Metadata-Version: 1.1
Name: tdigest-cffi
Version: 0.1.1
Summary: A data structure for accurate on-line accumulation of rank-based statistics.
Home-page: https://github.com/kpdemetriou/tdigest-cffi
Author: Phil Demetriou
Author-email: inbox@philonas.net
License: BSD
Description-Content-Type: UNKNOWN
Description: 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.insert(1000)
        
           # Insertion with custom weight
           digest.insert(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.insert(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
             list of conditions and the following disclaimer.
        
           * Redistributions in binary form must reproduce the above copyright notice,
             this list of conditions and the following disclaimer in the documentation
             and/or other materials provided with the distribution.
        
           * Neither the name of the copyright holder nor the names of its
             contributors may be used to endorse or promote products derived from
             this software without specific prior written permission.
        
           THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
           AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
           IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
           DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
           FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
           DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
           SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
           CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
           OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
           OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: System Administrators
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Utilities
