ConstrainedRangeStatistics.h

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//# Copyright (C) 2000,2001
//# Associated Universities, Inc. Washington DC, USA.
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#ifndef SCIMATH_CONSTRAINEDRANGESTATISTICS_H
#define SCIMATH_CONSTRAINEDRANGESTATISTICS_H

#include <casacore/casa/aips.h>

#include <casacore/scimath/StatsFramework/ClassicalStatistics.h>
#include <casacore/scimath/StatsFramework/ConstrainedRangeQuantileComputer.h>

#include <set>
#include <vector>
#include <utility>

namespace casacore {

// Abstract base class for statistics algorithms which are characterized by
// a range of good values. The range is usually calculated dynamically based
// on the entire distribution. The specifics of such calculations are
// delegated to derived classes.

template <
    class AccumType, class DataIterator, class MaskIterator=const Bool*,
    class WeightsIterator=DataIterator
>
class ConstrainedRangeStatistics : public ClassicalStatistics<CASA_STATP> {
public:

    ConstrainedRangeStatistics() = delete;

    virtual ~ConstrainedRangeStatistics();

    // <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.
    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
    );

    // 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
    );

    // 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 seperate methods. The return value is the median;
    // the quantiles are returned in the <src>quantileToValue</src> map.
    virtual AccumType getMedianAndQuantiles(
        std::map<Double, AccumType>& quantileToValue,
        const std::set<Double>& quantiles,
        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>quantiles</src> must be between 0 and
    // 1, noninclusive.
    virtual std::map<Double, AccumType> getQuantiles(
        const std::set<Double>& quantiles,
        CountedPtr<uInt64> knownNpts=nullptr,
        CountedPtr<AccumType> knownMin=nullptr,
        CountedPtr<AccumType> knownMax=NULL,
        uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt nBins=10000
    );
    // </group>

    // get the min and max of the data set
    virtual void getMinMax(AccumType& mymin, AccumType& mymax);

    // 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 LocationType getStatisticIndex(StatisticsData::STATS stat);

    // reset object to initial state. Clears all private fields including data,
    // accumulators, global range.
    virtual void reset();

protected:

    // Concrete derived classes are responsible for providing an appropriate
    // QuantileComputer object to the constructor, which is ultimately passed
    // up the instantiation hierarchy and stored at the StatisticsAlgorithm
    // level.
    ConstrainedRangeStatistics(
       CountedPtr<ConstrainedRangeQuantileComputer<CASA_STATP>> qc
    );

    // copy semantics
    ConstrainedRangeStatistics(
        const ConstrainedRangeStatistics<CASA_STATP>& other
    );

    // copy semantics
    ConstrainedRangeStatistics<CASA_STATP>& operator=(
        const ConstrainedRangeStatistics<CASA_STATP>& other
    );

    // <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& dataStart, uInt64 nr, uInt dataStride
    ) const;

    virtual void _accumNpts(
        uInt64& npts,
        const DataIterator& dataStart, 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>

    virtual AccumType _getStatistic(StatisticsData::STATS stat);

    virtual StatsData<AccumType> _getStatistics();

    // <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>

    // This method is purposefully non-virtual. Derived classes
    // should implement the version with no parameters.
    void _setRange(CountedPtr<std::pair<AccumType, AccumType> > r);

    // derived classes need to implement how to set their respective range
    virtual void _setRange() = 0;

    // <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>

    // <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:

    CountedPtr<std::pair<AccumType, AccumType>> _range{};

};

}

#ifndef CASACORE_NO_AUTO_TEMPLATES
#include <casacore/scimath/StatsFramework/ConstrainedRangeStatistics.tcc>
#endif

#endif