qtdigest-cffi 0.5.0
A data structure for accurate on-line accumulation of rank-based statistics.
License: BSD

t-digest CFFI
=============

`t-digest <https://github.com/tdunning/t-digest>`__ is a data structure
for accurate on-line accumulation of rank-based statistics such as
quantiles and trimmed means, designed by `Ted
Dunning <https://github.com/tdunning>`__.

The t-digest construction algorithm uses a variant of 1-dimensional
k-means clustering to produce a data structure that is related to the
Q-digest. This t-digest data structure can be used to estimate quantiles
or compute other rank statistics. The advantage of the t-digest over the
Q-digest is that the t-digest can handle floating point values while the
Q-digest is limited to integers. With small changes, the t-digest can
handle any values from any ordered set that has something akin to a
mean. The accuracy of quantile estimates produced by t-digests can be
orders of magnitude more accurate than those produced by Q-digests in
spite of the fact that t-digests are more compact when stored on disk.

This package provides tested, performant, thread-safe **Python 3** CFFI
bindings to an adapted implementation of t-digest by `Usman
Masood <https://github.com/usmanm>`__ originally written for
`redis-tdigest <https://github.com/usmanm/redis-tdigest>`__.

Installation
============

You can install this package using ``pip`` or the included ``setup.py``
script:

::

   # Using pip
   pip install tdigest-cffi

   # Using setup.py
   python setup.py install

Usage
=====

.. code:: python

   from tdigest import TDigest, RawTDigest

   # Thread-safe instance with default compression factor
   digest = TDigest()

   # Raw instance with default compression factor
   digest = RawTDigest()

   # Thread-safe instance with a custom compression factor
   digest = TDigest(compression=500)

   # Digest compression
   compression = digest.compression

   # Digest weight
   weight = digest.weight

   # Centroid count
   centroid_count = digest.centroid_count

   # Compression count
   compression_count = digest.compression_count

   # Insertion with unit weight
   digest.push(1000)

   # Insertion with custom weight
   digest.push(1000, 2)

   # 99th percentile calculation
   quantile = digest.quantile(0.99)
   percentile = digest.percentile(99)

   # Cumulative distribution function
   cdf = digest.cdf(1000)  # P(X <= 1000)

   # Centroid extraction
   for centroid in digest.centroids():
       print(centroid.mean, centroid.weight)

   # Digest merging
   other = TDigest()
   other.push(42)
   digest.merge(other)

License
=======

.. code:: text

   BSD 3-Clause License

   Copyright (c) 2018, Phil Demetriou
   All rights reserved.

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions are met:

   * Redistributions of source code must retain the above copyright notice, this
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   * Redistributions in binary form must reproduce the above copyright notice,
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   * Neither the name of the copyright holder nor the names of its
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