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/*! \file
* \brief
* Declares analysis data modules for calculating histograms.
*
- * \author Teemu Murtola <teemu.murtola@cbr.su.se>
+ * \author Teemu Murtola <teemu.murtola@gmail.com>
* \inpublicapi
* \ingroup module_analysisdata
*/
#ifndef GMX_ANALYSISDATA_MODULES_HISTOGRAM_H
#define GMX_ANALYSISDATA_MODULES_HISTOGRAM_H
-#include "../abstractdata.h"
-#include "../arraydata.h"
-#include "../datamodule.h"
-#include "../../utility/uniqueptr.h"
+#include <boost/shared_ptr.hpp>
+
+#include "gromacs/analysisdata/abstractdata.h"
+#include "gromacs/analysisdata/arraydata.h"
+#include "gromacs/analysisdata/datamodule.h"
namespace gmx
{
histogramFromBins(real start, int nbins, real binwidth)
{
return AnalysisHistogramSettingsInitializer()
- .start(start).binCount(nbins).binWidth(binwidth);
+ .start(start).binCount(nbins).binWidth(binwidth);
}
* This constructor is not explicit to allow initialization of
* histograms directly from AnalysisHistogramSettingsInitializer:
* \code
- gmx::AnalysisDataSimpleHistogramModule *hist =
- new gmx::AnalysisDataSimpleHistogramModule(
- histogramFromRange(0.0, 5.0).binWidth(0.5));
+ gmx::AnalysisDataSimpleHistogramModule *hist =
+ new gmx::AnalysisDataSimpleHistogramModule(
+ histogramFromRange(0.0, 5.0).binWidth(0.5));
* \endcode
*/
AnalysisHistogramSettings(const AnalysisHistogramSettingsInitializer &settings);
};
-namespace internal
-{
-
-class BasicHistogramImpl;
-
-} // namespace internal
-
class AbstractAverageHistogram;
//! Smart pointer to manage an AbstractAverageHistogram object.
-typedef gmx_unique_ptr<AbstractAverageHistogram>::type
- AverageHistogramPointer;
+typedef boost::shared_ptr<AbstractAverageHistogram>
+ AverageHistogramPointer;
/*! \brief
* Base class for representing histograms averaged over frames.
* the main use of the object is to postprocess the histogram once the
* calculation is finished.
*
+ * This class can represent multiple histograms in one object: each column in
+ * the data is an independent histogram.
+ * The X values correspond to center of the bins, except for a cumulative
+ * histogram made with makeCumulative().
+ *
* \inpublicapi
* \ingroup module_analysisdata
*/
AverageHistogramPointer clone() const;
//! Normalizes the histogram such that the integral over it is one.
void normalizeProbability();
- //! Scales the value of each bin by an uniform scaling factor.
- void scale(real norm);
+ /*! \brief
+ * Makes the histograms cumulative by summing up each bin to all bins
+ * after it.
+ *
+ * The X values in the data are adjusted such that they match the right
+ * edges of bins instead of bin centers.
+ */
+ void makeCumulative();
+ //! Scales a single histogram by a uniform scaling factor.
+ void scaleSingle(int index, real factor);
+ //! Scales all histograms by a uniform scaling factor.
+ void scaleAll(real factor);
//! Scales the value of each bin by a different scaling factor.
- void scaleVector(real norm[]);
+ void scaleAllByVector(real factor[]);
/*! \brief
* Notifies attached modules of the histogram data.
*
/*! \brief
* Data module for per-frame histograms.
*
- * Output data contains the same number of frames as the input data.
- * Each frame contains the histogram for the points in that frame.
- * All input columns are averaged into the same histogram.
- * The number of columns equals the number of bins in the histogram.
+ * Output data contains the same number of frames and data sets as the input
+ * data. Each frame contains the histogram(s) for the points in that frame.
+ * Each input data set is processed independently into the corresponding output
+ * data set. Missing values are ignored.
+ * All input columns for a data set are averaged into the same histogram.
+ * The number of columns for all data sets equals the number of bins in the
+ * histogram.
+ *
+ * The histograms are accumulated as 64-bit integers within a frame and summed
+ * in double precision across frames, even if the output data is in single
+ * precision.
*
* \inpublicapi
* \ingroup module_analysisdata
*/
class AnalysisDataSimpleHistogramModule : public AbstractAnalysisData,
- public AnalysisDataModuleInterface
+ public AnalysisDataModuleParallel
{
public:
/*! \brief
//! Returns bin properties for the histogram.
const AnalysisHistogramSettings &settings() const;
+ virtual int frameCount() const;
+
virtual int flags() const;
- virtual void dataStarted(AbstractAnalysisData *data);
+ virtual bool parallelDataStarted(
+ AbstractAnalysisData *data,
+ const AnalysisDataParallelOptions &options);
virtual void frameStarted(const AnalysisDataFrameHeader &header);
virtual void pointsAdded(const AnalysisDataPointSetRef &points);
virtual void frameFinished(const AnalysisDataFrameHeader &header);
virtual AnalysisDataFrameRef tryGetDataFrameInternal(int index) const;
virtual bool requestStorageInternal(int nframes);
- PrivateImplPointer<internal::BasicHistogramImpl> impl_;
+ class Impl;
+
+ PrivateImplPointer<Impl> impl_;
// Copy and assign disallowed by base.
};
/*! \brief
* Data module for per-frame weighted histograms.
*
- * Output data contains the same number of frames as the input data.
- * Each frame contains the histogram for the points in that frame, interpreted
- * such that the first column passed to pointsAdded() determines the bin and
- * the rest give weights to be added to that bin (input data should have at
- * least two colums, and at least two columns should be added at the same time).
- * All input columns are averaged into the same histogram.
- * The number of columns equals the number of bins in the histogram.
+ * Output data contains the same number of frames and data sets as the input
+ * data. Each frame contains the histogram(s) for the points in that frame,
+ * interpreted such that the first column passed to pointsAdded() determines
+ * the bin and the rest give weights to be added to that bin (input data should
+ * have at least two colums, and at least two columns should be added at the
+ * same time).
+ * Each input data set is processed independently into the corresponding output
+ * data set.
+ * All input columns for a data set are averaged into the same histogram.
+ * The number of columns for all data sets equals the number of bins in the
+ * histogram.
+ *
+ * The histograms are accumulated in double precision, even if the output data
+ * is in single precision.
*
* \inpublicapi
* \ingroup module_analysisdata
*/
class AnalysisDataWeightedHistogramModule : public AbstractAnalysisData,
- public AnalysisDataModuleInterface
+ public AnalysisDataModuleParallel
{
public:
//! \copydoc AnalysisDataSimpleHistogramModule::AnalysisDataSimpleHistogramModule()
//! \copydoc AnalysisDataSimpleHistogramModule::settings()
const AnalysisHistogramSettings &settings() const;
+ virtual int frameCount() const;
+
virtual int flags() const;
- virtual void dataStarted(AbstractAnalysisData *data);
+ virtual bool parallelDataStarted(
+ AbstractAnalysisData *data,
+ const AnalysisDataParallelOptions &options);
virtual void frameStarted(const AnalysisDataFrameHeader &header);
virtual void pointsAdded(const AnalysisDataPointSetRef &points);
virtual void frameFinished(const AnalysisDataFrameHeader &header);
virtual AnalysisDataFrameRef tryGetDataFrameInternal(int index) const;
virtual bool requestStorageInternal(int nframes);
- PrivateImplPointer<internal::BasicHistogramImpl> impl_;
+ class Impl;
+
+ PrivateImplPointer<Impl> impl_;
// Copy and assign disallowed by base.
};
* Data module for bin averages.
*
* Output data contains one row for each bin; see AbstractAverageHistogram.
- * Output data contains three columns: the first is the average over all frames
- * for that bin, the second is the standard deviation of the values, and the
- * third is the number of samples in that bin.
+ * Output data contains one column for each input data set.
+ * The value in a column is the average over all frames of that data set for
+ * that bin.
* The input data is interpreted such that the first column passed to
* pointsAdded() determines the bin and the rest give values to be added to
* that bin (input data should have at least two colums, and at least two
* columns should be added at the same time).
- * All input columns are averaged into the same histogram.
+ * All input columns for a data set are averaged into the same histogram.
*
* \inpublicapi
* \ingroup module_analysisdata
*/
class AnalysisDataBinAverageModule : public AbstractAnalysisArrayData,
- public AnalysisDataModuleInterface
+ public AnalysisDataModuleSerial
{
public:
//! \copydoc AnalysisDataSimpleHistogramModule::AnalysisDataSimpleHistogramModule()
void init(const AnalysisHistogramSettings &settings);
//! \copydoc AnalysisDataSimpleHistogramModule::settings()
- const AnalysisHistogramSettings &settings() const { return settings_; }
+ const AnalysisHistogramSettings &settings() const;
virtual int flags() const;
virtual void dataFinished();
private:
- AnalysisHistogramSettings settings_;
+ class Impl;
+
+ PrivateImplPointer<Impl> impl_;
// Copy and assign disallowed by base.
};
//! Smart pointer to manage an AnalysisDataSimpleHistogramModule object.
typedef boost::shared_ptr<AnalysisDataSimpleHistogramModule>
- AnalysisDataSimpleHistogramModulePointer;
+ AnalysisDataSimpleHistogramModulePointer;
//! Smart pointer to manage an AnalysisDataWeightedHistogramModule object.
typedef boost::shared_ptr<AnalysisDataWeightedHistogramModule>
- AnalysisDataWeightedHistogramModulePointer;
+ AnalysisDataWeightedHistogramModulePointer;
//! Smart pointer to manage an AnalysisDataBinAverageModule object.
typedef boost::shared_ptr<AnalysisDataBinAverageModule>
- AnalysisDataBinAverageModulePointer;
+ AnalysisDataBinAverageModulePointer;
} // namespace gmx
biod.pnpi.spb.ru / alexxy / gromacs.git / blobdiff