* the iterator of this map, which is based on the enum value
* (and matches the order of the lines below).
*/
-const std::map<AwhOutputEntryType, Normalization> outputTypeToNormalization =
-{
- { AwhOutputEntryType::MetaData, Normalization::None },
- { AwhOutputEntryType::CoordValue, Normalization::Coordinate },
- { AwhOutputEntryType::Pmf, Normalization::FreeEnergy },
- { AwhOutputEntryType::Bias, Normalization::FreeEnergy },
- { AwhOutputEntryType::Visits, Normalization::Distribution },
- { AwhOutputEntryType::Weights, Normalization::Distribution },
- { AwhOutputEntryType::Target, Normalization::Distribution },
+const std::map<AwhOutputEntryType, Normalization> outputTypeToNormalization = {
+ { AwhOutputEntryType::MetaData, Normalization::None },
+ { AwhOutputEntryType::CoordValue, Normalization::Coordinate },
+ { AwhOutputEntryType::Pmf, Normalization::FreeEnergy },
+ { AwhOutputEntryType::Bias, Normalization::FreeEnergy },
+ { AwhOutputEntryType::Visits, Normalization::Distribution },
+ { AwhOutputEntryType::Weights, Normalization::Distribution },
+ { AwhOutputEntryType::Target, Normalization::Distribution },
{ AwhOutputEntryType::ForceCorrelationVolume, Normalization::Distribution },
- { AwhOutputEntryType::FrictionTensor, Normalization::None }
+ { AwhOutputEntryType::FrictionTensor, Normalization::None }
};
/*! \brief
* \param[in] dimIndex Dimensional index.
* \returns the coordinate normalization value.
*/
-float getCoordNormalizationValue(const Bias &bias,
- int dimIndex)
+float getCoordNormalizationValue(const Bias& bias, int dimIndex)
{
/* AWH may use different units internally but here we convert to user units */
return bias.dimParams()[dimIndex].scaleInternalToUserInput(1);
* \param[in] numBlocks The number of blocks for this output type.
* \returns the normalization value.
*/
-float getNormalizationValue(AwhOutputEntryType outputType,
- const Bias &bias,
- int numBlocks)
+float getNormalizationValue(AwhOutputEntryType outputType, const Bias& bias, int numBlocks)
{
float normalizationValue = 0;
case AwhOutputEntryType::ForceCorrelationVolume:
normalizationValue = static_cast<double>(bias.state().points().size());
break;
- default:
- break;
+ default: break;
}
return normalizationValue;
}
-} // namespace
+} // namespace
-AwhEnergyBlock::AwhEnergyBlock(int numPoints,
- Normalization normalizationType,
- float normalizationValue) :
+AwhEnergyBlock::AwhEnergyBlock(int numPoints, Normalization normalizationType, float normalizationValue) :
normalizationType(normalizationType),
normalizationValue(normalizationValue),
data_(numPoints)
{
}
-BiasWriter::BiasWriter(const Bias &bias)
+BiasWriter::BiasWriter(const Bias& bias)
{
std::map<AwhOutputEntryType, int> outputTypeNumBlock; /* Number of blocks per output type */
* We keep track of the starting block for each variable.
*/
int blockCount = 0;
- for (const auto &pair : outputTypeToNormalization)
+ for (const auto& pair : outputTypeToNormalization)
{
const AwhOutputEntryType outputType = pair.first;
{
}
/* Initialize the data blocks for each variable */
- for (const auto &pair : outputTypeToNormalization)
+ for (const auto& pair : outputTypeToNormalization)
{
const AwhOutputEntryType outputType = pair.first;
int numPoints;
}
for (int b = 0; b < outputTypeNumBlock[outputType]; b++)
{
- block_.emplace_back(numPoints,
- pair.second,
- getNormalizationValue(outputType, bias, b));
+ block_.emplace_back(numPoints, pair.second, getNormalizationValue(outputType, bias, b));
}
}
}
* \param[in,out] block The block to normalize.
* \param[in] bias The AWH bias.
*/
-static void normalizeBlock(AwhEnergyBlock *block, const Bias &bias)
+static void normalizeBlock(AwhEnergyBlock* block, const Bias& bias)
{
gmx::ArrayRef<float> data = block->data();
switch (block->normalizationType)
{
- case Normalization::None:
- break;
+ case Normalization::None: break;
case Normalization::Coordinate:
/* Normalize coordinate values by a scale factor */
- for (float &point : data)
+ for (float& point : data)
{
point *= block->normalizationValue;
}
break;
case Normalization::Distribution:
/* Normalize distribution values by normalizing their sum */
- for (float &point : data)
+ for (float& point : data)
{
sum += point;
}
if (sum > 0)
{
- recipNorm = block->normalizationValue/static_cast<float>(sum);
+ recipNorm = block->normalizationValue / static_cast<float>(sum);
}
- for (float &point : data)
+ for (float& point : data)
{
point *= recipNorm;
}
break;
- default:
- GMX_RELEASE_ASSERT(false, "Unknown AWH normalization type");
- break;
+ default: GMX_RELEASE_ASSERT(false, "Unknown AWH normalization type"); break;
}
}
-void BiasWriter::transferMetaDataToWriter(gmx::index metaDataIndex,
- AwhOutputMetaData metaDataType,
- const Bias &bias)
+void BiasWriter::transferMetaDataToWriter(gmx::index metaDataIndex,
+ AwhOutputMetaData metaDataType,
+ const Bias& bias)
{
gmx::ArrayRef<float> data = block_[getVarStartBlock(AwhOutputEntryType::MetaData)].data();
- GMX_ASSERT(metaDataIndex < data.ssize(), "Attempt to transfer AWH meta data to block for index out of range");
+ GMX_ASSERT(metaDataIndex < data.ssize(),
+ "Attempt to transfer AWH meta data to block for index out of range");
/* Transfer the point data of this variable to the right block(s) */
switch (metaDataType)
break;
case AwhOutputMetaData::TargetError:
/* The theoretical target error */
- data[metaDataIndex] = bias.params().initialErrorInKT*std::sqrt(bias.params().initialHistogramSize/bias.state().histogramSize().histogramSize());
+ data[metaDataIndex] = bias.params().initialErrorInKT
+ * std::sqrt(bias.params().initialHistogramSize
+ / bias.state().histogramSize().histogramSize());
break;
case AwhOutputMetaData::ScaledSampleWeight:
/* The logarithm of the sample weight relative to a sample weight of 1 at the initial time.
In the normal case: this will increase in the initial stage and then stay at a constant value. */
data[metaDataIndex] = bias.state().histogramSize().logScaledSampleWeight();
break;
- case AwhOutputMetaData::Count:
- break;
+ case AwhOutputMetaData::Count: break;
}
}
-void
-BiasWriter::transferPointDataToWriter(AwhOutputEntryType outputType,
- int pointIndex,
- const Bias &bias,
- gmx::ArrayRef<const float> pmf)
+void BiasWriter::transferPointDataToWriter(AwhOutputEntryType outputType,
+ int pointIndex,
+ const Bias& bias,
+ gmx::ArrayRef<const float> pmf)
{
/* The starting block index of this output type.
* Note that some variables need several (contiguous) blocks.
*/
int blockStart = getVarStartBlock(outputType);
- GMX_ASSERT(pointIndex < static_cast<int>(block_[blockStart].data().size()), "Attempt to transfer AWH data to block for point index out of range");
+ GMX_ASSERT(pointIndex < static_cast<int>(block_[blockStart].data().size()),
+ "Attempt to transfer AWH data to block for point index out of range");
- const CorrelationGrid &forceCorrelation = bias.forceCorrelationGrid();
+ const CorrelationGrid& forceCorrelation = bias.forceCorrelationGrid();
int numCorrelation = forceCorrelation.tensorSize();
/* Transfer the point data of this variable to the right block(s) */
break;
case AwhOutputEntryType::CoordValue:
{
- const awh_dvec &coordValue = bias.getGridCoordValue(pointIndex);
+ const awh_dvec& coordValue = bias.getGridCoordValue(pointIndex);
for (int d = 0; d < bias.ndim(); d++)
{
block_[b].data()[pointIndex] = coordValue[d];
}
break;
case AwhOutputEntryType::Pmf:
- block_[b].data()[pointIndex] = bias.state().points()[pointIndex].inTargetRegion() ? pmf[pointIndex] : 0;
+ block_[b].data()[pointIndex] =
+ bias.state().points()[pointIndex].inTargetRegion() ? pmf[pointIndex] : 0;
break;
case AwhOutputEntryType::Bias:
{
- const awh_dvec &coordValue = bias.getGridCoordValue(pointIndex);
- block_[b].data()[pointIndex] = bias.state().points()[pointIndex].inTargetRegion() ? bias.calcConvolvedBias(coordValue) : 0;
+ const awh_dvec& coordValue = bias.getGridCoordValue(pointIndex);
+ block_[b].data()[pointIndex] = bias.state().points()[pointIndex].inTargetRegion()
+ ? bias.calcConvolvedBias(coordValue)
+ : 0;
}
break;
case AwhOutputEntryType::Visits:
block_[b].data()[pointIndex] = bias.state().points()[pointIndex].target();
break;
case AwhOutputEntryType::ForceCorrelationVolume:
- block_[b].data()[pointIndex] = forceCorrelation.tensors()[pointIndex].getVolumeElement(forceCorrelation.dtSample);
+ block_[b].data()[pointIndex] =
+ forceCorrelation.tensors()[pointIndex].getVolumeElement(forceCorrelation.dtSample);
break;
case AwhOutputEntryType::FrictionTensor:
/* Store force correlation in units of friction, i.e. time/length^2 */
for (int n = 0; n < numCorrelation; n++)
{
- block_[b].data()[pointIndex] = forceCorrelation.tensors()[pointIndex].getTimeIntegral(n, forceCorrelation.dtSample);
+ block_[b].data()[pointIndex] = forceCorrelation.tensors()[pointIndex].getTimeIntegral(
+ n, forceCorrelation.dtSample);
b++;
}
break;
- default:
- GMX_RELEASE_ASSERT(false, "Unknown AWH output variable");
- break;
+ default: GMX_RELEASE_ASSERT(false, "Unknown AWH output variable"); break;
}
}
-void BiasWriter::prepareBiasOutput(const Bias &bias)
+void BiasWriter::prepareBiasOutput(const Bias& bias)
{
/* Pack the AWH data into the writer data. */
{
transferMetaDataToWriter(i, static_cast<AwhOutputMetaData>(i), bias);
}
- for (const auto &pair : outputTypeToNormalization)
+ for (const auto& pair : outputTypeToNormalization)
{
const AwhOutputEntryType outputType = pair.first;
/* Skip metadata (transfered above) and unused blocks */
}
/* For looks of the output, normalize it */
- for (AwhEnergyBlock &block : block_)
+ for (AwhEnergyBlock& block : block_)
{
normalizeBlock(&block, bias);
}
}
-int BiasWriter::writeToEnergySubblocks(const Bias &bias,
- t_enxsubblock *sub)
+int BiasWriter::writeToEnergySubblocks(const Bias& bias, t_enxsubblock* sub)
{
prepareBiasOutput(bias);
return block_.size();
}
-} // namespace gmx
+} // namespace gmx
biod.pnpi.spb.ru / alexxy / gromacs.git / blobdiff