Uses of Interface cern.colt.buffer.DoubleBufferConsumer (Colt 1.2.0 - API Specification)

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## Uses of Interface
cern.colt.buffer.DoubleBufferConsumer

Packages that use DoubleBufferConsumer
cern.colt.buffer | Fixed sized (non resizable) streaming buffers connected to a target objects to which data is automatically flushed upon buffer overflow.
cern.colt.list | Resizable lists holding objects or primitive data types such as `int`, `double`, etc.
hep.aida.bin | Multisets (bags) with efficient statistics operations defined upon; This package requires the Colt distribution.

Uses of DoubleBufferConsumer in cern.colt.buffer

Classes in cern.colt.buffer that implement DoubleBufferConsumer
`class` | `DoubleBuffer` Fixed sized (non resizable) streaming buffer connected to a target `DoubleBufferConsumer` to which data is automatically flushed upon buffer overflow.

Constructors in cern.colt.buffer with parameters of type DoubleBufferConsumer
`DoubleBuffer(DoubleBufferConsumer target, int capacity)` Constructs and returns a new buffer with the given target.

Uses of DoubleBufferConsumer in cern.colt.list

Classes in cern.colt.list that implement DoubleBufferConsumer
`class` | `AbstractDoubleList` Abstract base class for resizable lists holding `double` elements; abstract.
`class` | `DoubleArrayList` Resizable list holding `double` elements; implemented with arrays.

Uses of DoubleBufferConsumer in hep.aida.bin

Classes in hep.aida.bin that implement DoubleBufferConsumer
`class` | `AbstractBin1D` Abstract base class for all 1-dimensional bins consumes `double` elements.
`class` | `DynamicBin1D` 1-dimensional rebinnable bin holding `double` elements; Efficiently computes advanced statistics of data sequences.
`class` | `MightyStaticBin1D` Static and the same as its superclass, except that it can do more: Additionally computes moments of arbitrary integer order, harmonic mean, geometric mean, etc.
`class` | `QuantileBin1D` 1-dimensional non-rebinnable bin holding `double` elements with scalable quantile operations defined upon; Using little main memory, quickly computes approximate quantiles over very large data sequences with and even without a-priori knowledge of the number of elements to be filled; Conceptually a strongly lossily compressed multiset (or bag); Guarantees to respect the worst case approximation error specified upon instance construction.
`class` | `StaticBin1D` 1-dimensional non-rebinnable bin consuming `double` elements; Efficiently computes basic statistics of data sequences.

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