FitToHalfStatistics.h

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//# Copyright (C) 2000,2001
//# Associated Universities, Inc. Washington DC, USA.
//#
//# This library is free software; you can redistribute it and/or modify it
//# under the terms of the GNU Library General Public License as published by
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//#
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//#        Internet email: aips2-request@nrao.edu.
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//# $Id: Array.h 21545 2015-01-22 19:36:35Z gervandiepen $

#ifndef SCIMATH_FITTOHALFSTATISTICS_H
#define SCIMATH_FITTOHALFSTATISTICS_H

#include <casacore/casa/aips.h>

#include <casacore/scimath/Mathematics/ConstrainedRangeStatistics.h>
#include <casacore/scimath/Mathematics/FitToHalfStatisticsData.h>

namespace casacore {

// Class to calculate statistics using the so-called fit to half algorithm. In this
// algorithm, a center value is specified, and only points greater or equal or less or equal
// this value are included. Furthermore, each of the included points is reflected about
// the center value, and these virtual points are added to the included points and
// the union of sets of included real points and virtual points are used for computing statistics.
// The specified center point is therefore the mean and median of the resulting
// distribution, and the total number of points is exactly twice the number of real
// data points that are included.

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

    const static AccumType TWO;

    // <src>value</src> is only used if <src>center</src>=CVALUE
    FitToHalfStatistics(
        FitToHalfStatisticsData::CENTER center=FitToHalfStatisticsData::CMEAN,
        FitToHalfStatisticsData::USE_DATA useData=FitToHalfStatisticsData::LE_CENTER,
        AccumType value=0
    );

    virtual ~FitToHalfStatistics();

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

    // get the algorithm that this object uses for computing stats
    virtual StatisticsData::ALGORITHM algorithm() const {
        return StatisticsData::FITTOHALF;
    };

    // The median is just the center value, so none of the parameters to this method are used.
    AccumType getMedian(
        CountedPtr<uInt64> knownNpts=NULL, CountedPtr<AccumType> knownMin=NULL,
        CountedPtr<AccumType> knownMax=NULL, uInt binningThreshholdSizeBytes=4096*4096,
        Bool persistSortedArray=False, uInt64 nBins=10000
    );

    // <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.

    AccumType getMedianAndQuantiles(
        std::map<Double, AccumType>& quantiles, const std::set<Double>& fractions,
        CountedPtr<uInt64> knownNpts=NULL, CountedPtr<AccumType> knownMin=NULL,
        CountedPtr<AccumType> knownMax=NULL,
        uInt binningThreshholdSizeBytes=4096*4096, Bool persistSortedArray=False,
        uInt64 nBins=10000
    );

    // get the median of the absolute deviation about the median of the data.
    AccumType getMedianAbsDevMed(
        CountedPtr<uInt64> knownNpts=NULL,
        CountedPtr<AccumType> knownMin=NULL, CountedPtr<AccumType> knownMax=NULL,
        uInt binningThreshholdSizeBytes=4096*4096, Bool persistSortedArray=False,
        uInt64 nBins=10000
    );

    // Get the specified quantiles. <src>fractions</src> must be between 0 and 1,
    // noninclusive.
    std::map<Double, AccumType> getQuantiles(
        const std::set<Double>& fractions, CountedPtr<uInt64> knownNpts=NULL,
        CountedPtr<AccumType> knownMin=NULL, CountedPtr<AccumType> knownMax=NULL,
        uInt binningThreshholdSizeBytes=4096*4096, Bool persistSortedArray=False,
        uInt64 nBins=10000
    );
    // </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.
    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.
    uInt64 getNPts();

    // reset object to initial state. Clears all private fields including data,
    // accumulators, global range. It does not affect the fence factor (_f), which was
    // set at object construction.
    virtual void reset();

    // This class does not allow statistics to be calculated as datasets are added, so
    // an exception will be thrown if <src>c</src> is True.
    void setCalculateAsAdded(Bool c);

protected:

    virtual void _clearData();

    virtual StatsData<AccumType> _getInitialStats() const;

    StatsData<AccumType> _getStatistics();

    inline StatsData<AccumType>& _getStatsData() { return _statsData; }

    inline const StatsData<AccumType>& _getStatsData() const { return _statsData; }

    // <group>
    // no weights, no mask, no ranges
    void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, Int64 nr, uInt dataStride
    );

    // no weights, no mask
    void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, Int64 nr, uInt dataStride,
        const DataRanges& ranges, Bool isInclude
    );

    void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, Int64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride
    );

    void _unweightedStats(
        StatsData<AccumType>& stats, uInt64& ngood, LocationType& location,
        const DataIterator& dataBegin, Int64 nr, uInt dataStride,
        const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    );
    // </group>

    void _updateDataProviderMaxMin(
        const StatsData<AccumType>& threadStats
    );  

    
    // <group>
    // has weights, but no mask, no ranges
    void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        Int64 nr, uInt dataStride
    );

    void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightsBegin,
        Int64 nr, uInt dataStride, const DataRanges& ranges, Bool isInclude
    );

    void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightBegin,
        Int64 nr, uInt dataStride, const MaskIterator& maskBegin, uInt maskStride
    );

    void _weightedStats(
        StatsData<AccumType>& stats, LocationType& location,
        const DataIterator& dataBegin, const WeightsIterator& weightBegin,
        Int64 nr, uInt dataStride, const MaskIterator& maskBegin, uInt maskStride,
        const DataRanges& ranges, Bool isInclude
    );
    // </group>

private:
    FitToHalfStatisticsData::CENTER _centerType;
    Bool _useLower;
    AccumType _centerValue;
    StatsData<AccumType> _statsData;
    Bool _doMedAbsDevMed, _rangeIsSet;
    // these are the max and min for the real portion of the dataset
    CountedPtr<AccumType> _realMax, _realMin;

    void _getRealMinMax(
            CountedPtr<AccumType>& realMin, CountedPtr<AccumType>& realMax,
        CountedPtr<AccumType> knownMin, CountedPtr<AccumType> knownMax
    );

    void _setRange();
};

}

#ifndef CASACORE_NO_AUTO_TEMPLATES
#include <casacore/scimath/Mathematics/FitToHalfStatistics.tcc>
#endif //# CASACORE_NO_AUTO_TEMPLATES

#endif