Line data Source code
1 : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 : Copyright (c) 2017 of Glen Hocky and Andrew White
3 :
4 : The eds module is free software: you can redistribute it and/or modify
5 : it under the terms of the GNU Lesser General Public License as published by
6 : the Free Software Foundation, either version 3 of the License, or
7 : (at your option) any later version.
8 :
9 : The eds module is distributed in the hope that it will be useful,
10 : but WITHOUT ANY WARRANTY; without even the implied warranty of
11 : MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
12 : GNU Lesser General Public License for more details.
13 :
14 : You should have received a copy of the GNU Lesser General Public License
15 : along with plumed. If not, see <http://www.gnu.org/licenses/>.
16 : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
17 : #include "bias/Bias.h"
18 : #include "bias/ReweightBase.h"
19 : #include "core/ActionAtomistic.h"
20 : #include "core/ActionRegister.h"
21 : #include "core/PlumedMain.h"
22 : #include "tools/File.h"
23 : #include "tools/Matrix.h"
24 : #include "tools/Random.h"
25 :
26 : #include <iostream>
27 :
28 : using namespace PLMD;
29 : using namespace bias;
30 :
31 : // namespace is lowercase to match
32 : // module names being all lowercase
33 :
34 : namespace PLMD {
35 : namespace eds {
36 :
37 : //+PLUMEDOC EDSMOD_BIAS EDS
38 : /*
39 : Add a linear bias on a set of observables.
40 :
41 : This force is the same as the linear part of the bias in \ref
42 : RESTRAINT, but this bias has the ability to compute the prefactors
43 : adaptively using the scheme of White and Voth \cite white2014efficient
44 : in order to match target observable values for a set of CVs.
45 : Further updates to the algorithm are described in \cite hocky2017cgds
46 : and you can read a review on the method and its applications here: \cite Amirkulova2019Recent.
47 :
48 : You can
49 : see a tutorial on EDS specifically for biasing coordination number at
50 : <a
51 : href="http://thewhitelab.org/blog/tutorial/2017/05/10/lammps-coordination-number-tutorial/">
52 : Andrew White's webpage</a>.
53 :
54 : The addition to the potential is of the form
55 : \f[
56 : \sum_i \frac{\alpha_i}{s_i} x_i
57 : \f]
58 :
59 : where for CV \f$x_i\f$, a coupling constant \f${\alpha}_i\f$ is determined
60 : adaptively or set by the user to match a target value for
61 : \f$x_i\f$. \f$s_i\f$ is a scale parameter, which by default is set to
62 : the target value. It may also be set separately.
63 :
64 : \warning
65 : It is not possible to set the target value of the observable
66 : to zero with the default value of \f$s_i\f$ as this will cause a
67 : divide-by-zero error. Instead, set \f$s_i=1\f$ or modify the CV so the
68 : desired target value is no longer zero.
69 :
70 : Notice that a similar method is available as \ref MAXENT, although with different features and using a different optimization algorithm.
71 :
72 : \par Virial
73 :
74 : The bias forces modify the virial and this can change your simulation density if the bias is used in an NPT simulation.
75 : One way to avoid changing the virial contribution from the bias is to add the keyword VIRIAL=1.0, which changes how the bias
76 : is computed to minimize its contribution to the virial. This can also lead to smaller magnitude biases that are more robust if
77 : transferred to other systems. VIRIAL=1.0 can be a reasonable starting point and increasing the value changes the balance between matching
78 : the set-points and minimizing the virial. See \cite Amirkulova2019Recent for details on the equations. Since the coupling constants
79 : are unique with a single CV, VIRIAL is not applicable with a single CV. When used with multiple CVs, the CVs should be correlated
80 : which is almost always the case.
81 :
82 : \par Weighting
83 :
84 : EDS computes means and variances as part of its algorithm. If you are
85 : also using a biasing method like metadynamics, you may wish to remove
86 : the effect of this bias in your EDS computations so that EDS works on
87 : the canonical values (reweighted to be unbiased). For example, you may be using
88 : metadynamics to bias a dihedral angle to enhance sampling and be using
89 : EDS to set the average distance between two particular atoms. Specifically:
90 :
91 : \plumedfile
92 : # set-up metadynamics
93 : t: TORSION ATOMS=1,2,3,4
94 : md: METAD ARG=d SIGMA=0.2 HEIGHT=0.3 PACE=500 TEMP=300
95 : # compute bias weights
96 : bias: REWEIGHT_METAD TEMP=300
97 : # now do EDS on distance while removing effect of metadynamics
98 : d: DISTANCE ATOMS=4,7
99 : eds: EDS ARG=d CENTER=3.0 PERIOD=100 TEMP=300 LOGWEIGHTS=bias
100 : \endplumedfile
101 :
102 : This is an approximation though because EDS uses a finite samples while running to get means/variances.
103 : At the end of a run,
104 : you should ensure this approach worked and indeed your reweighted CV matches the target value.
105 :
106 : \par Examples
107 :
108 : The following input for a harmonic oscillator of two beads will
109 : adaptively find a linear bias to change the mean and variance to the
110 : target values. The PRINT line shows how to access the value of the
111 : coupling constants.
112 :
113 : \plumedfile
114 : dist: DISTANCE ATOMS=1,2
115 : # this is the squared of the distance
116 : dist2: COMBINE ARG=dist POWERS=2 PERIODIC=NO
117 :
118 : # bias mean and variance
119 : eds: EDS ARG=dist,dist2 CENTER=2.0,1.0 PERIOD=100 TEMP=1.0
120 : PRINT ARG=dist,dist2,eds.dist_coupling,eds.dist2_coupling,eds.bias,eds.force2 FILE=colvars.dat STRIDE=100
121 : \endplumedfile
122 :
123 : <hr>
124 :
125 : Rather than trying to find the coupling constants adaptively, one can ramp up to a constant value.
126 : \plumedfile
127 : dist: DISTANCE ATOMS=1,2
128 : dist2: COMBINE ARG=dist POWERS=2 PERIODIC=NO
129 :
130 : # ramp couplings from 0,0 to -1,1 over 50000 steps
131 : eds: EDS ARG=dist,dist2 CENTER=2.0,1.0 FIXED=-1,1 RAMP PERIOD=50000 TEMP=1.0
132 :
133 : # same as above, except starting at -0.5,0.5 rather than default of 0,0
134 : eds2: EDS ARG=dist,dist2 CENTER=2.0,1.0 FIXED=-1,1 INIT=-0.5,0.5 RAMP PERIOD=50000 TEMP=1.0
135 : \endplumedfile
136 :
137 : <hr>
138 : A restart file can be added to dump information needed to restart/continue simulation using these parameters every PERIOD.
139 : \plumedfile
140 : dist: DISTANCE ATOMS=1,2
141 : dist2: COMBINE ARG=dist POWERS=2 PERIODIC=NO
142 :
143 : # add the option to write to a restart file
144 : eds: EDS ARG=dist,dist2 CENTER=2.0,1.0 PERIOD=100 TEMP=1.0 OUT_RESTART=checkpoint.eds
145 : \endplumedfile
146 :
147 : The first few lines of the restart file that is output if we run a calculation with one CV will look something like this:
148 :
149 : \auxfile{restart.eds}
150 : #! FIELDS time d1_center d1_set d1_target d1_coupling d1_maxrange d1_maxgrad d1_accum d1_mean d1_std
151 : #! SET adaptive 1
152 : #! SET update_period 1
153 : #! SET seed 0
154 : #! SET kbt 2.4943
155 : 0.0000 1.0000 0.0000 0.0000 0.0000 7.4830 0.1497 0.0000 0.0000 0.0000
156 : 1.0000 1.0000 0.0000 0.0000 0.0000 7.4830 0.1497 0.0000 0.0000 0.0000
157 : 2.0000 1.0000 -7.4830 0.0000 0.0000 7.4830 0.1497 0.0224 0.0000 0.0000
158 : 3.0000 1.0000 -7.4830 0.0000 -7.4830 7.4830 0.1497 0.0224 0.0000 0.0000
159 : 4.0000 1.0000 -7.4830 0.0000 -7.4830 7.4830 0.1497 0.0224 0.0000 0.0000
160 : \endauxfile
161 :
162 : <hr>
163 :
164 : Read in a previous restart file. Adding RESTART flag makes output append
165 : \plumedfile
166 : d1: DISTANCE ATOMS=1,2
167 :
168 : eds: EDS ARG=d1 CENTER=2.0 PERIOD=100 TEMP=1.0 IN_RESTART=restart.eds RESTART=YES
169 : \endplumedfile
170 :
171 : <hr>
172 :
173 : Read in a previous restart file and freeze the bias at the final level from the previous simulation
174 : \plumedfile
175 : d1: DISTANCE ATOMS=1,2
176 :
177 : eds: EDS ARG=d1 CENTER=2.0 TEMP=1.0 IN_RESTART=restart.eds FREEZE
178 : \endplumedfile
179 :
180 : <hr>
181 :
182 : Read in a previous restart file and freeze the bias at the mean from the previous simulation
183 : \plumedfile
184 : d1: DISTANCE ATOMS=1,2
185 :
186 : eds: EDS ARG=d1 CENTER=2.0 TEMP=1.0 IN_RESTART=restart.eds FREEZE MEAN
187 : \endplumedfile
188 :
189 :
190 : */
191 : //+ENDPLUMEDOC
192 :
193 : class EDS : public Bias {
194 :
195 : private:
196 : /*We will get this and store it once, since on-the-fly changing number of CVs will be fatal*/
197 : const unsigned int ncvs_;
198 : std::vector<double> center_;
199 : std::vector<Value *> center_values_;
200 : ReweightBase *logweights_; // weights to use if reweighting averages
201 : std::vector<double> scale_;
202 : std::vector<double> current_coupling_; // actually current coupling
203 : std::vector<double> set_coupling_; // what our coupling is ramping up to. Equal to current_coupling when gathering stats
204 : std::vector<double> target_coupling_; // used when loaded to reach a value
205 : std::vector<double> max_coupling_range_; // used for adaptive range
206 : std::vector<double> max_coupling_grad_; // maximum allowed gradient
207 : std::vector<double> coupling_rate_;
208 : std::vector<double> coupling_accum_;
209 : std::vector<double> means_;
210 : std::vector<double> differences_;
211 : std::vector<double> alpha_vector_;
212 : std::vector<double> alpha_vector_2_;
213 : std::vector<double> ssds_;
214 : std::vector<double> step_size_;
215 : std::vector<double> pseudo_virial_;
216 : std::vector<Value *> out_coupling_;
217 : Matrix<double> covar_;
218 : Matrix<double> covar2_;
219 : Matrix<double> lm_inv_;
220 : std::string in_restart_name_;
221 : std::string out_restart_name_;
222 : std::string fmt_;
223 : OFile out_restart_;
224 : IFile in_restart_;
225 : bool b_c_values_;
226 : bool b_adaptive_;
227 : bool b_freeze_;
228 : bool b_equil_;
229 : bool b_ramp_;
230 : bool b_covar_;
231 : bool b_restart_;
232 : bool b_write_restart_;
233 : bool b_lm_;
234 : bool b_virial_;
235 : bool b_update_virial_;
236 : bool b_weights_;
237 : int seed_;
238 : int update_period_;
239 : int avg_coupling_count_;
240 : int update_calls_;
241 : double kbt_;
242 : double multi_prop_;
243 : double lm_mixing_par_;
244 : double virial_scaling_;
245 : double pseudo_virial_sum_; // net virial for all cvs in current period
246 : double max_logweight_; // maximum observed max logweight for period
247 : double wsum_; // sum of weights thus far
248 : Random rand_;
249 : Value *value_force2_;
250 : Value *value_pressure_;
251 :
252 : /*read input restart. b_mean sets if we use mean or final value for freeze*/
253 : void readInRestart(const bool b_mean);
254 : /*setup output restart*/
255 : void setupOutRestart();
256 : /*write output restart*/
257 : void writeOutRestart();
258 : void update_statistics();
259 : void update_pseudo_virial();
260 : void calc_lm_step_size();
261 : void calc_covar_step_size();
262 : void calc_ssd_step_size();
263 : void reset_statistics();
264 : void update_bias();
265 : void apply_bias();
266 :
267 : public:
268 : explicit EDS(const ActionOptions &);
269 : void calculate();
270 : void update();
271 : void turnOnDerivatives();
272 : static void registerKeywords(Keywords &keys);
273 : ~EDS();
274 : };
275 :
276 : PLUMED_REGISTER_ACTION(EDS, "EDS")
277 :
278 10 : void EDS::registerKeywords(Keywords &keys) {
279 10 : Bias::registerKeywords(keys);
280 10 : keys.use("ARG");
281 20 : keys.add("optional", "CENTER", "The desired centers (equilibrium values) which will be sought during the adaptive linear biasing. This is for fixed centers");
282 20 : keys.add("optional", "CENTER_ARG", "The desired centers (equilibrium values) which will be sought during the adaptive linear biasing. "
283 : "CENTER_ARG is for calculated centers, e.g. from a CV or analysis. ");
284 :
285 20 : keys.add("optional", "PERIOD", "Steps over which to adjust bias for adaptive or ramping");
286 20 : keys.add("compulsory", "RANGE", "25.0", "The (starting) maximum increase in coupling constant per PERIOD (in k_B T/[BIAS_SCALE unit]) for each CV biased");
287 20 : keys.add("compulsory", "SEED", "0", "Seed for random order of changing bias");
288 20 : keys.add("compulsory", "INIT", "0", "Starting value for coupling constant");
289 20 : keys.add("compulsory", "FIXED", "0", "Fixed target values for coupling constant. Non-adaptive.");
290 20 : keys.add("optional", "BIAS_SCALE", "A divisor to set the units of the bias. "
291 : "If not set, this will be the CENTER value by default (as is done in White and Voth 2014).");
292 20 : keys.add("optional", "TEMP", "The system temperature. If not provided will be taken from MD code (if available)");
293 20 : keys.add("optional", "MULTI_PROP", "What proportion of dimensions to update at each step. "
294 : "Must be in interval [1,0), where 1 indicates all and any other indicates a stochastic update. "
295 : "If not set, default is 1 / N, where N is the number of CVs. ");
296 20 : keys.add("optional", "VIRIAL", "Add an update penalty for having non-zero virial contributions. Only makes sense with multiple correlated CVs.");
297 20 : keys.add("optional", "LOGWEIGHTS", "Add weights to use for computing statistics. For example, if biasing with metadynamics.");
298 20 : keys.addFlag("LM", false, "Use Levenberg-Marquadt algorithm along with simultaneous keyword. Otherwise use gradient descent.");
299 20 : keys.add("compulsory", "LM_MIXING", "1", "Initial mixing parameter when using Levenberg-Marquadt minimization.");
300 20 : keys.add("optional", "RESTART_FMT", "the format that should be used to output real numbers in EDS restarts");
301 20 : keys.add("optional", "OUT_RESTART", "Output file for all information needed to continue EDS simulation. "
302 : "If you have the RESTART directive set (global or for EDS), this file will be appended to. "
303 : "Note that the header will be printed again if appending.");
304 20 : keys.add("optional", "IN_RESTART", "Read this file to continue an EDS simulation. "
305 : "If same as OUT_RESTART and you have not set the RESTART directive, the file will be backed-up and overwritten with new output. "
306 : "If you do have the RESTART flag set and it is the same name as OUT_RESTART, this file will be appended.");
307 :
308 20 : keys.addFlag("RAMP", false, "Slowly increase bias constant to a fixed value");
309 20 : keys.addFlag("COVAR", false, "Utilize the covariance matrix when updating the bias. Default Off, but may be enabled due to other options");
310 20 : keys.addFlag("FREEZE", false, "Fix bias at current level (only used for restarting).");
311 20 : keys.addFlag("MEAN", false, "Instead of using final bias level from restart, use average. Can only be used in conjunction with FREEZE");
312 :
313 10 : keys.use("RESTART");
314 :
315 20 : keys.addOutputComponent("force2", "default", "squared value of force from the bias");
316 20 : keys.addOutputComponent("pressure", "default", "If using virial keyword, this is the current sum of virial terms. It is in units of pressure (energy / vol^3)");
317 20 : keys.addOutputComponent("_coupling", "default", "For each named CV biased, there will be a corresponding output CV_coupling storing the current linear bias prefactor.");
318 10 : }
319 :
320 8 : EDS::EDS(const ActionOptions &ao) : PLUMED_BIAS_INIT(ao),
321 8 : ncvs_(getNumberOfArguments()),
322 8 : scale_(ncvs_, 0.0),
323 8 : current_coupling_(ncvs_, 0.0),
324 8 : set_coupling_(ncvs_, 0.0),
325 8 : target_coupling_(ncvs_, 0.0),
326 8 : max_coupling_range_(ncvs_, 25.0),
327 8 : max_coupling_grad_(ncvs_, 0.0),
328 8 : coupling_rate_(ncvs_, 1.0),
329 8 : coupling_accum_(ncvs_, 0.0),
330 8 : means_(ncvs_, 0.0),
331 8 : step_size_(ncvs_, 0.0),
332 8 : pseudo_virial_(ncvs_),
333 8 : out_coupling_(ncvs_, NULL),
334 8 : in_restart_name_(""),
335 8 : out_restart_name_(""),
336 8 : fmt_("%f"),
337 8 : b_adaptive_(true),
338 8 : b_freeze_(false),
339 8 : b_equil_(true),
340 8 : b_ramp_(false),
341 8 : b_covar_(false),
342 8 : b_restart_(false),
343 8 : b_write_restart_(false),
344 8 : b_lm_(false),
345 8 : b_virial_(false),
346 8 : b_weights_(false),
347 8 : seed_(0),
348 8 : update_period_(0),
349 8 : avg_coupling_count_(1),
350 8 : update_calls_(0),
351 8 : kbt_(0.0),
352 8 : multi_prop_(-1.0),
353 8 : lm_mixing_par_(0.1),
354 8 : virial_scaling_(0.),
355 8 : pseudo_virial_sum_(0.0),
356 8 : max_logweight_(0.0),
357 8 : wsum_(0.0),
358 32 : value_force2_(NULL) {
359 : double temp = -1.0;
360 8 : bool b_mean = false;
361 : std::vector<Value *> wvalues;
362 :
363 16 : addComponent("force2");
364 8 : componentIsNotPeriodic("force2");
365 8 : value_force2_ = getPntrToComponent("force2");
366 :
367 20 : for (unsigned int i = 0; i < ncvs_; ++i) {
368 12 : std::string comp = getPntrToArgument(i)->getName() + "_coupling";
369 12 : addComponent(comp);
370 12 : componentIsNotPeriodic(comp);
371 12 : out_coupling_[i] = getPntrToComponent(comp);
372 : }
373 :
374 8 : parseVector("CENTER", center_);
375 8 : parseArgumentList("CENTER_ARG", center_values_);
376 8 : parseArgumentList("LOGWEIGHTS", wvalues);
377 8 : parseVector("BIAS_SCALE", scale_);
378 8 : parseVector("RANGE", max_coupling_range_);
379 8 : parseVector("FIXED", target_coupling_);
380 8 : parseVector("INIT", set_coupling_);
381 8 : parse("PERIOD", update_period_);
382 8 : kbt_ = getkBT();
383 8 : parse("SEED", seed_);
384 8 : parse("MULTI_PROP", multi_prop_);
385 8 : parse("LM_MIXING", lm_mixing_par_);
386 8 : parse("RESTART_FMT", fmt_);
387 8 : parse("VIRIAL", virial_scaling_);
388 8 : fmt_ = " " + fmt_; // add space since parse strips them
389 8 : parse("OUT_RESTART", out_restart_name_);
390 8 : parseFlag("LM", b_lm_);
391 8 : parseFlag("RAMP", b_ramp_);
392 8 : parseFlag("FREEZE", b_freeze_);
393 8 : parseFlag("MEAN", b_mean);
394 8 : parseFlag("COVAR", b_covar_);
395 8 : parse("IN_RESTART", in_restart_name_);
396 8 : checkRead();
397 :
398 : /*
399 : * Things that are different when using changing centers:
400 : * 1. Scale
401 : * 2. The log file
402 : * 3. Reading Restarts
403 : */
404 :
405 8 : if (center_.size() == 0) {
406 1 : if (center_values_.size() == 0) {
407 0 : error("Must set either CENTER or CENTER_ARG");
408 1 : } else if (center_values_.size() != ncvs_) {
409 0 : error("CENTER_ARG must contain the same number of variables as ARG");
410 : }
411 1 : b_c_values_ = true;
412 1 : center_.resize(ncvs_);
413 1 : log.printf(" EDS will use possibly varying centers\n");
414 : } else {
415 7 : if (center_.size() != ncvs_) {
416 0 : error("Must have same number of CENTER arguments as ARG arguments");
417 7 : } else if (center_values_.size() != 0) {
418 0 : error("You can only set CENTER or CENTER_ARG. Not both");
419 : }
420 7 : b_c_values_ = false;
421 7 : log.printf(" EDS will use fixed centers\n");
422 : }
423 :
424 : // check for weights
425 8 : if (wvalues.size() > 1) {
426 0 : error("LOGWEIGHTS can only support one weight set. Please only pass one action");
427 8 : } else if (wvalues.size() == 1) {
428 1 : logweights_ = dynamic_cast<ReweightBase *>(wvalues[0]->getPntrToAction());
429 1 : b_weights_ = true;
430 : }
431 :
432 8 : log.printf(" setting scaling:");
433 8 : if (scale_.size() > 0 && scale_.size() < ncvs_) {
434 0 : error("the number of BIAS_SCALE values be the same as number of CVs");
435 8 : } else if (scale_.size() == 0 && b_c_values_) {
436 0 : log.printf(" Setting SCALE to be 1 for all CVs\n");
437 0 : scale_.resize(ncvs_);
438 0 : for (unsigned int i = 0; i < ncvs_; ++i) {
439 0 : scale_[i] = 1;
440 : }
441 8 : } else if (scale_.size() == 0 && !b_c_values_) {
442 2 : log.printf(" (default) ");
443 :
444 2 : scale_.resize(ncvs_);
445 6 : for (unsigned int i = 0; i < scale_.size(); ++i) {
446 4 : if (center_[i] == 0) {
447 0 : error("BIAS_SCALE parameter has been set to CENTER value of 0 (as is default). This will divide by 0, so giving up. See doc for EDS bias");
448 : }
449 4 : scale_[i] = center_[i];
450 : }
451 : } else {
452 14 : for (unsigned int i = 0; i < scale_.size(); ++i) {
453 8 : log.printf(" %f", scale_[i]);
454 : }
455 : }
456 8 : log.printf("\n");
457 :
458 8 : if (b_lm_) {
459 1 : log.printf(" EDS will perform Levenberg-Marquardt minimization with mixing parameter = %f\n", lm_mixing_par_);
460 1 : differences_.resize(ncvs_);
461 1 : alpha_vector_.resize(ncvs_);
462 1 : alpha_vector_2_.resize(ncvs_);
463 1 : covar_.resize(ncvs_, ncvs_);
464 1 : covar2_.resize(ncvs_, ncvs_);
465 1 : lm_inv_.resize(ncvs_, ncvs_);
466 1 : covar2_ *= 0;
467 1 : lm_inv_ *= 0;
468 1 : if (multi_prop_ != 1) {
469 0 : log.printf(" WARNING - doing LM minimization but MULTI_PROP!=1\n");
470 : }
471 7 : } else if (b_covar_) {
472 1 : log.printf(" EDS will utilize covariance matrix for update steps\n");
473 1 : covar_.resize(ncvs_, ncvs_);
474 : } else {
475 6 : log.printf(" EDS will utilize variance for update steps\n");
476 6 : ssds_.resize(ncvs_);
477 : }
478 :
479 8 : b_virial_ = virial_scaling_;
480 :
481 8 : if (b_virial_) {
482 1 : if (ncvs_ == 1) {
483 0 : error("Minimizing the virial is only valid with multiply correlated collective variables.");
484 : }
485 : // check that the CVs can be used to compute pseudo-virial
486 1 : log.printf(" EDS will compute virials of CVs and penalize with scale of %f. Checking CVs are valid...", virial_scaling_);
487 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
488 3 : auto a = dynamic_cast<ActionAtomistic *>(getPntrToArgument(i)->getPntrToAction());
489 3 : if (!a) {
490 0 : error("If using VIRIAL keyword, you must have normal CVs as arguments to EDS. Offending action: " + getPntrToArgument(i)->getPntrToAction()->getName());
491 : }
492 : // cppcheck-suppress nullPointerRedundantCheck
493 3 : if (!(a->getPbc().isOrthorombic())) {
494 3 : log.printf(" WARNING: EDS Virial should have a orthorombic cell\n");
495 : }
496 : }
497 1 : log.printf("done.\n");
498 2 : addComponent("pressure");
499 1 : componentIsNotPeriodic("pressure");
500 1 : value_pressure_ = getPntrToComponent("pressure");
501 : }
502 :
503 8 : if (b_mean && !b_freeze_) {
504 0 : error("EDS keyword MEAN can only be used along with keyword FREEZE");
505 : }
506 :
507 8 : if (in_restart_name_ != "") {
508 2 : b_restart_ = true;
509 2 : log.printf(" reading simulation information from file: %s\n", in_restart_name_.c_str());
510 2 : readInRestart(b_mean);
511 : } else {
512 :
513 : // in driver, this results in kbt of 0
514 6 : if (kbt_ == 0) {
515 0 : error(" Unable to determine valid kBT. "
516 : "Could be because you are runnning from driver or MD didn't give temperature.\n"
517 : "Consider setting temperature manually with the TEMP keyword.");
518 : kbt_ = 1;
519 : }
520 :
521 6 : log.printf(" kBT = %f\n", kbt_);
522 6 : log.printf(" Updating every %i steps\n", update_period_);
523 :
524 6 : if (!b_c_values_) {
525 5 : log.printf(" with centers:");
526 14 : for (unsigned int i = 0; i < ncvs_; ++i) {
527 9 : log.printf(" %f ", center_[i]);
528 : }
529 : } else {
530 1 : log.printf(" with actions centers:");
531 2 : for (unsigned int i = 0; i < ncvs_; ++i) {
532 1 : log.printf(" %s ", center_values_[i]->getName().c_str());
533 : // add dependency on these actions
534 1 : addDependency(center_values_[i]->getPntrToAction());
535 : }
536 : }
537 :
538 6 : log.printf("\n with initial ranges / rates:\n");
539 16 : for (unsigned int i = 0; i < max_coupling_range_.size(); ++i) {
540 : // this is just an empirical guess. Bigger range, bigger grads. Less frequent updates, bigger changes
541 : //
542 : // using the current maxing out scheme, max_coupling_range is the biggest step that can be taken in any given interval
543 10 : max_coupling_range_[i] *= kbt_;
544 10 : max_coupling_grad_[i] = max_coupling_range_[i];
545 10 : log.printf(" %f / %f\n", max_coupling_range_[i], max_coupling_grad_[i]);
546 : }
547 :
548 6 : if (seed_ > 0) {
549 2 : log.printf(" setting random seed = %i", seed_);
550 2 : rand_.setSeed(seed_);
551 : }
552 :
553 16 : for (unsigned int i = 0; i < ncvs_; ++i)
554 10 : if (target_coupling_[i] != 0.0) {
555 1 : b_adaptive_ = false;
556 : }
557 :
558 6 : if (!b_adaptive_) {
559 1 : if (b_ramp_) {
560 1 : log.printf(" ramping up coupling constants over %i steps\n", update_period_);
561 : }
562 :
563 1 : log.printf(" with starting coupling constants");
564 2 : for (unsigned int i = 0; i < set_coupling_.size(); ++i) {
565 1 : log.printf(" %f", set_coupling_[i]);
566 : }
567 1 : log.printf("\n");
568 1 : log.printf(" and final coupling constants");
569 2 : for (unsigned int i = 0; i < target_coupling_.size(); ++i) {
570 1 : log.printf(" %f", target_coupling_[i]);
571 : }
572 1 : log.printf("\n");
573 : }
574 :
575 : // now do setup
576 6 : if (b_ramp_) {
577 1 : update_period_ *= -1;
578 : }
579 :
580 16 : for (unsigned int i = 0; i < set_coupling_.size(); ++i) {
581 10 : current_coupling_[i] = set_coupling_[i];
582 : }
583 :
584 : // if b_adaptive_, then first half will be used for equilibrating and second half for statistics
585 6 : if (update_period_ > 0) {
586 5 : update_period_ /= 2;
587 : }
588 : }
589 :
590 8 : if (b_freeze_) {
591 1 : b_adaptive_ = false;
592 1 : update_period_ = 0;
593 1 : if (b_mean) {
594 1 : log.printf(" freezing bias at the average level from the restart file\n");
595 : } else {
596 0 : log.printf(" freezing bias at current level\n");
597 : }
598 : }
599 :
600 8 : if (multi_prop_ == -1.0) {
601 5 : log.printf(" Will update each dimension stochastically with probability 1 / number of CVs\n");
602 5 : multi_prop_ = 1.0 / ncvs_;
603 3 : } else if (multi_prop_ > 0 && multi_prop_ <= 1.0) {
604 3 : log.printf(" Will update each dimension stochastically with probability %f\n", multi_prop_);
605 : } else {
606 0 : error(" MULTI_PROP must be between 0 and 1\n");
607 : }
608 :
609 8 : if (out_restart_name_.length() > 0) {
610 8 : log.printf(" writing restart information every %i steps to file %s with format %s\n", abs(update_period_), out_restart_name_.c_str(), fmt_.c_str());
611 8 : b_write_restart_ = true;
612 8 : setupOutRestart();
613 : }
614 :
615 16 : log << " Bibliography " << plumed.cite("White and Voth, J. Chem. Theory Comput. 10 (8), 3023-3030 (2014)") << "\n";
616 16 : log << " Bibliography " << plumed.cite("G. M. Hocky, T. Dannenhoffer-Lafage, G. A. Voth, J. Chem. Theory Comput. 13 (9), 4593-4603 (2017)") << "\n";
617 8 : }
618 :
619 2 : void EDS::readInRestart(const bool b_mean) {
620 2 : int adaptive_i = 0;
621 :
622 2 : in_restart_.open(in_restart_name_);
623 :
624 4 : if (in_restart_.FieldExist("kbt")) {
625 2 : in_restart_.scanField("kbt", kbt_);
626 : } else {
627 0 : error("No field 'kbt' in restart file");
628 : }
629 2 : log.printf(" with kBT = %f\n", kbt_);
630 :
631 4 : if (in_restart_.FieldExist("update_period")) {
632 2 : in_restart_.scanField("update_period", update_period_);
633 : } else {
634 0 : error("No field 'update_period' in restart file");
635 : }
636 2 : log.printf(" Updating every %i steps\n", update_period_);
637 :
638 4 : if (in_restart_.FieldExist("adaptive")) {
639 : // note, no version of scanField for boolean
640 2 : in_restart_.scanField("adaptive", adaptive_i);
641 : } else {
642 0 : error("No field 'adaptive' in restart file");
643 : }
644 2 : b_adaptive_ = bool(adaptive_i);
645 :
646 4 : if (in_restart_.FieldExist("seed")) {
647 2 : in_restart_.scanField("seed", seed_);
648 : } else {
649 0 : error("No field 'seed' in restart file");
650 : }
651 2 : if (seed_ > 0) {
652 0 : log.printf(" setting random seed = %i", seed_);
653 0 : rand_.setSeed(seed_);
654 : }
655 :
656 : double time, tmp;
657 2 : std::vector<double> avg_bias = std::vector<double>(center_.size());
658 : unsigned int N = 0;
659 : std::string cv_name;
660 :
661 24 : while (in_restart_.scanField("time", time)) {
662 :
663 20 : for (unsigned int i = 0; i < ncvs_; ++i) {
664 : cv_name = getPntrToArgument(i)->getName();
665 20 : in_restart_.scanField(cv_name + "_center", set_coupling_[i]);
666 20 : in_restart_.scanField(cv_name + "_set", set_coupling_[i]);
667 20 : in_restart_.scanField(cv_name + "_target", target_coupling_[i]);
668 20 : in_restart_.scanField(cv_name + "_coupling", current_coupling_[i]);
669 20 : in_restart_.scanField(cv_name + "_maxrange", max_coupling_range_[i]);
670 20 : in_restart_.scanField(cv_name + "_maxgrad", max_coupling_grad_[i]);
671 20 : in_restart_.scanField(cv_name + "_accum", coupling_accum_[i]);
672 10 : in_restart_.scanField(cv_name + "_mean", means_[i]);
673 20 : if (in_restart_.FieldExist(cv_name + "_pseudovirial")) {
674 0 : if (b_virial_) {
675 0 : in_restart_.scanField(cv_name + "_pseudovirial", pseudo_virial_[i]);
676 : } else { // discard the field
677 0 : in_restart_.scanField(cv_name + "_pseudovirial", tmp);
678 : }
679 : }
680 : // unused due to difference between covar/nocovar
681 20 : in_restart_.scanField(cv_name + "_std", tmp);
682 :
683 10 : avg_bias[i] += current_coupling_[i];
684 : }
685 10 : N++;
686 :
687 10 : in_restart_.scanField();
688 : }
689 :
690 2 : log.printf(" with centers:");
691 4 : for (unsigned int i = 0; i < center_.size(); ++i) {
692 2 : log.printf(" %f", center_[i]);
693 : }
694 2 : log.printf("\n and scaling:");
695 4 : for (unsigned int i = 0; i < scale_.size(); ++i) {
696 2 : log.printf(" %f", scale_[i]);
697 : }
698 :
699 2 : log.printf("\n with initial ranges / rates:\n");
700 4 : for (unsigned int i = 0; i < max_coupling_range_.size(); ++i) {
701 2 : log.printf(" %f / %f\n", max_coupling_range_[i], max_coupling_grad_[i]);
702 : }
703 :
704 2 : if (!b_adaptive_ && update_period_ < 0) {
705 0 : log.printf(" ramping up coupling constants over %i steps\n", -update_period_);
706 : }
707 :
708 2 : if (b_mean) {
709 1 : log.printf("Loaded in averages for coupling constants...\n");
710 2 : for (unsigned int i = 0; i < current_coupling_.size(); ++i) {
711 1 : current_coupling_[i] = avg_bias[i] / N;
712 : }
713 2 : for (unsigned int i = 0; i < current_coupling_.size(); ++i) {
714 1 : set_coupling_[i] = avg_bias[i] / N;
715 : }
716 : }
717 :
718 2 : log.printf(" with current coupling constants:\n ");
719 4 : for (unsigned int i = 0; i < current_coupling_.size(); ++i) {
720 2 : log.printf(" %f", current_coupling_[i]);
721 : }
722 2 : log.printf("\n");
723 2 : log.printf(" with initial coupling constants:\n ");
724 4 : for (unsigned int i = 0; i < set_coupling_.size(); ++i) {
725 2 : log.printf(" %f", set_coupling_[i]);
726 : }
727 2 : log.printf("\n");
728 2 : log.printf(" and final coupling constants:\n ");
729 4 : for (unsigned int i = 0; i < target_coupling_.size(); ++i) {
730 2 : log.printf(" %f", target_coupling_[i]);
731 : }
732 2 : log.printf("\n");
733 :
734 2 : in_restart_.close();
735 2 : }
736 :
737 8 : void EDS::setupOutRestart() {
738 8 : out_restart_.link(*this);
739 8 : out_restart_.fmtField(fmt_);
740 8 : out_restart_.open(out_restart_name_);
741 : out_restart_.setHeavyFlush();
742 :
743 16 : out_restart_.addConstantField("adaptive").printField("adaptive", b_adaptive_);
744 16 : out_restart_.addConstantField("update_period").printField("update_period", update_period_);
745 16 : out_restart_.addConstantField("seed").printField("seed", seed_);
746 16 : out_restart_.addConstantField("kbt").printField("kbt", kbt_);
747 8 : }
748 :
749 27 : void EDS::writeOutRestart() {
750 : std::string cv_name;
751 27 : out_restart_.printField("time", getTimeStep() * getStep());
752 :
753 66 : for (unsigned int i = 0; i < ncvs_; ++i) {
754 : cv_name = getPntrToArgument(i)->getName();
755 78 : out_restart_.printField(cv_name + "_center", center_[i]);
756 78 : out_restart_.printField(cv_name + "_set", set_coupling_[i]);
757 78 : out_restart_.printField(cv_name + "_target", target_coupling_[i]);
758 78 : out_restart_.printField(cv_name + "_coupling", current_coupling_[i]);
759 78 : out_restart_.printField(cv_name + "_maxrange", max_coupling_range_[i]);
760 78 : out_restart_.printField(cv_name + "_maxgrad", max_coupling_grad_[i]);
761 78 : out_restart_.printField(cv_name + "_accum", coupling_accum_[i]);
762 39 : out_restart_.printField(cv_name + "_mean", means_[i]);
763 39 : if (b_virial_) {
764 18 : out_restart_.printField(cv_name + "_pseudovirial", pseudo_virial_[i]);
765 : }
766 39 : if (!b_covar_ && !b_lm_) {
767 42 : out_restart_.printField(cv_name + "_std", ssds_[i] / (fmax(1, update_calls_ - 1)));
768 : } else {
769 36 : out_restart_.printField(cv_name + "_std", covar_(i, i) / (fmax(1, update_calls_ - 1)));
770 : }
771 : }
772 27 : out_restart_.printField();
773 27 : }
774 :
775 40 : void EDS::calculate() {
776 :
777 : // get center values from action if necessary
778 40 : if (b_c_values_)
779 10 : for (unsigned int i = 0; i < ncvs_; ++i) {
780 5 : center_[i] = center_values_[i]->get();
781 : }
782 :
783 40 : apply_bias();
784 40 : }
785 :
786 40 : void EDS::apply_bias() {
787 : // Compute linear force as in "restraint"
788 : double ene = 0, totf2 = 0, cv, m, f;
789 :
790 100 : for (unsigned int i = 0; i < ncvs_; ++i) {
791 60 : cv = difference(i, center_[i], getArgument(i));
792 60 : m = current_coupling_[i];
793 60 : f = -m;
794 60 : ene += m * cv;
795 60 : setOutputForce(i, f);
796 60 : totf2 += f * f;
797 : }
798 :
799 40 : setBias(ene);
800 40 : value_force2_->set(totf2);
801 40 : }
802 :
803 12 : void EDS::update_statistics() {
804 : double s, N, w = 1.0;
805 12 : std::vector<double> deltas(ncvs_);
806 :
807 : // update weight max, if necessary
808 12 : if (b_weights_) {
809 2 : w = logweights_->getLogWeight();
810 2 : if (max_logweight_ < w) {
811 : // we have new max. Need to shift existing values
812 0 : wsum_ *= exp(max_logweight_ - w);
813 0 : max_logweight_ = w;
814 : }
815 : // convert to weight
816 2 : w = exp(w - max_logweight_);
817 2 : wsum_ += w;
818 : N = wsum_;
819 : } else {
820 10 : N = fmax(1, update_calls_);
821 : }
822 :
823 : // Welford, West, and Hanso online variance method
824 : // with weights (default = 1.0)
825 32 : for (unsigned int i = 0; i < ncvs_; ++i) {
826 20 : deltas[i] = difference(i, means_[i], getArgument(i)) * w;
827 20 : means_[i] += deltas[i] / N;
828 20 : if (!b_covar_ && !b_lm_) {
829 8 : ssds_[i] += deltas[i] * difference(i, means_[i], getArgument(i));
830 : }
831 : }
832 12 : if (b_covar_ || b_lm_) {
833 16 : for (unsigned int i = 0; i < ncvs_; ++i) {
834 36 : for (unsigned int j = i; j < ncvs_; ++j) {
835 24 : s = (N - 1) * deltas[i] * deltas[j] / N / N - covar_(i, j) / N;
836 24 : covar_(i, j) += s;
837 : // do this so we don't double count
838 24 : covar_(j, i) = covar_(i, j);
839 : }
840 : }
841 : }
842 12 : if (b_virial_) {
843 2 : update_pseudo_virial();
844 : }
845 12 : }
846 :
847 8 : void EDS::reset_statistics() {
848 20 : for (unsigned int i = 0; i < ncvs_; ++i) {
849 12 : means_[i] = 0;
850 12 : if (!b_covar_ && !b_lm_) {
851 6 : ssds_[i] = 0;
852 : }
853 : }
854 8 : if (b_covar_ || b_lm_)
855 8 : for (unsigned int i = 0; i < ncvs_; ++i)
856 24 : for (unsigned int j = 0; j < ncvs_; ++j) {
857 18 : covar_(i, j) = 0;
858 : }
859 8 : if (b_virial_) {
860 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
861 3 : pseudo_virial_[i] = 0;
862 : }
863 1 : pseudo_virial_sum_ = 0;
864 : }
865 8 : if (b_weights_) {
866 2 : wsum_ = 0;
867 2 : max_logweight_ = 0;
868 : }
869 8 : }
870 :
871 1 : void EDS::calc_lm_step_size() {
872 : // calulcate step size
873 : // uses scale here, which by default is center
874 :
875 1 : mult(covar_, covar_, covar2_);
876 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
877 3 : differences_[i] = difference(i, center_[i], means_[i]);
878 3 : covar2_[i][i] += lm_mixing_par_ * covar2_[i][i];
879 : }
880 :
881 : // "step_size_vec" = 2*inv(covar*covar+ lambda diag(covar*covar))*covar*(mean-center)
882 1 : mult(covar_, differences_, alpha_vector_);
883 1 : Invert(covar2_, lm_inv_);
884 1 : mult(lm_inv_, alpha_vector_, alpha_vector_2_);
885 :
886 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
887 3 : step_size_[i] = 2 * alpha_vector_2_[i] / kbt_ / scale_[i];
888 : }
889 1 : }
890 :
891 1 : void EDS::calc_covar_step_size() {
892 : // calulcate step size
893 : // uses scale here, which by default is center
894 : double tmp;
895 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
896 : tmp = 0;
897 12 : for (unsigned int j = 0; j < ncvs_; ++j) {
898 9 : tmp += difference(i, center_[i], means_[i]) * covar_(i, j);
899 : }
900 3 : step_size_[i] = 2 * tmp / kbt_ / scale_[i] * update_calls_ / fmax(1, update_calls_ - 1);
901 : }
902 1 : }
903 :
904 6 : void EDS::calc_ssd_step_size() {
905 : double tmp;
906 12 : for (unsigned int i = 0; i < ncvs_; ++i) {
907 6 : tmp = 2. * difference(i, center_[i], means_[i]) * ssds_[i] / fmax(1, update_calls_ - 1);
908 6 : step_size_[i] = tmp / kbt_ / scale_[i];
909 : }
910 6 : }
911 :
912 2 : void EDS::update_pseudo_virial() {
913 : // We want to compute the bias force on each atom times the position
914 : // of the atoms.
915 : double p, netp = 0, netpv = 0;
916 : double volume = 0;
917 8 : for (unsigned int i = 0; i < ncvs_; ++i) {
918 : // checked in setup to ensure this cast is valid.
919 6 : ActionAtomistic *cv = dynamic_cast<ActionAtomistic *>(getPntrToArgument(i)->getPntrToAction());
920 6 : Tensor v(cv->getVirial());
921 6 : Tensor box(cv->getBox());
922 : const unsigned int natoms = cv->getNumberOfAtoms();
923 6 : if (!volume) {
924 2 : volume = box.determinant();
925 : }
926 :
927 : // pressure contribution is -dBias / dV
928 : // dBias / dV = alpha / w * dCV / dV
929 : // to get partial of CV wrt to volume
930 : // dCV/dV = sum dCV/dvij * vij / V
931 : // where vij is box element
932 : // add diagonal of virial tensor to get net pressure
933 : // TODO: replace this with adjugate (Jacobi's Formula) for non-orthorombic case(?)
934 6 : p = v(0, 0) * box(0, 0) + v(1, 1) * box(1, 1) + v(2, 2) * box(2, 2);
935 6 : p /= volume;
936 :
937 6 : netp += p;
938 :
939 : // now scale for correct units in EDS algorithm
940 6 : p *= (volume) / (kbt_ * natoms);
941 :
942 : // compute running mean of scaled
943 6 : if (set_coupling_[i] != 0) {
944 0 : pseudo_virial_[i] = (p - pseudo_virial_[i]) / (fmax(1, update_calls_));
945 : } else {
946 6 : pseudo_virial_[i] = 0;
947 : }
948 : // update net pressure
949 6 : netpv += pseudo_virial_[i];
950 : }
951 : // update pressure
952 2 : value_pressure_->set(netp);
953 2 : pseudo_virial_sum_ = netpv;
954 2 : }
955 :
956 8 : void EDS::update_bias() {
957 8 : log.flush();
958 8 : if (b_lm_) {
959 1 : calc_lm_step_size();
960 7 : } else if (b_covar_) {
961 1 : calc_covar_step_size();
962 : } else {
963 6 : calc_ssd_step_size();
964 : }
965 :
966 20 : for (unsigned int i = 0; i < ncvs_; ++i) {
967 :
968 : // multidimesional stochastic step
969 12 : if (ncvs_ == 1 || (rand_.RandU01() < (multi_prop_))) {
970 :
971 12 : if (b_virial_) {
972 : // apply virial regularization
973 : // P * dP/dcoupling
974 : // coupling is already included in virial term due to plumed propogating from bias to forces
975 : // thus we need to divide by it to get the derivative (since force is linear in coupling)
976 3 : if (fabs(set_coupling_[i]) > 0.000000001) // my heuristic for if EDS has started to prevent / 0
977 : // scale^2 here is to align units
978 : {
979 0 : step_size_[i] -= 2 * scale_[i] * scale_[i] * virial_scaling_ * pseudo_virial_sum_ * pseudo_virial_sum_ / set_coupling_[i];
980 : }
981 : }
982 12 : if (step_size_[i] == 0) {
983 4 : continue;
984 : }
985 :
986 : // clip gradient
987 8 : step_size_[i] = copysign(fmin(fabs(step_size_[i]), max_coupling_grad_[i]), step_size_[i]);
988 8 : coupling_accum_[i] += step_size_[i] * step_size_[i];
989 :
990 : // equation 5 in White and Voth, JCTC 2014
991 : // no negative sign because it's in step_size
992 8 : set_coupling_[i] += step_size_[i] * max_coupling_range_[i] / sqrt(coupling_accum_[i]);
993 8 : coupling_rate_[i] = (set_coupling_[i] - current_coupling_[i]) / update_period_;
994 : } else {
995 : // do not change the bias
996 0 : coupling_rate_[i] = 0;
997 : }
998 : }
999 :
1000 : // reset means/vars
1001 8 : reset_statistics();
1002 8 : }
1003 :
1004 40 : void EDS::update() {
1005 : // adjust parameters according to EDS recipe
1006 40 : update_calls_++;
1007 :
1008 : // if we aren't wating for the bias to equilibrate, set flag to collect data
1009 : // want statistics before writing restart
1010 40 : if (!b_equil_ && update_period_ > 0) {
1011 12 : update_statistics();
1012 : }
1013 :
1014 : // write restart with correct statistics before bias update
1015 : // check if we're ramping or doing normal updates and then restart if needed. The ramping check
1016 : // is complicated because we could be frozen, finished ramping or not ramping.
1017 : // The + 2 is so we have an extra line showing that the bias isn't changing (for my sanity and yours)
1018 40 : if (b_write_restart_) {
1019 40 : if (getStep() == 0 ||
1020 32 : ((update_period_ < 0 && !b_freeze_ && update_calls_ <= fabs(update_period_) + 2) ||
1021 24 : (update_period_ > 0 && update_calls_ % update_period_ == 0))) {
1022 27 : writeOutRestart();
1023 : }
1024 : }
1025 :
1026 : int b_finished_equil_flag = 1;
1027 :
1028 : // assume forces already applied and saved
1029 : // are we ramping to a constant value and not done equilibrating?
1030 40 : if (update_period_ < 0) {
1031 5 : if (update_calls_ <= fabs(update_period_) && !b_freeze_) {
1032 4 : for (unsigned int i = 0; i < ncvs_; ++i) {
1033 2 : current_coupling_[i] += (target_coupling_[i] - set_coupling_[i]) / fabs(update_period_);
1034 : }
1035 : }
1036 : // make sure we don't reset update calls
1037 : b_finished_equil_flag = 0;
1038 35 : } else if (update_period_ == 0) {
1039 : // do we have a no-update case?
1040 : // not updating
1041 : // pass
1042 30 : } else if (b_equil_) {
1043 : // equilibrating
1044 : // check if we've reached the setpoint
1045 48 : for (unsigned int i = 0; i < ncvs_; ++i) {
1046 60 : if (coupling_rate_[i] == 0 || pow(current_coupling_[i] - set_coupling_[i], 2) < pow(coupling_rate_[i], 2)) {
1047 14 : b_finished_equil_flag &= 1;
1048 : } else {
1049 16 : current_coupling_[i] += coupling_rate_[i];
1050 : b_finished_equil_flag = 0;
1051 : }
1052 : }
1053 : }
1054 :
1055 : // reduce all the flags
1056 40 : if (b_equil_ && b_finished_equil_flag) {
1057 11 : b_equil_ = false;
1058 11 : update_calls_ = 0;
1059 : }
1060 :
1061 : // Now we update coupling constant, if necessary
1062 40 : if (!b_equil_ && update_period_ > 0 && update_calls_ == update_period_ && !b_freeze_) {
1063 8 : update_bias();
1064 8 : update_calls_ = 0;
1065 8 : avg_coupling_count_++;
1066 8 : b_equil_ = true; // back to equilibration now
1067 : } // close update if
1068 :
1069 : // pass couplings out so they are accessible
1070 100 : for (unsigned int i = 0; i < ncvs_; ++i) {
1071 60 : out_coupling_[i]->set(current_coupling_[i]);
1072 : }
1073 40 : }
1074 :
1075 16 : EDS::~EDS() {
1076 8 : out_restart_.close();
1077 24 : }
1078 :
1079 0 : void EDS::turnOnDerivatives() {
1080 : // do nothing
1081 : // this is to avoid errors triggered when a bias is used as a CV
1082 : // (This is done in ExtendedLagrangian.cpp)
1083 0 : }
1084 :
1085 : }
1086 : } // close the 2 namespaces
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