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38 #include "biaswriter.h"
43 #include "gromacs/awh/awh.h"
44 #include "gromacs/mdtypes/awh_params.h"
45 #include "gromacs/mdtypes/commrec.h"
46 #include "gromacs/trajectory/energyframe.h"
47 #include "gromacs/utility/gmxassert.h"
48 #include "gromacs/utility/smalloc.h"
51 #include "correlationgrid.h"
53 #include "pointstate.h"
62 * Map the output entry type to a normalization type.
64 * The data is written to energy file blocks in the order given by
65 * the iterator of this map, which is based on the enum value
66 * (and matches the order of the lines below).
68 const std::map<AwhOutputEntryType, Normalization> outputTypeToNormalization =
70 { AwhOutputEntryType::MetaData, Normalization::None },
71 { AwhOutputEntryType::CoordValue, Normalization::Coordinate },
72 { AwhOutputEntryType::Pmf, Normalization::FreeEnergy },
73 { AwhOutputEntryType::Bias, Normalization::FreeEnergy },
74 { AwhOutputEntryType::Visits, Normalization::Distribution },
75 { AwhOutputEntryType::Weights, Normalization::Distribution },
76 { AwhOutputEntryType::Target, Normalization::Distribution },
77 { AwhOutputEntryType::ForceCorrelationVolume, Normalization::Distribution },
78 { AwhOutputEntryType::FrictionTensor, Normalization::None }
82 * Gets the coordinate normalization value for the given dimension.
84 * \param[in] bias The AWH bias.
85 * \param[in] dimIndex Dimensional index.
86 * \returns the coordinate normalization value.
88 float getCoordNormalizationValue(const Bias &bias,
91 /* AWH may use different units internally but here we convert to user units */
92 return bias.dimParams()[dimIndex].scaleInternalToUserInput(1);
96 * Gets the normalization value for the given output entry type.
98 * \param[in] outputType Output entry type.
99 * \param[in] bias The AWH bias.
100 * \param[in] numBlocks The number of blocks for this output type.
101 * \returns the normalization value.
103 float getNormalizationValue(AwhOutputEntryType outputType,
107 float normalizationValue = 0;
111 case AwhOutputEntryType::CoordValue:
112 normalizationValue = getCoordNormalizationValue(bias, numBlocks);
114 case AwhOutputEntryType::Visits:
115 case AwhOutputEntryType::Weights:
116 case AwhOutputEntryType::Target:
117 normalizationValue = static_cast<float>(bias.state().points().size());
119 case AwhOutputEntryType::ForceCorrelationVolume:
120 normalizationValue = static_cast<double>(bias.state().points().size());
126 return normalizationValue;
131 AwhEnergyBlock::AwhEnergyBlock(int numPoints,
132 Normalization normalizationType,
133 float normalizationValue) :
134 normalizationType(normalizationType),
135 normalizationValue(normalizationValue),
140 BiasWriter::BiasWriter(const Bias &bias)
142 std::map<AwhOutputEntryType, int> outputTypeNumBlock; /* Number of blocks per output type */
144 /* Different output variable types need different number of blocks.
145 * We keep track of the starting block for each variable.
148 for (const auto &pair : outputTypeToNormalization)
150 const AwhOutputEntryType outputType = pair.first;
152 outputTypeToBlock_[outputType] = blockCount;
154 if (outputType == AwhOutputEntryType::CoordValue)
156 outputTypeNumBlock[outputType] = bias.ndim();
158 else if (outputType == AwhOutputEntryType::FrictionTensor)
160 outputTypeNumBlock[outputType] = bias.forceCorrelationGrid().tensorSize();
164 /* Most output variable types need one block */
165 outputTypeNumBlock[outputType] = 1;
168 blockCount += outputTypeNumBlock[outputType];
171 /* Initialize the data blocks for each variable */
172 for (const auto &pair : outputTypeToNormalization)
174 const AwhOutputEntryType outputType = pair.first;
176 if (outputType == AwhOutputEntryType::MetaData)
178 numPoints = static_cast<int>(AwhOutputMetaData::Count);
182 numPoints = bias.state().points().size();
184 for (int b = 0; b < outputTypeNumBlock[outputType]; b++)
186 block_.emplace_back(numPoints,
188 getNormalizationValue(outputType, bias, b));
194 * Normalizes block data for output.
196 * \param[in,out] block The block to normalize.
197 * \param[in] bias The AWH bias.
199 static void normalizeBlock(AwhEnergyBlock *block, const Bias &bias)
201 gmx::ArrayRef<float> data = block->data();
203 /* Here we operate on float data (which is accurate enough, since it
204 * is statistical data that will never reach full float precision).
205 * But since we can have very many data points, we sum into a double.
208 float minValue = GMX_FLOAT_MAX;
211 switch (block->normalizationType)
213 case Normalization::None:
215 case Normalization::Coordinate:
216 /* Normalize coordinate values by a scale factor */
217 for (float &point : data)
219 point *= block->normalizationValue;
222 case Normalization::FreeEnergy:
223 /* Normalize free energy values by subtracting the minimum value */
224 for (gmx::index index = 0; index < data.ssize(); index++)
226 if (bias.state().points()[index].inTargetRegion() && data[index] < minValue)
228 minValue = data[index];
231 for (gmx::index index = 0; index < data.ssize(); index++)
233 if (bias.state().points()[index].inTargetRegion())
235 data[index] -= minValue;
240 case Normalization::Distribution:
241 /* Normalize distribution values by normalizing their sum */
242 for (float &point : data)
248 recipNorm = block->normalizationValue/static_cast<float>(sum);
250 for (float &point : data)
256 GMX_RELEASE_ASSERT(false, "Unknown AWH normalization type");
261 void BiasWriter::transferMetaDataToWriter(gmx::index metaDataIndex,
262 AwhOutputMetaData metaDataType,
265 gmx::ArrayRef<float> data = block_[getVarStartBlock(AwhOutputEntryType::MetaData)].data();
266 GMX_ASSERT(metaDataIndex < data.ssize(), "Attempt to transfer AWH meta data to block for index out of range");
268 /* Transfer the point data of this variable to the right block(s) */
269 switch (metaDataType)
271 case AwhOutputMetaData::NumBlock:
272 /* The number of subblocks per awh (needed by gmx_energy) */
273 data[metaDataIndex] = static_cast<double>(block_.size());
274 /* Note: a single subblock takes only a single type and we need doubles. */
276 case AwhOutputMetaData::TargetError:
277 /* The theoretical target error */
278 data[metaDataIndex] = bias.params().initialErrorInKT*std::sqrt(bias.params().initialHistogramSize/bias.state().histogramSize().histogramSize());
280 case AwhOutputMetaData::ScaledSampleWeight:
281 /* The logarithm of the sample weight relative to a sample weight of 1 at the initial time.
282 In the normal case: this will increase in the initial stage and then stay at a constant value. */
283 data[metaDataIndex] = bias.state().histogramSize().logScaledSampleWeight();
285 case AwhOutputMetaData::Count:
291 BiasWriter::transferPointDataToWriter(AwhOutputEntryType outputType,
294 gmx::ArrayRef<const float> pmf)
296 /* The starting block index of this output type.
297 * Note that some variables need several (contiguous) blocks.
299 int blockStart = getVarStartBlock(outputType);
300 GMX_ASSERT(pointIndex < static_cast<int>(block_[blockStart].data().size()), "Attempt to transfer AWH data to block for point index out of range");
302 const CorrelationGrid &forceCorrelation = bias.forceCorrelationGrid();
303 int numCorrelation = forceCorrelation.tensorSize();
305 /* Transfer the point data of this variable to the right block(s) */
309 case AwhOutputEntryType::MetaData:
310 GMX_RELEASE_ASSERT(false, "MetaData is handled by a different function");
312 case AwhOutputEntryType::CoordValue:
314 const awh_dvec &coordValue = bias.getGridCoordValue(pointIndex);
315 for (int d = 0; d < bias.ndim(); d++)
317 block_[b].data()[pointIndex] = coordValue[d];
322 case AwhOutputEntryType::Pmf:
323 block_[b].data()[pointIndex] = bias.state().points()[pointIndex].inTargetRegion() ? pmf[pointIndex] : 0;
325 case AwhOutputEntryType::Bias:
327 const awh_dvec &coordValue = bias.getGridCoordValue(pointIndex);
328 block_[b].data()[pointIndex] = bias.state().points()[pointIndex].inTargetRegion() ? bias.calcConvolvedBias(coordValue) : 0;
331 case AwhOutputEntryType::Visits:
332 block_[b].data()[pointIndex] = bias.state().points()[pointIndex].numVisitsTot();
334 case AwhOutputEntryType::Weights:
335 block_[b].data()[pointIndex] = bias.state().points()[pointIndex].weightSumTot();
337 case AwhOutputEntryType::Target:
338 block_[b].data()[pointIndex] = bias.state().points()[pointIndex].target();
340 case AwhOutputEntryType::ForceCorrelationVolume:
341 block_[b].data()[pointIndex] = forceCorrelation.tensors()[pointIndex].getVolumeElement(forceCorrelation.dtSample);
343 case AwhOutputEntryType::FrictionTensor:
344 /* Store force correlation in units of friction, i.e. time/length^2 */
345 for (int n = 0; n < numCorrelation; n++)
347 block_[b].data()[pointIndex] = forceCorrelation.tensors()[pointIndex].getTimeIntegral(n, forceCorrelation.dtSample);
352 GMX_RELEASE_ASSERT(false, "Unknown AWH output variable");
357 void BiasWriter::prepareBiasOutput(const Bias &bias)
359 /* Pack the AWH data into the writer data. */
361 /* Evaluate the PMF for all points */
362 gmx::ArrayRef<float> pmf = block_[getVarStartBlock(AwhOutputEntryType::Pmf)].data();
363 bias.state().getPmf(pmf);
365 /* Pack the data point by point.
366 * Unfortunately we can not loop over a class enum, so we cast to int.
367 * \todo Use strings instead of enum when we port the output to TNG.
369 for (int i = 0; i < static_cast<int>(AwhOutputMetaData::Count); i++)
371 transferMetaDataToWriter(i, static_cast<AwhOutputMetaData>(i), bias);
373 for (const auto &pair : outputTypeToNormalization)
375 const AwhOutputEntryType outputType = pair.first;
376 /* Skip metadata (transfered above) and unused blocks */
377 if (outputType == AwhOutputEntryType::MetaData || !hasVarBlock(outputType))
381 for (size_t m = 0; m < bias.state().points().size(); m++)
383 transferPointDataToWriter(outputType, m, bias, pmf);
387 /* For looks of the output, normalize it */
388 for (AwhEnergyBlock &block : block_)
390 normalizeBlock(&block, bias);
394 int BiasWriter::writeToEnergySubblocks(const Bias &bias,
397 prepareBiasOutput(bias);
399 for (size_t b = 0; b < block_.size(); b++)
401 sub[b].type = xdr_datatype_float;
402 sub[b].nr = block_[b].data().size();
403 sub[b].fval = block_[b].data().data();
406 return block_.size();