1 < 0.5)
1077
            {
1078
                if (fep_state == 0)
1079
                {
1080
                    lamtrial = fep_state;
1081
                }
1082
                else
1083
                {
1084
                    lamtrial = fep_state-1;
1085
                }
1086
            }
1087
            else
1088
            {
1089
                if (fep_state == nlim-1)
1090
                {
1091
                    lamtrial = fep_state;
1092
                }
1093
                else
1094
                {
1095
                    lamtrial = fep_state+1;
1096
                }
1097
            }
1098

    
1099
            de = weighted_lamee[lamtrial] - weighted_lamee[fep_state];
1100
            if (expand->elmcmove == elmcmoveMETROPOLIS)
1101
            {
1102
                tprob     = 1.0;
1103
                trialprob = exp(de);
1104
                if (trialprob < tprob)
1105
                {
1106
                    tprob = trialprob;
1107
                }
1108
                propose[fep_state] = 0;
1109
                propose[lamtrial]  = 1.0; /* note that this overwrites the above line if fep_state = ntrial, which only occurs at the ends */
1110
                accept[fep_state]  = 1.0; /* doesn't actually matter, never proposed unless fep_state = ntrial, in which case it's 1.0 anyway */
1111
                accept[lamtrial]   = tprob;
1112

    
1113
            }
1114
            else if (expand->elmcmove == elmcmoveBARKER)
1115
            {
1116
                tprob = 1.0/(1.0+exp(-de));
1117

    
1118
                propose[fep_state] = (1-tprob);
1119
                propose[lamtrial] += tprob; /* we add, to account for the fact that at the end, they might be the same point */
1120
                accept[fep_state]  = 1.0;
1121
                accept[lamtrial]   = 1.0;
1122
            }
1123
            
1124
            r2 = rnd[1];
1125
            if (r2 < tprob)
1126
            {
1127
                lamnew = lamtrial;
1128
            }
1129
            else
1130
            {
1131
                lamnew = fep_state; 
1132
            }
1133

    
1134
        }
1135

    
1136
        for (ifep = 0; ifep < nlim; ifep++)
1137
        {
1138
            dfhist->Tij[fep_state][ifep]      += propose[ifep]*accept[ifep];
1139
            dfhist->Tij[fep_state][fep_state] += propose[ifep]*(1.0-accept[ifep]);
1140
        }
1141
        fep_state = lamnew;
1142
    }
1143

    
1144
    dfhist->Tij_empirical[starting_fep_state][lamnew] += 1.0;
1145

    
1146
    sfree(propose);
1147
    sfree(accept);
1148
    sfree(remainder);
1149

    
1150
    return lamnew;
1151
}
1152

    
1153
/* print out the weights to the log, along with current state */
1154
extern void PrintFreeEnergyInfoToFile(FILE *outfile, t_lambda *fep, t_expanded *expand, t_simtemp *simtemp, df_history_t *dfhist,
1155
                                      int fep_state, int frequency, gmx_int64_t step)
1156
{
1157
    int         nlim, i, ifep, jfep;
1158
    real        dw, dg, dv, dm, Tprint;
1159
    real       *temps;
1160
    const char *print_names[efptNR] = {" FEPL", "MassL", "CoulL", " VdwL", "BondL", "RestT", "Temp.(K)"};
1161
    gmx_bool    bSimTemp            = FALSE;
1162

    
1163
    nlim = fep->n_lambda;
1164
    if (simtemp != NULL)
1165
    {
1166
        bSimTemp = TRUE;
1167
    }
1168

    
1169
    if (mod(step, frequency) == 0)
1170
    {
1171
        fprintf(outfile, "             MC-lambda information\n");
1172
        if (EWL(expand->elamstats) && (!(dfhist->bEquil)))
1173
        {
1174
            fprintf(outfile, "  Wang-Landau incrementor is: %11.5g\n", dfhist->wl_delta);
1175
        }
1176
        fprintf(outfile, "  N");
1177
        for (i = 0; i < efptNR; i++)
1178
        {
1179
            if (fep->separate_dvdl[i])
1180
            {
1181
                fprintf(outfile, "%7s", print_names[i]);
1182
            }
1183
            else if ((i == efptTEMPERATURE) && bSimTemp)
1184
            {
1185
                fprintf(outfile, "%10s", print_names[i]); /* more space for temperature formats */
1186
            }
1187
        }
1188
        fprintf(outfile, "    Count   ");
1189
        if (expand->elamstats == elamstatsMINVAR)
1190
        {
1191
            fprintf(outfile, "W(in kT)   G(in kT)  dG(in kT)  dV(in kT)\n");
1192
        }
1193
        else
1194
        {
1195
            if (expand->elmcmove == elmcmoveAIM)
1196
            {
1197
                fprintf(outfile, "G(in kT)  dG(in kJ/mol) \n");
1198
            }
1199
            else
1200
            {
1201
                fprintf(outfile, "G(in kT)  dG(in kT) \n");
1202
            }
1203
        }
1204
        for (ifep = 0; ifep < nlim; ifep++)
1205
        {
1206
            if (ifep == nlim-1)
1207
            {
1208
                dw = 0.0;
1209
                dg = 0.0;
1210
                dv = 0.0;
1211
                dm = 0.0;
1212
            }
1213
            else
1214
            {
1215
                if (expand->elmcmove == elmcmoveAIM)
1216
                {
1217
                    dw = dfhist->dfavg[ifep];
1218
                }
1219
                else
1220
                {
1221
                    dw = dfhist->sum_weights[ifep+1] - dfhist->sum_weights[ifep];
1222
                }
1223
                dg = dfhist->sum_dg[ifep+1] - dfhist->sum_dg[ifep];
1224
                dv = sqrt(pow(dfhist->sum_variance[ifep+1], 2) - pow(dfhist->sum_variance[ifep], 2));
1225
                dm = dfhist->sum_minvar[ifep+1] - dfhist->sum_minvar[ifep];
1226
            }
1227
            fprintf(outfile, "%3d", (ifep+1));
1228
            for (i = 0; i < efptNR; i++)
1229
            {
1230
                if (fep->separate_dvdl[i])
1231
                {
1232
                    fprintf(outfile, "%7.3f", fep->all_lambda[i][ifep]);
1233
                }
1234
                else if (i == efptTEMPERATURE && bSimTemp)
1235
                {
1236
                    fprintf(outfile, "%9.3f", simtemp->temperatures[ifep]);
1237
                }
1238
            }
1239
            if (EWL(expand->elamstats) && (!(dfhist->bEquil)))  /* if performing WL and still haven't equilibrated */
1240
            {
1241
                if (expand->elamstats == elamstatsWL)
1242
                {
1243
                    fprintf(outfile, " %8d", (int)dfhist->wl_histo[ifep]);
1244
                }
1245
                else
1246
                {
1247
                    fprintf(outfile, " %8.3f", dfhist->wl_histo[ifep]);
1248
                }
1249
            }
1250
            else   /* we have equilibrated weights */
1251
            {
1252
                fprintf(outfile, " %8d", dfhist->n_at_lam[ifep]);
1253
            }
1254
            if (expand->elamstats == elamstatsMINVAR)
1255
            {
1256
                fprintf(outfile, " %10.5f %10.5f %10.5f %10.5f", dfhist->sum_weights[ifep], dfhist->sum_dg[ifep], dg, dv);
1257
            }
1258
            else
1259
            {    
1260
                fprintf(outfile, " %10.5f %10.5f ", dfhist->sum_weights[ifep], dw );
1261
            }
1262
            if (ifep == fep_state)
1263
            {
1264
                fprintf(outfile, " <<\n");
1265
            }
1266
            else
1267
            {
1268
                fprintf(outfile, "   \n");
1269
            }
1270
        }
1271
        fprintf(outfile, "\n");
1272

    
1273
        if ((mod(step, expand->nstTij) == 0) && (expand->nstTij > 0) && (step > 0))
1274
        {
1275
            fprintf(outfile, "                     Transition Matrix\n");
1276
            for (ifep = 0; ifep < nlim; ifep++)
1277
            {
1278
                fprintf(outfile, "%12d", (ifep+1));
1279
            }
1280
            fprintf(outfile, "\n");
1281
            for (ifep = 0; ifep < nlim; ifep++)
1282
            {
1283
                for (jfep = 0; jfep < nlim; jfep++)
1284
                {
1285
                    if (dfhist->n_at_lam[ifep] > 0)
1286
                    {
1287
                        if (expand->bSymmetrizedTMatrix)
1288
                        {
1289
                            Tprint = (dfhist->Tij[ifep][jfep]+dfhist->Tij[jfep][ifep])/(dfhist->n_at_lam[ifep]+dfhist->n_at_lam[jfep]);
1290
                        }
1291
                        else
1292
                        {
1293
                            Tprint = (dfhist->Tij[ifep][jfep])/(dfhist->n_at_lam[ifep]);
1294
                        }
1295
                    }
1296
                    else
1297
                    {
1298
                        Tprint = 0.0;
1299
                    }
1300
                    fprintf(outfile, "%12.8f", Tprint);
1301
                }
1302
                fprintf(outfile, "%3d\n", (ifep+1));
1303
            }
1304

    
1305
            fprintf(outfile, "                  Empirical Transition Matrix\n");
1306
            for (ifep = 0; ifep < nlim; ifep++)
1307
            {
1308
                fprintf(outfile, "%12d", (ifep+1));
1309
            }
1310
            fprintf(outfile, "\n");
1311
            for (ifep = 0; ifep < nlim; ifep++)
1312
            {
1313
                for (jfep = 0; jfep < nlim; jfep++)
1314
                {
1315
                    if (dfhist->n_at_lam[ifep] > 0)
1316
                    {
1317
                        if (expand->bSymmetrizedTMatrix)
1318
                        {
1319
                            Tprint = (dfhist->Tij_empirical[ifep][jfep]+dfhist->Tij_empirical[jfep][ifep])/(dfhist->n_at_lam[ifep]+dfhist->n_at_lam[jfep]);
1320
                        }
1321
                        else
1322
                        {
1323
                            Tprint = dfhist->Tij_empirical[ifep][jfep]/(dfhist->n_at_lam[ifep]);
1324
                        }
1325
                    }
1326
                    else
1327
                    {
1328
                        Tprint = 0.0;
1329
                    }
1330
                    fprintf(outfile, "%12.8f", Tprint);
1331
                }
1332
                fprintf(outfile, "%3d\n", (ifep+1));
1333
            }
1334
        }
1335
    }
1336
}
1337

    
1338
extern int ExpandedEnsembleDynamics(FILE *log, t_inputrec *ir, gmx_enerdata_t *enerd,
1339
                                    t_state *state, t_extmass *MassQ, int fep_state, df_history_t *dfhist,
1340
                                    gmx_int64_t step,
1341
                                    rvec *v, t_mdatoms *mdatoms)
1342
/* Note that the state variable is only needed for simulated tempering, not
1343
   Hamiltonian expanded ensemble.  May be able to remove it after integrator refactoring. */
1344
{
1345
    real       *pfep_lamee, *scaled_lamee, *weighted_lamee;
1346
    double     *p_k;
1347
    int         i, nlim, lamnew, totalsamples;
1348
    real        oneovert, maxscaled = 0, maxweighted = 0;
1349
    t_expanded *expand;
1350
    t_simtemp  *simtemp;
1351
    double     *temperature_lambdas;
1352
    gmx_bool    bIfReset, bSwitchtoOneOverT, bDoneEquilibrating = FALSE;
1353

    
1354
    expand  = ir->expandedvals;
1355
    simtemp = ir->simtempvals;
1356
    nlim    = ir->fepvals->n_lambda;
1357

    
1358
    snew(scaled_lamee, nlim);
1359
    snew(weighted_lamee, nlim);
1360
    snew(pfep_lamee, nlim);
1361
    snew(p_k, nlim);
1362

    
1363
    /* update the count at the current lambda*/
1364
    dfhist->n_at_lam[fep_state]++;
1365

    
1366
    /* need to calculate the PV term somewhere, but not needed here? Not until there's a lambda state that's
1367
       pressure controlled.*/
1368
    /*
1369
       pVTerm = 0;
1370
       where does this PV term go?
1371
       for (i=0;i<nlim;i++)
1372
       {
1373
       fep_lamee[i] += pVTerm;
1374
       }
1375
     */
1376

    
1377
    /* determine the minimum value to avoid overflow.  Probably a better way to do this */
1378
    /* we don't need to include the pressure term, since the volume is the same between the two.
1379
       is there some term we are neglecting, however? */
1380

    
1381
    if (ir->efep != efepNO)
1382
    {
1383
        for (i = 0; i < nlim; i++)
1384
        {
1385
            if (ir->bSimTemp)
1386
            {
1387
                /* Note -- this assumes no mass changes, since kinetic energy is not added  . . . */
1388
                scaled_lamee[i] = (enerd->enerpart_lambda[i+1]-enerd->enerpart_lambda[0])/(simtemp->temperatures[i]*BOLTZ)
1389
                    + enerd->term[F_EPOT]*(1.0/(simtemp->temperatures[i])- 1.0/(simtemp->temperatures[fep_state]))/BOLTZ;
1390
            }
1391
            else
1392
            {
1393
                scaled_lamee[i] = (enerd->enerpart_lambda[i+1]-enerd->enerpart_lambda[0])/(expand->mc_temp*BOLTZ);
1394
                /* mc_temp is currently set to the system reft unless otherwise defined */
1395
            }
1396

    
1397
            /* save these energies for printing, so they don't get overwritten by the next step */
1398
            /* they aren't overwritten in the non-free energy case, but we always print with these
1399
               for simplicity */
1400
        }
1401
    }
1402
    else
1403
    {
1404
        if (ir->bSimTemp)
1405
        {
1406
            for (i = 0; i < nlim; i++)
1407
            {
1408
                scaled_lamee[i] = enerd->term[F_EPOT]*(1.0/simtemp->temperatures[i] - 1.0/simtemp->temperatures[fep_state])/BOLTZ;
1409
            }
1410
        }
1411
    }
1412

    
1413
    for (i = 0; i < nlim; i++)
1414
    {
1415
        pfep_lamee[i] = scaled_lamee[i];
1416

    
1417
        weighted_lamee[i] = dfhist->sum_weights[i] - scaled_lamee[i];
1418
        if (i == 0)
1419
        {
1420
            maxscaled   = scaled_lamee[i];
1421
            maxweighted = weighted_lamee[i];
1422
        }
1423
        else
1424
        {
1425
            if (scaled_lamee[i] > maxscaled)
1426
            {
1427
                maxscaled = scaled_lamee[i];
1428
            }
1429
            if (weighted_lamee[i] > maxweighted)
1430
            {
1431
                maxweighted = weighted_lamee[i];
1432
            }
1433
        }
1434
    }
1435

    
1436
    for (i = 0; i < nlim; i++)
1437
    {
1438
        scaled_lamee[i]   -= maxscaled;
1439
        weighted_lamee[i] -= maxweighted;
1440
    }
1441

    
1442
    /* update weights - we decide whether or not to actually do this inside */
1443

    
1444
    bDoneEquilibrating = UpdateWeights(nlim, expand, dfhist, fep_state, scaled_lamee, weighted_lamee, step);
1445
    if (bDoneEquilibrating)
1446
    {
1447
        if (log)
1448
        {
1449
            fprintf(log, "\nStep %d: Weights have equilibrated, using criteria: %s\n", (int)step, elmceq_names[expand->elmceq]);
1450
        }
1451
    }
1452
   
1453

    
1454
    if (expand->elmcmove == elmcmoveAIM)
1455
    {
1456

    
1457
    lamnew = AIMChooseNewLambda(nlim, expand, dfhist, fep_state, weighted_lamee, 
1458
                             ir->expandedvals->lmc_seed, step, enerd);
1459
    }
1460
    else
1461
    {
1462

    
1463
    lamnew = ChooseNewLambda(nlim, expand, dfhist, fep_state, weighted_lamee, p_k,
1464
                             ir->expandedvals->lmc_seed, step);
1465
    }
1466

    
1467
    /* if using simulated tempering, we need to adjust the temperatures */
1468
    if (ir->bSimTemp && (lamnew != fep_state)) /* only need to change the temperatures if we change the state */
1469
    {
1470
        int   i, j, n, d;
1471
        real *buf_ngtc;
1472
        real  told;
1473
        int   nstart, nend, gt;
1474

    
1475
        snew(buf_ngtc, ir->opts.ngtc);
1476

    
1477
        for (i = 0; i < ir->opts.ngtc; i++)
1478
        {
1479
            if (ir->opts.ref_t[i] > 0)
1480
            {
1481
                told              = ir->opts.ref_t[i];
1482
                ir->opts.ref_t[i] =  simtemp->temperatures[lamnew];
1483
                buf_ngtc[i]       = sqrt(ir->opts.ref_t[i]/told); /* using the buffer as temperature scaling */
1484
            }
1485
        }
1486

    
1487
        /* we don't need to manipulate the ekind information, as it isn't due to be reset until the next step anyway */
1488

    
1489
        nstart = 0;
1490
        nend   = mdatoms->homenr;
1491
        for (n = nstart; n < nend; n++)
1492
        {
1493
            gt = 0;
1494
            if (mdatoms->cTC)
1495
            {
1496
                gt = mdatoms->cTC[n];
1497
            }
1498
            for (d = 0; d < DIM; d++)
1499
            {
1500
                v[n][d] *= buf_ngtc[gt];
1501
            }
1502
        }
1503

    
1504
        if (IR_NPT_TROTTER(ir) || IR_NPH_TROTTER(ir) || IR_NVT_TROTTER(ir))
1505
        {
1506
            /* we need to recalculate the masses if the temperature has changed */
1507
            init_npt_masses(ir, state, MassQ, FALSE);
1508
            for (i = 0; i < state->nnhpres; i++)
1509
            {
1510
                for (j = 0; j < ir->opts.nhchainlength; j++)
1511
                {
1512
                    state->nhpres_vxi[i+j] *= buf_ngtc[i];
1513
                }
1514
            }
1515
            for (i = 0; i < ir->opts.ngtc; i++)
1516
            {
1517
                for (j = 0; j < ir->opts.nhchainlength; j++)
1518
                {
1519
                    state->nosehoover_vxi[i+j] *= buf_ngtc[i];
1520
                }
1521
            }
1522
        }
1523
        sfree(buf_ngtc);
1524
    }
1525

    
1526
    /* now check on the Wang-Landau updating critera */
1527

    
1528
    if (EWL(expand->elamstats))
1529
    {
1530
        bSwitchtoOneOverT = FALSE;
1531
        if (expand->bWLoneovert)
1532
        {
1533
            totalsamples = 0;
1534
            for (i = 0; i < nlim; i++)
1535
            {
1536
                totalsamples += dfhist->n_at_lam[i];
1537
            }
1538
            oneovert = (1.0*nlim)/totalsamples;
1539
            /* oneovert has decreasd by a bit since last time, so we actually make sure its within one of this number */
1540
            /* switch to 1/t incrementing when wl_delta has decreased at least once, and wl_delta is now less than 1/t */
1541
            if ((dfhist->wl_delta <= ((totalsamples)/(totalsamples-1.00001))*oneovert) &&
1542
                (dfhist->wl_delta < expand->init_wl_delta))
1543
            {
1544
                bSwitchtoOneOverT = TRUE;
1545
            }
1546
        }
1547
        if (bSwitchtoOneOverT)
1548
        {
1549
            dfhist->wl_delta = oneovert; /* now we reduce by this each time, instead of only at flatness */
1550
        }
1551
        else
1552
        {
1553
            bIfReset = CheckHistogramRatios(nlim, dfhist->wl_histo, expand->wl_ratio);
1554
            if (bIfReset)
1555
            {
1556
                for (i = 0; i < nlim; i++)
1557
                {
1558
                    dfhist->wl_histo[i] = 0;
1559
                }
1560
                dfhist->wl_delta *= expand->wl_scale;
1561
                if (log)
1562
                {
1563
                    fprintf(log, "\nStep %d: weights are now:", (int)step);
1564
                    for (i = 0; i < nlim; i++)
1565
                    {
1566
                        fprintf(log, " %.5f", dfhist->sum_weights[i]);
1567
                    }
1568
                    fprintf(log, "\n");
1569
                }
1570
            }
1571
        }
1572
    }
1573
    sfree(pfep_lamee);
1574
    sfree(scaled_lamee);
1575
    sfree(weighted_lamee);
1576
    sfree(p_k);
1577

    
1578
    return lamnew;
1579
}