/* Add the convolved PMF weights for the neighbors of this point.
Note that this function only adds point within the target > 0 region.
Sum weights, take the logarithm last to get the free energy. */
- double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
- biasNeighbor, point.coordValue, {}, m);
+ double logWeight = biasedLogWeightFromPoint(
+ dimParams, points_, grid, neighbor, biasNeighbor, point.coordValue, {}, m);
freeEnergyWeights += std::exp(logWeight);
}
}
"If you are not certain about your settings you might want to increase your "
"pull force constant or "
"modify your sampling region.\n",
- biasIndex + 1, t, pointValueString.c_str(), maxHistogramRatio);
+ biasIndex + 1,
+ t,
+ pointValueString.c_str(),
+ maxHistogramRatio);
gmx::TextLineWrapper wrapper;
wrapper.settings().setLineLength(c_linewidth);
fprintf(fplog, "%s", wrapper.wrapToString(warningMessage).c_str());
bool onlySampleUmbrellaGridpoint)
{
/* Generate and set a new coordinate reference value */
- coordState_.sampleUmbrellaGridpoint(grid, coordState_.gridpointIndex(), probWeightNeighbor,
- step, seed, indexSeed);
+ coordState_.sampleUmbrellaGridpoint(
+ grid, coordState_.gridpointIndex(), probWeightNeighbor, step, seed, indexSeed);
if (onlySampleUmbrellaGridpoint)
{
/* In between global updates the reference histogram size is kept constant so we trivially
know what the histogram size was at the time of the skipped update. */
double histogramSize = histogramSize_.histogramSize();
- setHistogramUpdateScaleFactors(params, histogramSize, histogramSize, weightHistScaling,
- logPmfSumScaling);
+ setHistogramUpdateScaleFactors(
+ params, histogramSize, histogramSize, weightHistScaling, logPmfSumScaling);
}
else
{
/* Label each point along each dimension as covered or not. */
for (int d = 0; d < grid.numDimensions(); d++)
{
- labelCoveredPoints(checkDim[d].visited, checkDim[d].checkCovering, grid.axis(d).numPoints(),
- grid.axis(d).numPointsInPeriod(), params.coverRadius()[d], checkDim[d].covered);
+ labelCoveredPoints(checkDim[d].visited,
+ checkDim[d].checkCovering,
+ grid.axis(d).numPoints(),
+ grid.axis(d).numPointsInPeriod(),
+ params.coverRadius()[d],
+ checkDim[d].covered);
}
/* Now check for global covering. Each dimension needs to be covered separately.
{
sumOverSimulations(
gmx::arrayRefFromArray(checkDim[d].covered.data(), grid.axis(d).numPoints()),
- commRecord, multiSimComm);
+ commRecord,
+ multiSimComm);
}
}
}
/* The weighthistogram size after this update. */
- double newHistogramSize = histogramSize_.newHistogramSize(params, t, detectedCovering, points_,
- weightSumCovering_, fplog);
+ double newHistogramSize = histogramSize_.newHistogramSize(
+ params, t, detectedCovering, points_, weightSumCovering_, fplog);
/* Make the update list. Usually we try to only update local points,
* but if the update has non-trivial or non-deterministic effects
}
double weightHistScalingNew;
double logPmfsumScalingNew;
- setHistogramUpdateScaleFactors(params, newHistogramSize, histogramSize_.histogramSize(),
- &weightHistScalingNew, &logPmfsumScalingNew);
+ setHistogramUpdateScaleFactors(
+ params, newHistogramSize, histogramSize_.histogramSize(), &weightHistScalingNew, &logPmfsumScalingNew);
/* Update free energy and reference weight histogram for points in the update list. */
for (int pointIndex : *updateList)
/* Do updates from previous update steps that were skipped because this point was at that time non-local. */
if (params.skipUpdates())
{
- pointStateToUpdate->performPreviouslySkippedUpdates(params, histogramSize_.numUpdates(),
- weightHistScalingSkipped,
- logPmfsumScalingSkipped);
+ pointStateToUpdate->performPreviouslySkippedUpdates(
+ params, histogramSize_.numUpdates(), weightHistScalingSkipped, logPmfsumScalingSkipped);
}
/* Now do an update with new sampling data. */
- pointStateToUpdate->updateWithNewSampling(params, histogramSize_.numUpdates(),
- weightHistScalingNew, logPmfsumScalingNew);
+ pointStateToUpdate->updateWithNewSampling(
+ params, histogramSize_.numUpdates(), weightHistScalingNew, logPmfsumScalingNew);
}
/* Only update the histogram size after we are done with the local point updates */
if (n < neighbors.size())
{
const int neighbor = neighbors[n];
- (*weight)[n] = biasedLogWeightFromPoint(
- dimParams, points_, grid, neighbor, points_[neighbor].bias(),
- coordState_.coordValue(), neighborLambdaEnergies, coordState_.gridpointIndex());
+ (*weight)[n] = biasedLogWeightFromPoint(dimParams,
+ points_,
+ grid,
+ neighbor,
+ points_[neighbor].bias(),
+ coordState_.coordValue(),
+ neighborLambdaEnergies,
+ coordState_.gridpointIndex());
}
else
{
{
continue;
}
- double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
- points_[neighbor].bias(), coordValue, {}, point);
+ double logWeight = biasedLogWeightFromPoint(
+ dimParams, points_, grid, neighbor, points_[neighbor].bias(), coordValue, {}, point);
weightSum += std::exp(logWeight);
}
std::vector<double> lambdaMarginalDistribution =
calculateFELambdaMarginalDistribution(grid, neighbors, probWeightNeighbor);
- awh_dvec coordValueAlongLambda = { coordState_.coordValue()[0], coordState_.coordValue()[1],
- coordState_.coordValue()[2], coordState_.coordValue()[3] };
+ awh_dvec coordValueAlongLambda = { coordState_.coordValue()[0],
+ coordState_.coordValue()[1],
+ coordState_.coordValue()[2],
+ coordState_.coordValue()[3] };
for (size_t i = 0; i < neighbors.size(); i++)
{
const int neighbor = neighbors[i];
gmx_bcast(points_.size() * sizeof(PointState), points_.data(), commRecord->mpi_comm_mygroup);
- gmx_bcast(weightSumCovering_.size() * sizeof(double), weightSumCovering_.data(),
- commRecord->mpi_comm_mygroup);
+ gmx_bcast(weightSumCovering_.size() * sizeof(double), weightSumCovering_.data(), commRecord->mpi_comm_mygroup);
gmx_bcast(sizeof(histogramSize_), &histogramSize_, commRecord->mpi_comm_mygroup);
}
if (numRows <= 0)
{
- std::string mesg = gmx::formatString("%s is empty!.\n\n%s", filename.c_str(),
- correctFormatMessage.c_str());
+ std::string mesg = gmx::formatString(
+ "%s is empty!.\n\n%s", filename.c_str(), correctFormatMessage.c_str());
GMX_THROW(InvalidInputError(mesg));
}
std::string mesg = gmx::formatString(
"%s contains too few data points (%d)."
"The minimum number of points is 2.",
- filename.c_str(), numRows);
+ filename.c_str(),
+ numRows);
GMX_THROW(InvalidInputError(mesg));
}
std::string mesg = gmx::formatString(
"The number of columns in %s should be at least %d."
"\n\n%s",
- filename.c_str(), numColumnsMin, correctFormatMessage.c_str());
+ filename.c_str(),
+ numColumnsMin,
+ correctFormatMessage.c_str());
GMX_THROW(InvalidInputError(mesg));
}
std::string mesg = gmx::formatString(
"Found %d trailing zero data rows in %s. Please remove trailing empty lines and "
"try again.",
- numZeroRows, filename.c_str());
+ numZeroRows,
+ filename.c_str());
GMX_THROW(InvalidInputError(mesg));
}
if (target < 0)
{
std::string mesg = gmx::formatString(
- "Target distribution weight at point %zu (%g) in %s is negative.", m, target,
+ "Target distribution weight at point %zu (%g) in %s is negative.",
+ m,
+ target,
filename.c_str());
GMX_THROW(InvalidInputError(mesg));
}
{
std::string mesg =
gmx::formatString("The target weights given in column %d in %s are all 0",
- columnIndexTarget, filename.c_str());
+ columnIndexTarget,
+ filename.c_str());
GMX_THROW(InvalidInputError(mesg));
}
biod.pnpi.spb.ru / alexxy / gromacs.git / blobdiff