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/*! \file
* \brief
* Declares gmx::AnalysisDataAverageModule.
*
- * \author Teemu Murtola <teemu.murtola@cbr.su.se>
+ * \author Teemu Murtola <teemu.murtola@gmail.com>
* \inpublicapi
* \ingroup module_analysisdata
*/
#include <vector>
-#include "../arraydata.h"
-#include "../datamodule.h"
+#include "gromacs/analysisdata/abstractdata.h"
+#include "gromacs/analysisdata/arraydata.h"
+#include "gromacs/analysisdata/datamodule.h"
+#include "gromacs/utility/common.h"
namespace gmx
{
/*! \brief
- * Data module for simple averaging of columns.
+ * Data module for independently averaging each column in input data.
*
- * Output data contains a frame for each column of input data.
- * There are two columns: the average and standard deviation of
- * that column.
- * The data becomes available only after the original data has been
- * finished.
+ * Computes the average and standard deviation independently for each column in
+ * the input data. Multipoint data, multiple data sets, and missing data
+ * points are all supported.
+ * The average is always calculated over all frames and data points for a
+ * column.
*
- * Multipoint data and missing data points are both supported. The average
- * is always calculated over all data points present in a column.
+ * Output data contains a column for each data set in the input data, and a
+ * frame for each column in the input data. If different data sets have
+ * different number of columns, the frame count accomodates the largest data
+ * set. Other columns are padded with zero values that are additionally marked
+ * as missing.
+ * Each value in the output data is the average of the corresponding
+ * input column in the corresponding input data set. The error value for each
+ * value provides the standard deviation of the corresponding input column.
+ * average(), standardDeviation(), and sampleCount() methods are also
+ * provided for convenient access to these properties.
+ *
+ * The output data becomes available only after the input data has been
+ * finished.
*
* \inpublicapi
* \ingroup module_analysisdata
*/
class AnalysisDataAverageModule : public AbstractAnalysisArrayData,
- public AnalysisDataModuleInterface
+ public AnalysisDataModuleSerial
{
public:
AnalysisDataAverageModule();
virtual ~AnalysisDataAverageModule();
using AbstractAnalysisArrayData::setXAxis;
+ using AbstractAnalysisArrayData::setXAxisValue;
+
+ /*! \brief
+ * Sets the averaging to happen over entire data sets.
+ *
+ * If \p bDataSets is false (the default), the module averages each
+ * column separately. The output will have a column for each data set,
+ * and a row for each column.
+ *
+ * If \p bDataSets is true, the module averages all values within
+ * a single data set into a single average/standard deviation.
+ * The output will have only one column, with one row for each data
+ * set.
+ */
+ void setAverageDataSets(bool bDataSets);
virtual int flags() const;
virtual void frameFinished(const AnalysisDataFrameHeader &header);
virtual void dataFinished();
- //! Convenience access to the average of a data column.
- real average(int index) const;
- //! Convenience access to the standard deviation of a data column.
- real stddev(int index) const;
+ /*! \brief
+ * Convenience access to the average of a data column.
+ *
+ * Note that the interpretation of the parameters follows their naming:
+ * with \c setAverageDataSets(false), \p dataSet corresponds to a
+ * column in the output, but with \c setAverageDataSets(false) it
+ * corresponds to an output row. In both cases, it selects the data
+ * set; with \c setAverageDataSets(false), \p column should always be
+ * zero as there is only one value per data set.
+ */
+ real average(int dataSet, int column) const;
+ /*! \brief
+ * Convenience access to the standard deviation of a data column.
+ *
+ * See average() for the interpretation of the parameters.
+ */
+ real standardDeviation(int dataSet, int column) const;
+ /*! \brief
+ * Access the number of samples for a data column.
+ *
+ * See average() for the interpretation of the parameters.
+ */
+ int sampleCount(int dataSet, int column) const;
private:
- std::vector<int> nsamples_;
+ class Impl;
- // Copy and assign disallowed by base.
+ PrivateImplPointer<Impl> impl_;
};
//! Smart pointer to manage an AnalysisDataAverageModule object.
typedef boost::shared_ptr<AnalysisDataAverageModule>
- AnalysisDataAverageModulePointer;
+ AnalysisDataAverageModulePointer;
+
+/*! \brief
+ * Data module for averaging of columns for each frame.
+ *
+ * Output data has the same number of frames as the input data.
+ * The number of columns in the output data is the same as the number of data
+ * sets in the input data.
+ * Each frame in the output contains the average of the column values for each
+ * data set in the corresponding frame of the input data.
+ *
+ * Multipoint data and missing data points are both supported. The average
+ * is always calculated over all data points present in a column for a data
+ * set.
+ *
+ * \inpublicapi
+ * \ingroup module_analysisdata
+ */
+class AnalysisDataFrameAverageModule : public AbstractAnalysisData,
+ public AnalysisDataModuleSerial
+{
+ public:
+ AnalysisDataFrameAverageModule();
+ virtual ~AnalysisDataFrameAverageModule();
+
+ virtual int frameCount() const;
+
+ virtual int flags() const;
+
+ virtual void dataStarted(AbstractAnalysisData *data);
+ virtual void frameStarted(const AnalysisDataFrameHeader &header);
+ virtual void pointsAdded(const AnalysisDataPointSetRef &points);
+ virtual void frameFinished(const AnalysisDataFrameHeader &header);
+ virtual void dataFinished();
+
+ private:
+ virtual AnalysisDataFrameRef tryGetDataFrameInternal(int index) const;
+ virtual bool requestStorageInternal(int nframes);
+
+ class Impl;
+
+ PrivateImplPointer<Impl> impl_;
+};
+
+//! Smart pointer to manage an AnalysisDataFrameAverageModule object.
+typedef boost::shared_ptr<AnalysisDataFrameAverageModule>
+ AnalysisDataFrameAverageModulePointer;
} // namespace gmx
biod.pnpi.spb.ru / alexxy / gromacs.git / blobdiff