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casacore
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casacore
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Base class of statistics algorithm class hierarchy. More...
#include <StatisticsAlgorithm.h>
Public Member Functions | |
| virtual | ~StatisticsAlgorithm () |
| virtual StatisticsAlgorithm < CASA_STATP > * | clone () const =0 |
| Clone this instance. More... | |
| 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. More... | |
| void | addData (const DataIterator &first, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | addData (const DataIterator &first, const MaskIterator &maskFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | addData (const DataIterator &first, const MaskIterator &maskFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | addData (const DataIterator &first, const WeightsIterator &weightFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | addData (const DataIterator &first, const WeightsIterator &weightFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | addData (const DataIterator &first, const WeightsIterator &weightFirst, const MaskIterator &maskFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | addData (const DataIterator &first, const WeightsIterator &weightFirst, const MaskIterator &maskFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| virtual StatisticsData::ALGORITHM | algorithm () const =0 |
| get the algorithm that this object uses for computing stats More... | |
| 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)=0 |
| 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)=0 |
The return value is the median; the quantiles are returned in the quantileToValue map. More... | |
| 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)=0 |
| get the median of the absolute deviation about the median of the data. More... | |
| AccumType | getQuantile (Double quantile, CountedPtr< uInt64 > knownNpts=nullptr, CountedPtr< AccumType > knownMin=nullptr, CountedPtr< AccumType > knownMax=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000) |
| Purposefully not virtual. More... | |
| virtual std::map< Double, AccumType > | getQuantiles (const std::set< Double > &quantiles, CountedPtr< uInt64 > npts=nullptr, CountedPtr< AccumType > min=nullptr, CountedPtr< AccumType > max=nullptr, uInt binningThreshholdSizeBytes=4096 *4096, Bool persistSortedArray=False, uInt nBins=10000)=0 |
| get a map of quantiles to values. More... | |
| AccumType | getStatistic (StatisticsData::STATS stat) |
| get the value of the specified statistic. More... | |
| virtual LocationType | getStatisticIndex (StatisticsData::STATS stat)=0 |
| certain statistics such as max and min have locations in the dataset associated with them. More... | |
| StatsData< AccumType > | getStatistics () |
| Return statistics. More... | |
| virtual void | reset () |
| reset this object by clearing data. More... | |
| 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. More... | |
| void | setData (const DataIterator &first, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | setData (const DataIterator &first, const MaskIterator &maskFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | setData (const DataIterator &first, const MaskIterator &maskFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | setData (const DataIterator &first, const WeightsIterator &weightFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | setData (const DataIterator &first, const WeightsIterator &weightFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False) |
| void | setData (const DataIterator &first, const WeightsIterator &weightFirst, const MaskIterator &maskFirst, uInt nr, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| void | setData (const DataIterator &first, const WeightsIterator &weightFirst, const MaskIterator &maskFirst, uInt nr, const DataRanges &dataRanges, Bool isInclude=True, uInt dataStride=1, Bool nrAccountsForStride=False, uInt maskStride=1) |
| virtual void | setDataProvider (StatsDataProvider< CASA_STATP > *dataProvider) |
| instead of setting and adding data "by hand", set the data provider that will provide all the data sets. More... | |
| virtual void | setStatsToCalculate (std::set< StatisticsData::STATS > &stats) |
| Provide guidance to algorithms by specifying a priori which statistics the caller would like calculated. More... | |
Protected Member Functions | |
| StatisticsAlgorithm () | |
| StatisticsAlgorithm (const StatisticsAlgorithm &other) | |
| use copy semantics, except for the data provider which uses reference semantics More... | |
| StatisticsAlgorithm & | operator= (const StatisticsAlgorithm &other) |
| use copy semantics, except for the data provider which uses reference semantics More... | |
| virtual void | _addData () |
| Allows derived classes to do things after data is set or added. More... | |
| const StatisticsDataset < CASA_STATP > & | _getDataset () const |
| These methods are purposefully not virtual. More... | |
| StatisticsDataset< CASA_STATP > & | _getDataset () |
| virtual AccumType | _getStatistic (StatisticsData::STATS stat)=0 |
| virtual StatsData< AccumType > | _getStatistics ()=0 |
| 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. More... | |
Private Member Functions | |
| void | _resetExceptDataset () |
Private Attributes | |
| std::set< StatisticsData::STATS > | _statsToCalculate |
| std::set< StatisticsData::STATS > | _unsupportedStats |
| StatisticsDataset< CASA_STATP > | _dataset |
| Bool | _resetDataset |
Base class of statistics algorithm class hierarchy.
The default implementation is such that statistics are only calculated when methods that actually compute statistics are called. Until then, the iterators which point to the beginning of data sets, masks, etc. are held in memory. Thus, the caller must keep all data sets available for the statistics object until these methods are called, and of course, if the actual data values are changed between adding data and calculating statistics, the updated values are used when calculating statistics. Derived classes may override this behavior.
PRECISION CONSIDERATIONS Many statistics are computed via accumulators. This can lead to precision issues, especially for large datasets. For this reason, it is highly recommended that the data type one uses as the AccumType be of higher precision, if possible, than the data type pointed to by input iterator. So for example, if one has a data set of Float values (to which the InputIterator type points to), then one should use type Double for the AccumType. In this case, the Float data values will be converted to Doubles before they are accumulated.
METHODS OF PROVIDING DATA Data may be provided in one of two mutually exclusive ways. The first way is simpler, and that is to use the setData()/addData() methods. Calling setData() will clear any previous data that was added via these methods or via a data provider (see below). Calling addData() after having called setData() will add a data set to the set of data sets on which statistics will be calculated. In order for this to work correctly, the iterators which are passed into these methods must still be valid when statistics are calculated (although note that some derived classes allow certain statistics to be updated as data sets are added via these methods. See specific classes for details).
The second way to provide data is via an object derived from class StatsDataProvider, in which methods are implemented for retrieving various information about the data sets to be included. Such an interface is necessary for data structures which do not easily lend themselves to be provided via the setData()/addData() methods. For example, in the case of iterating through a Lattice, a lattice iterator will overwrite the memory location of the previous chunk of data with the current chunk of data. Therefore, if one does not wish to load data from the entire lattice into memory (which is why LatticeIterator was designed to have the behavior it does), one must use the LatticeStatsDataProvider class, which the statistics framework will use to iterate through the lattice, only keeping one chunk of the data of the lattice in memory any given moment.
STORAGE OF DATA In order to reduce maintenance costs, the accounting details of the data sets are maintained in a StatisticsDataset object. This object is held in memory at the StatisticsAlgorithm level in the _dataset private field of this class when a derived class is instantiated. A StatisticsDataset object should never need to be explicitly instantiated by an API developer.
QUANTILES A quantile is a value contained in a data set, such that, it has a zero-based index of ceil(q*n)-1 in the equivalent ordered dataset, where 0 < q < 1 specifies the fractional location within the ordered dataset and n is the total number of valid elements. Note that, for a dataset with an odd number of elements, the median is the same as the quantile value when q = 0.5. However, there is no such correspondence between the median in a dataset with an even number of elements, since the median in that case is given by the mean of the elements of zero-based indices n/2-1 and n/2 in the equivalent ordered dataset. Thus, in the case of a dataset with an even number of values, the median may not even exist in the dataset, while a generic quantile value must exist in the dataset by definition. Note when calculating quantile values, a dataset that does not fall in specified dataset ranges, is not included via a stride specification, is masked, or has a weight of zero, is not considered a member of the dataset for the purposes of quantile calculations.
CLASS ORGANIZATION In general, in the StatsFramework class hierarchy, classes derived from StatisticsAlgorithm and its descendants contain methods which calculate the relevant statistics which are computed via accumulation. These classes also contain the top level methods for computing the quantile-like statistics, for the convenience of the API developer. Derived classes of StatisticsAlgorithm normally will have a private field which is an object that contains methods which compute the various quantile-like statistics. These so-called QuantileComputer classes have been created to reduce maintainability costs; because putting all the code into single class files was becoming unwieldy. The concrete QuantileComputer classes are ultimately derived from StatisticsAlgorithmQuantileComputer, which is the virtual base class of this hierarchy. StatisticsAlgorithm objects do not contain a StatisticsAlgorithmQuantileComputer private field, since StatisticsAlgorithm is also a virtual base class and hence no actual statistics are computed within it. The design is such that the only classes an API developer should over instantiate are the derived classes of StatisticsAlgorithm; the QuantileComputer classes should never be explicitly instantiated in code which uses the StatsFramework API.
Definition at line 137 of file StatisticsAlgorithm.h.
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use copy semantics, except for the data provider which uses reference semantics
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inlineprotectedvirtual |
Allows derived classes to do things after data is set or added.
Default implementation does nothing.
Reimplemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::ClassicalStatistics< CASA_STATP >.
Definition at line 355 of file StatisticsAlgorithm.h.
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inlineprotected |
These methods are purposefully not virtual.
Derived classes should not implement.
Definition at line 360 of file StatisticsAlgorithm.h.
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Definition at line 364 of file StatisticsAlgorithm.h.
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protectedpure virtual |
Implemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::ConstrainedRangeStatistics< CASA_STATP >.
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protectedpure virtual |
Implemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ConstrainedRangeStatistics< CASA_STATP >, casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
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Definition at line 371 of file StatisticsAlgorithm.h.
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Definition at line 376 of file StatisticsAlgorithm.h.
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private |
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inlineprotected |
Derived classes should normally call this in their constructors, if applicable.
Definition at line 382 of file StatisticsAlgorithm.h.
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| ) |
Add a dataset to an existing set of datasets on which statistics are to be calculated.
nr is the number of points to be considered. If dataStride is greater than 1, when nrAccountsForStride=True indicates that the stride has been taken into account in the value of nr. Otherwise, it has not so that the actual number of points to include is nr/dataStride if nr % dataStride == 0 or (int)(nr/dataStride) + 1 otherwise. if one calls this method after a data provider has been set, an exception will be thrown. In this case, one should call setData(), rather than addData(), to indicate that the underlying data provider should be removed. dataRanges provide the ranges of data to include if isInclude is True, or ranges of data to exclude if isInclude is False. If a datum equals the end point of a data range, it is considered good (included) if isInclude is True, and it is considered bad (excluded) if isInclude is False.
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::addData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| ) |
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get the algorithm that this object uses for computing stats
Implemented in casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ChauvenetCriterionStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::HingesFencesStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
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Clone this instance.
Implemented in casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ChauvenetCriterionStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::HingesFencesStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
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get the median of the absolute deviation about the median of the data.
Implemented in casacore::ClassicalStatistics< CASA_STATP >, and casacore::ConstrainedRangeStatistics< CASA_STATP >.
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The return value is the median; the quantiles are returned in the quantileToValue map.
Implemented in casacore::ClassicalStatistics< CASA_STATP >, and casacore::ConstrainedRangeStatistics< CASA_STATP >.
| AccumType casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::getQuantile | ( | Double | quantile, |
| CountedPtr< uInt64 > | knownNpts = nullptr, |
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| CountedPtr< AccumType > | knownMin = nullptr, |
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| CountedPtr< AccumType > | knownMax = nullptr, |
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| uInt | binningThreshholdSizeBytes = 4096 *4096, |
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| Bool | persistSortedArray = False, |
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| uInt | nBins = 10000 |
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Purposefully not virtual.
Derived classes should not implement.
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get a map of quantiles to values.
Implemented in casacore::ClassicalStatistics< CASA_STATP >, and casacore::ConstrainedRangeStatistics< CASA_STATP >.
| AccumType casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::getStatistic | ( | StatisticsData::STATS | stat | ) |
get the value of the specified statistic.
Purposefully not virtual. Derived classes should not implement.
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certain statistics such as max and min have locations in the dataset associated with them.
This method gets those locations. The first value in the returned pair is the zero-based dataset number that was set or added. The second value is the zero-based index in that dataset. A data stride of greater than one is not accounted for, so the index represents the actual location in the data set, independent of the dataStride value.
Implemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::ConstrainedRangeStatistics< CASA_STATP >.
| StatsData<AccumType> casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::getStatistics | ( | ) |
Return statistics.
Purposefully not virtual. Derived classes should not implement.
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use copy semantics, except for the data provider which uses reference semantics
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reset this object by clearing data.
Reimplemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ConstrainedRangeStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ConstrainedRangeStatistics< CASA_STATP >, casacore::ChauvenetCriterionStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::HingesFencesStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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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.
See addData() for parameter meanings. These methods are purposefully not virtual. Derived classes should not implement.
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| ) |
| void casacore::StatisticsAlgorithm< AccumType, DataIterator, MaskIterator, WeightsIterator >::setData | ( | const DataIterator & | first, |
| const WeightsIterator & | weightFirst, | ||
| const MaskIterator & | maskFirst, | ||
| uInt | nr, | ||
| const DataRanges & | dataRanges, | ||
| Bool | isInclude = True, |
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| uInt | dataStride = 1, |
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| Bool | nrAccountsForStride = False, |
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| uInt | maskStride = 1 |
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| ) |
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instead of setting and adding data "by hand", set the data provider that will provide all the data sets.
Calling this method will clear any other data sets that have previously been set or added. Method is virtual to allow derived classes to carry out any necessary specialized accounting when resetting the data provider.
Reimplemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::ClassicalStatistics< CASA_STATP >.
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Provide guidance to algorithms by specifying a priori which statistics the caller would like calculated.
Reimplemented in casacore::ClassicalStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, casacore::ClassicalStatistics< CASA_STATP >, casacore::BiweightStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >, and casacore::FitToHalfStatistics< AccumType, DataIterator, MaskIterator, WeightsIterator >.
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Definition at line 390 of file StatisticsAlgorithm.h.
Referenced by casacore::StatisticsAlgorithm< CASA_STATP >::_getDataset().
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Definition at line 391 of file StatisticsAlgorithm.h.
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Definition at line 389 of file StatisticsAlgorithm.h.
Referenced by casacore::StatisticsAlgorithm< CASA_STATP >::_getStatsToCalculate().
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Definition at line 389 of file StatisticsAlgorithm.h.
Referenced by casacore::StatisticsAlgorithm< CASA_STATP >::_getUnsupportedStatistics(), and casacore::StatisticsAlgorithm< CASA_STATP >::_setUnsupportedStatistics().
1.8.5