ClassicalStatistics.h

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//# Copyright (C) 2000,2001
//# Associated Universities, Inc. Washington DC, USA.
//#
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#ifndef SCIMATH_CLASSICALSTATISTICSS_H
#define SCIMATH_CLASSICALSTATISTICSS_H
 
#include <casacore/casa/aips.h>
 
#include <casacore/scimath/StatsFramework/StatisticsAlgorithm.h>
 
#include <casacore/scimath/StatsFramework/ClassicalQuantileComputer.h>
#include <casacore/scimath/StatsFramework/StatisticsTypes.h>
#include <casacore/scimath/StatsFramework/StatisticsUtilities.h>
#include <set>
#include <vector>
#include <utility>
 
namespace casacore {
 
template <class T> class PtrHolder;
 
// Class to calculate statistics in a "classical" sense, ie using accumulators
// with no special filtering beyond optional range filtering etc.
//
// setCalculateAsAdded() allows one to specify if statistics should be
// calculated and updated on upon each call to set/addData(). If False,
// statistics will be calculated only when getStatistic(), getStatistics(), or
// similar statistics computing methods are called. Setting this value to True
// allows the caller to not have to keep all the data accessible at once. Note
// however, that all data must be simultaneously accessible if quantile-like
// (eg median) calculations are desired.
//
// Objects of this class are instantiated using a ClassicalQuantileComputer
// object for computation of quantile-like statistics. See the documentation
// of StatisticsAlgorithm for details relating QuantileComputer classes.
 
template <
    class AccumType, class DataIterator, class MaskIterator=const Bool*,
    class WeightsIterator=DataIterator
>
class ClassicalStatistics
    : public StatisticsAlgorithm<CASA_STATP> {
 
    using ChunkType = typename StatisticsDataset<CASA_STATP>::ChunkData;
 
public:
 
    ClassicalStatistics();
 
    // copy semantics
    ClassicalStatistics(const ClassicalStatistics& cs);
 
    virtual ~ClassicalStatistics();
 
    // copy semantics
    ClassicalStatistics& operator=(const ClassicalStatistics& other);
 
    // Clone this instance
    virtual StatisticsAlgorithm<CASA_STATP>* clone() const;
    
    // get the algorithm that this object uses for computing stats
    virtual StatisticsData::ALGORITHM algorithm() const {
        return StatisticsData::CLASSICAL;
    };
 
    // <group>
    // In the following group of methods, if the size of the composite dataset
    // is smaller than <src>binningThreshholdSizeBytes</src>, the composite
    // dataset will be (perhaps partially) sorted and persisted in memory during
    // the call. In that case, and if <src>persistSortedArray</src> is True,
    // this sorted array will remain in memory after the call and will be used
    // on subsequent calls of this method when
    // <src>binningThreshholdSizeBytes</src> is greater than the size of the
    // composite dataset. If <src>persistSortedArray</src> is False, the sorted
    // array will not be stored after this call completes and so any subsequent
    // calls for which the dataset size is less than
    // <src>binningThreshholdSizeBytes</src>, the dataset will be sorted from
    // scratch. Values which are not included due to non-unity strides, are not
    // included in any specified ranges, are masked, or have associated weights
    // of zero are not considered as dataset members for quantile computations.
    // If one has a priori information regarding the number of points (npts)
    // and/or the minimum and maximum values of the data set, these can be
    // supplied to improve performance. Note however, that if these values are
    // not correct, the resulting median and/or quantile values will also not be
    // correct (although see the following notes regarding max/min). Note that
    // if this object has already had getStatistics() called, and the min and
    // max were calculated, there is no need to pass these values in as they
    // have been stored internally and used (although passing them in shouldn't
    // hurt anything). If provided, npts, the number of points falling in the
    // specified ranges which are not masked and have weights > 0, should be
    // exactly correct. <src>min</src> can be less than the true minimum, and
    // <src>max</src> can be greater than the True maximum, but for best
    // performance, these should be as close to the actual min and max as
    // possible. In order for quantile computations to occur over multiple
    // datasets, all datasets must be available. This means that if
    // setCalculateAsAdded() was previously called by passing in a value of
    // True, these methods will throw an exception as the previous call
    // indicates that there is no guarantee that all datasets will be available.
    // If one uses a data provider (by having called setDataProvider()), then
    // this should not be an issue.
 
    // Get the median of the distribution. For a dataset with an odd number of
    // good points, the median is just the value at index int(N/2) in the
    // equivalent sorted dataset, where N is the number of points. For a dataset
    // with an even number of points, the median is the mean of the values at
    // indices int(N/2)-1 and int(N/2) in the sorted dataset.  <src>nBins</src>
    // is the number of bins, per histogram, to use to bin the data. More
    // bins decrease the likelihood that multiple passes of the data set will be
    // necessary, but also increase the amount of memory used. If nBins is set
    // to less than 1,000, it is automatically increased to 1,000; there should
    // be no reason to ever set nBins to be this small.
    virtual AccumType getMedian(
        CountedPtr<uInt64> knownNpts=nullptr,
        CountedPtr<AccumType> knownMin=nullptr,
        CountedPtr<AccumType> knownMax=nullptr,
        uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt nBins=10000
    );
 
    // If one needs to compute both the median and quantile values, it is better
    // to call getMedianAndQuantiles() rather than getMedian() and
    // getQuantiles() separately, as the first will scan large data sets fewer
    // times than calling the separate methods. The return value is the median;
    // the quantiles are returned in the <src>quantiles</src> map. Values in the
    // <src>fractions</src> set represent the locations in the CDF and should be
    // between 0 and 1, exclusive.
    virtual AccumType getMedianAndQuantiles(
        std::map<Double, AccumType>& quantiles,
        const std::set<Double>& fractions, CountedPtr<uInt64> knownNpts=nullptr,
        CountedPtr<AccumType> knownMin=nullptr,
        CountedPtr<AccumType> knownMax=nullptr,
        uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt nBins=10000
    );
 
    // get the median of the absolute deviation about the median of the data.
    virtual AccumType getMedianAbsDevMed(
        CountedPtr<uInt64> knownNpts=nullptr,
        CountedPtr<AccumType> knownMin=nullptr,
        CountedPtr<AccumType> knownMax=nullptr,
        uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt nBins=10000
    );
 
    // Get the specified quantiles. <src>fractions</src> must be between 0 and
    // 1, noninclusive.
    virtual std::map<Double, AccumType> getQuantiles(
        const std::set<Double>& fractions,
        CountedPtr<uInt64> knownNpts=nullptr,
        CountedPtr<AccumType> knownMin=nullptr,
        CountedPtr<AccumType> knownMax=nullptr,
        uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt nBins=10000
    );
    // </group>
 
    // <group>
    // scan the dataset(s) that have been added, and find the min and max. This
    // method may be called even if setStatsToCaclulate has been called and MAX
    // and MIN has been excluded. If setCalculateAsAdded(True) has previously
    // been called after this object has been (re)initialized, an exception will
    // be thrown. The second version also determines npts in the same scan.
    virtual void getMinMax(AccumType& mymin, AccumType& mymax);
 
    virtual void getMinMaxNpts(
        uInt64& npts, AccumType& mymin, AccumType& mymax
    );
    // </group>
 
    // scan the dataset(s) that have been added, and find the number of good
    // points. This method may be called even if setStatsToCaclulate has been
    // called and NPTS has been excluded. If setCalculateAsAdded(True) has
    // previously been called after this object has been (re)initialized, an
    // exception will be thrown.
    virtual uInt64 getNPts();
 
    // see base class description
    virtual std::pair<Int64, Int64> getStatisticIndex(
        StatisticsData::STATS stat
    );
 
    // reset object to initial state. Clears all private fields including data,
    // accumulators, etc.
    virtual void reset();
 
    // Should statistics be updated with calls to addData or should they only be
    // calculated upon calls to getStatistics() etc? Beware that calling this
    // will automatically reinitialize the object, so that it will contain no
    // references to data et al. after this method has been called.
    virtual void setCalculateAsAdded(Bool c);
 
    // An exception will be thrown if setCalculateAsAdded(True) has been called.
    virtual void setDataProvider(StatsDataProvider<CASA_STATP> *dataProvider);
 
    // Allow derived objects to set the quantile computer object. API developers
    // shouldn't need to call this, unless they are writing derived classes
    // of ClassicalStatistics. Purposefully non-virtual. Derived classes should
    // not implement.
    void setQuantileComputer(
        CountedPtr<ClassicalQuantileComputer<CASA_STATP>> qc
    ) {
        _qComputer = qc;
    }
 
    virtual void setStatsToCalculate(std::set<StatisticsData::STATS>& stats);
 
protected:
 
    // This constructor should be used by derived objects in order to set
    // the proper quantile computer object
    ClassicalStatistics(CountedPtr<ClassicalQuantileComputer<CASA_STATP> > qc);
 
    // <group>
    // scan through the data set to determine the number of good (unmasked,
    // weight > 0, within range) points. The first with no mask, no ranges, and
    // no weights is trivial with npts = nr in this class, but is implemented
    // here so that derived classes may override it.
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin, uInt64 nr, uInt dataStride
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _accumNpts(
        uInt64& npts, const DataIterator& dataBegin,
        const WeightsIterator& weightBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    ) const;
    // </group>
 
    // <group>
    inline void _accumulate(
        StatsData<AccumType>& stats, const AccumType& datum,
        const LocationType& location
    );
 
    inline void _accumulate(
        StatsData<AccumType>& stats, const AccumType& datum,
        const AccumType& weight, const LocationType& location
    );
    // </group>
 
    void _addData();
 
    void _clearStats();
 
    Bool _getDoMaxMin() const { return _doMaxMin; }
 
    virtual StatsData<AccumType> _getInitialStats() const;
    
    virtual AccumType _getStatistic(StatisticsData::STATS stat);
 
    virtual StatsData<AccumType> _getStatistics();
 
    // Retrieve stats structure. Allows derived classes to maintain their own
    // StatsData structs.
    virtual StatsData<AccumType>& _getStatsData() { return _statsData; }
 
    virtual const StatsData<AccumType>& _getStatsData() const {
        return _statsData;
    }
    
    // <group>
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        uInt64 nr, uInt dataStride
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        uInt64 nr, uInt dataStride, const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        uInt64 nr, uInt dataStride, const MaskIterator& maskBegin,
        uInt maskStride, const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMax(
        CountedPtr<AccumType>& mymin, CountedPtr<AccumType>& mymax,
        const DataIterator& dataBegin, const WeightsIterator& weightBegin,
        uInt64 nr, uInt dataStride, const MaskIterator& maskBegin,
        uInt maskStride
    ) const;
    // </group>
 
    // <group>
    // Sometimes we want the min, max, and npts all in one scan.
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin, uInt64 nr,
        uInt dataStride
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin, uInt64 nr,
        uInt dataStride, const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin, uInt64 nr,
        uInt dataStride, const MaskIterator& maskBegin, uInt maskStride
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin, uInt64 nr,
        uInt dataStride, const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin,
        const WeightsIterator& weightsBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    ) const;
 
    virtual void _minMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymin,
        CountedPtr<AccumType>& mymax, const DataIterator& dataBegin,
        const WeightsIterator& weightBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    ) const;
    // </group>
 
    CountedPtr<StatisticsAlgorithmQuantileComputer<CASA_STATP>>
    _getQuantileComputer() {
        return _qComputer;
    }
 
    // <group>
    // no weights, no mask, no ranges
    virtual void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride
    );
 
    // no weights, no mask
    virtual void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    );
 
    virtual void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    );
 
    virtual void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, uInt64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    );
 
    // </group>
    virtual void _updateDataProviderMaxMin(
        const StatsData<AccumType>& threadStats
    );
 
    // <group>
    // has weights, but no mask, no ranges
    virtual void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        uInt64 nr, uInt dataStride
    );
 
    virtual void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        uInt64 nr, uInt dataStride, const DataRanges& ranges, Bool isInclude
    );
 
    virtual void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightBegin,
        uInt64 nr, uInt dataStride, const MaskIterator& maskBegin,
        uInt maskStride
    );
 
    virtual void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightBegin,
        uInt64 nr, uInt dataStride, const MaskIterator& maskBegin,
        uInt maskStride, const DataRanges& ranges, Bool isInclude
    );
    // </group>
 
private:
    StatsData<AccumType> _statsData;
    Bool _calculateAsAdded{False}, _doMaxMin{True}, _mustAccumulate{False};
 
    CountedPtr<ClassicalQuantileComputer<CASA_STATP>> _qComputer{};
 
    void _computeMinMax(
        CountedPtr<AccumType>& mymax, CountedPtr<AccumType>& mymin,
        DataIterator dataIter, MaskIterator maskIter,
        WeightsIterator weightsIter, uInt64 dataCount, const ChunkType& chunk
    );
 
    void _computeMinMaxNpts(
        uInt64& npts, CountedPtr<AccumType>& mymax,
        CountedPtr<AccumType>& mymin, DataIterator dataIter,
        MaskIterator maskIter, WeightsIterator weightsIter, uInt64 dataCount,
        const ChunkType& chunk
    );
 
    void _computeNpts(
        uInt64& npts, DataIterator dataIter, MaskIterator maskIter,
        WeightsIterator weightsIter, uInt64 dataCount, const ChunkType& chunk
    );
 
    void _computeStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        DataIterator dataIter, MaskIterator maskIter,
        WeightsIterator weightsIter, uInt64 count, const ChunkType& chunk
    );
 
    // scan dataset(s) to find min and max
    void _doMinMax(AccumType& vmin, AccumType& vmax);
 
    uInt64 _doMinMaxNpts(AccumType& vmin, AccumType& vmax);
 
    uInt64 _doNpts();
 
    // for quantile computations, if necessary, determines npts, min, max to
    // send to quantile calculator methods
    void _doNptsMinMax(
        uInt64& mynpts, AccumType& mymin, AccumType& mymax,
        CountedPtr<uInt64> knownNpts, CountedPtr<AccumType> knownMin,
        CountedPtr<AccumType> knownMax
    );
 
};
 
}
 
#ifndef CASACORE_NO_AUTO_TEMPLATES
#include <casacore/scimath/StatsFramework/ClassicalStatistics.tcc>
#endif 
 
#endif