casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator > Class Template Referenceabstract
Abstract base class for statistics algorithms which are characterized by a range of good values. More...
#include <ConstrainedRangeStatistics.h>
Public Member Functions | |
| ConstrainedRangeStatistics ()=delete | |
| virtual | ~ConstrainedRangeStatistics () |
| virtual AccumType | getMedian (std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< AccumType > knownMin=nullptr, std::shared_ptr< AccumType > knownMax=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| In the following group of methods, if the size of the composite dataset is smaller than binningThreshholdSizeBytes, the composite dataset will be (perhaps partially) sorted and persisted in memory during the call. | |
| virtual AccumType | getMedianAbsDevMed (std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< AccumType > knownMin=nullptr, std::shared_ptr< 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 | getMedianAndQuantiles (std::map< Double, AccumType > &quantileToValue, const std::set< Double > &quantiles, std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< AccumType > knownMin=nullptr, std::shared_ptr< 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. | |
| virtual std::map< Double, AccumType > | getQuantiles (const std::set< Double > &quantiles, std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< AccumType > knownMin=nullptr, std::shared_ptr< AccumType > knownMax=NULL, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| Get the specified quantiles. | |
| virtual void | getMinMax (AccumType &mymin, AccumType &mymax) |
| get the min and max of the data set | |
| virtual uInt64 | getNPts () |
| scan the dataset(s) that have been added, and find the number of good points. | |
| virtual LocationType | getStatisticIndex (StatisticsData::STATS stat) |
| see base class description | |
| virtual void | reset () |
| reset object to initial state. | |
| Public Member Functions inherited from casacore::ClassicalStatistics< CASA_STATP > | |
| ClassicalStatistics () | |
| virtual | ~ClassicalStatistics () |
| ClassicalStatistics & | operator= (const ClassicalStatistics &other) |
| copy semantics | |
| virtual StatisticsAlgorithm< CASA_STATP > * | clone () const |
| Clone this instance. | |
| virtual StatisticsData::ALGORITHM | algorithm () const |
| get the algorithm that this object uses for computing stats | |
| virtual CASA_STATP | getMedian (std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< CASA_STATP > knownMin=nullptr, std::shared_ptr< CASA_STATP > knownMax=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| In the following group of methods, if the size of the composite dataset is smaller than binningThreshholdSizeBytes, the composite dataset will be (perhaps partially) sorted and persisted in memory during the call. | |
| virtual CASA_STATP | getMedianAndQuantiles (std::map< Double, CASA_STATP > &quantiles, const std::set< Double > &fractions, std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< CASA_STATP > knownMin=nullptr, std::shared_ptr< CASA_STATP > 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. | |
| virtual CASA_STATP | getMedianAbsDevMed (std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< CASA_STATP > knownMin=nullptr, std::shared_ptr< CASA_STATP > 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 std::map< Double, CASA_STATP > | getQuantiles (const std::set< Double > &fractions, std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< CASA_STATP > knownMin=nullptr, std::shared_ptr< CASA_STATP > knownMax=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| Get the specified quantiles. | |
| virtual void | getMinMax (CASA_STATP &mymin, CASA_STATP &mymax) |
| scan the dataset(s) that have been added, and find the min and max. | |
| virtual void | getMinMaxNpts (uInt64 &npts, CASA_STATP &mymin, CASA_STATP &mymax) |
| virtual void | setCalculateAsAdded (Bool c) |
| Should statistics be updated with calls to addData or should they only be calculated upon calls to getStatistics() etc? | |
| virtual void | setDataProvider (StatsDataProvider< CASA_STATP > *dataProvider) |
| An exception will be thrown if setCalculateAsAdded(True) has been called. | |
| void | setQuantileComputer (std::shared_ptr< ClassicalQuantileComputer< CASA_STATP > > qc) |
| Allow derived objects to set the quantile computer object. | |
| virtual void | setStatsToCalculate (std::set< StatisticsData::STATS > &stats) |
| Provide guidance to algorithms by specifying a priori which statistics the caller would like calculated. | |
| Public Member Functions inherited from casacore::StatisticsAlgorithm< CASA_STATP > | |
| virtual | ~StatisticsAlgorithm () |
| void | addData (const DataIterator &first, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False) |
| Add a dataset to an existing set of datasets on which statistics are to be calculated. | |
| CASA_STATP | getQuantile (Double quantile, std::shared_ptr< uInt64 > knownNpts=nullptr, std::shared_ptr< CASA_STATP > knownMin=nullptr, std::shared_ptr< CASA_STATP > knownMax=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| Purposefully not virtual. | |
| CASA_STATP | getStatistic (StatisticsData::STATS stat) |
| get the value of the specified statistic. | |
| StatsData< CASA_STATP > | getStatistics () |
| Return statistics. | |
| void | setData (const DataIterator &first, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False) |
| setdata() clears any current datasets or data provider and then adds the specified data set as the first dataset in the (possibly new) set of data sets for which statistics are to be calculated. | |
Protected Member Functions | |
| ConstrainedRangeStatistics (std::shared_ptr< ConstrainedRangeQuantileComputer< CASA_STATP > > qc) | |
| 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 (const ConstrainedRangeStatistics< CASA_STATP > &other) | |
| copy semantics | |
| ConstrainedRangeStatistics< CASA_STATP > & | operator= (const ConstrainedRangeStatistics< CASA_STATP > &other) |
| copy semantics | |
| virtual void | _accumNpts (uInt64 &npts, const DataIterator &dataStart, uInt64 nr, uInt dataStride) const |
| scan through the data set to determine the number of good (unmasked, weight > 0, within range) points. | |
| 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 |
| virtual AccumType | _getStatistic (StatisticsData::STATS stat) |
| virtual StatsData< AccumType > | _getStatistics () |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const DataRanges &ranges, Bool isInclude) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const MaskIterator &maskBegin, uInt maskStride) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const MaskIterator &maskBegin, uInt maskStride, const DataRanges &ranges, Bool isInclude) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightsBegin, uInt64 nr, uInt dataStride) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightsBegin, uInt64 nr, uInt dataStride, const DataRanges &ranges, Bool isInclude) const |
| virtual void | _minMax (std::shared_ptr< AccumType > &mymin, std::shared_ptr< 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 (std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightBegin, uInt64 nr, uInt dataStride, const MaskIterator &maskBegin, uInt maskStride) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) const |
| Sometimes we want the min, max, and npts all in one scan. | |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const DataRanges &ranges, Bool isInclude) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const MaskIterator &maskBegin, uInt maskStride) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< 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, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightsBegin, uInt64 nr, uInt dataStride) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightsBegin, uInt64 nr, uInt dataStride, const DataRanges &ranges, Bool isInclude) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< AccumType > &mymin, std::shared_ptr< 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, std::shared_ptr< AccumType > &mymin, std::shared_ptr< AccumType > &mymax, const DataIterator &dataBegin, const WeightsIterator &weightBegin, uInt64 nr, uInt dataStride, const MaskIterator &maskBegin, uInt maskStride) const |
| void | _setRange (std::shared_ptr< std::pair< AccumType, AccumType > > r) |
| This method is purposefully non-virtual. | |
| virtual void | _setRange ()=0 |
| derived classes need to implement how to set their respective range | |
| virtual void | _unweightedStats (StatsData< AccumType > &stats, uInt64 &ngood, LocationType &location, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) |
| no weights, no mask, no ranges | |
| virtual void | _unweightedStats (StatsData< AccumType > &stats, uInt64 &ngood, LocationType &location, const DataIterator &dataBegin, uInt64 nr, uInt dataStride, const DataRanges &ranges, Bool isInclude) |
| no weights, no mask | |
| 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) |
| virtual void | _weightedStats (StatsData< AccumType > &stats, LocationType &location, const DataIterator &dataBegin, const WeightsIterator &weightsBegin, uInt64 nr, uInt dataStride) |
| 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, 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) |
| Protected Member Functions inherited from casacore::ClassicalStatistics< CASA_STATP > | |
| void | _accumulate (StatsData< CASA_STATP > &stats, const CASA_STATP &datum, const LocationType &location) |
| void | _addData () |
| Allows derived classes to do things after data is set or added. | |
| void | _clearStats () |
| Bool | _getDoMaxMin () const |
| virtual StatsData< CASA_STATP > | _getInitialStats () const |
| virtual StatsData< CASA_STATP > & | _getStatsData () |
| Retrieve stats structure. | |
| virtual void | _minMax (std::shared_ptr< CASA_STATP > &mymin, std::shared_ptr< CASA_STATP > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) const |
| virtual void | _minMaxNpts (uInt64 &npts, std::shared_ptr< CASA_STATP > &mymin, std::shared_ptr< CASA_STATP > &mymax, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) const |
| Sometimes we want the min, max, and npts all in one scan. | |
| std::shared_ptr< StatisticsAlgorithmQuantileComputer< CASA_STATP > > | _getQuantileComputer () |
| virtual void | _unweightedStats (StatsData< CASA_STATP > &stats, uInt64 &ngood, LocationType &location, const DataIterator &dataBegin, uInt64 nr, uInt dataStride) |
| no weights, no mask, no ranges | |
| virtual void | _updateDataProviderMaxMin (const StatsData< CASA_STATP > &threadStats) |
| virtual void | _weightedStats (StatsData< CASA_STATP > &stats, LocationType &location, const DataIterator &dataBegin, const DataIterator &weightsBegin, uInt64 nr, uInt dataStride) |
| has weights, but no mask, no ranges | |
| Protected Member Functions inherited from casacore::StatisticsAlgorithm< CASA_STATP > | |
| StatisticsAlgorithm () | |
| StatisticsAlgorithm & | operator= (const StatisticsAlgorithm &other) |
| use copy semantics, except for the data provider which uses reference semantics | |
| const StatisticsDataset< CASA_STATP > & | _getDataset () const |
| These methods are purposefully not virtual. | |
| const std::set< StatisticsData::STATS > | _getStatsToCalculate () const |
| virtual const std::set< StatisticsData::STATS > & | _getUnsupportedStatistics () const |
| void | _setUnsupportedStatistics (const std::set< StatisticsData::STATS > &stats) |
| Derived classes should normally call this in their constructors, if applicable. | |
Private Attributes | |
| std::shared_ptr< std::pair< AccumType, AccumType > > | _range |
Detailed Description
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
class casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >
class casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >
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.
Definition at line 49 of file ConstrainedRangeStatistics.h.
Constructor & Destructor Documentation
◆ ConstrainedRangeStatistics() [1/3]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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delete |
Referenced by ConstrainedRangeStatistics(), and operator=().
◆ ~ConstrainedRangeStatistics()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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virtual |
◆ ConstrainedRangeStatistics() [2/3]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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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() [3/3]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protected |
copy semantics
References ConstrainedRangeStatistics().
Member Function Documentation
◆ _accumNpts() [1/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _accumNpts() [2/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _accumNpts() [3/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _accumNpts() [4/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _accumNpts() [5/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _accumNpts() [6/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _accumNpts() [7/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
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.
Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
Reimplemented in casacore::HingesFencesStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
◆ _accumNpts() [8/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _getStatistic()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
◆ _getStatistics()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
Reimplemented in casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
◆ _minMax() [1/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMax() [2/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMax() [3/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMax() [4/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMax() [5/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMax() [6/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMax() [7/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMax() [8/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMaxNpts() [1/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMaxNpts() [2/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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◆ _minMaxNpts() [3/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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References DataRanges.
◆ _minMaxNpts() [4/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMaxNpts() [5/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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Sometimes we want the min, max, and npts all in one scan.
◆ _minMaxNpts() [6/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _minMaxNpts() [7/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _minMaxNpts() [8/8]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _setRange() [1/2]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedpure virtual |
derived classes need to implement how to set their respective range
◆ _setRange() [2/2]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protected |
This method is purposefully non-virtual.
Derived classes should implement the version with no parameters.
◆ _unweightedStats() [1/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
no weights, no mask, no ranges
◆ _unweightedStats() [2/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
no weights, no mask
References DataRanges.
◆ _unweightedStats() [3/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _unweightedStats() [4/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _weightedStats() [1/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
◆ _weightedStats() [2/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ _weightedStats() [3/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
has weights, but no mask, no ranges
◆ _weightedStats() [4/4]
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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protectedvirtual |
References DataRanges.
◆ getMedian()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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In the following group of methods, if the size of the composite dataset is smaller than binningThreshholdSizeBytes, the composite dataset will be (perhaps partially) sorted and persisted in memory during the call.
In that case, and if persistSortedArray is True, this sorted array will remain in memory after the call and will be used on subsequent calls of this method when binningThreshholdSizeBytes is greater than the size of the composite dataset. If persistSortedArray 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 binningThreshholdSizeBytes, 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. min can be less than the true minimum, and max 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.
References casacore::False.
◆ getMedianAbsDevMed()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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get the median of the absolute deviation about the median of the data.
References casacore::False.
◆ getMedianAndQuantiles()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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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 quantileToValue map.
References casacore::False.
◆ getMinMax()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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get the min and max of the data set
◆ getNPts()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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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.
Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
Reimplemented in casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
◆ getQuantiles()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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Get the specified quantiles.
quantiles must be between 0 and 1, noninclusive.
References casacore::False.
◆ getStatisticIndex()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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see base class description
Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
◆ operator=()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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copy semantics
References ConstrainedRangeStatistics().
◆ reset()
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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reset object to initial state.
Clears all private fields including data, accumulators, global range.
Reimplemented from casacore::ClassicalStatistics< CASA_STATP >.
Reimplemented in casacore::ChauvenetCriterionStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::HingesFencesStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
Member Data Documentation
◆ _range
template<class AccumType, class DataIterator, class MaskIterator = const Bool*, class WeightsIterator = DataIterator>
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Definition at line 410 of file ConstrainedRangeStatistics.h.
The documentation for this class was generated from the following file:
- scimath/StatsFramework/ConstrainedRangeStatistics.h
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