scikitplot.cexternals#

C-External libraries vendored for high-performance extensions for Scikit-Plots.

This package contains optimized C/C++ and template-based libraries that are bundled with Scikit-Plots to provide fast numerical, geometric, and algorithmic capabilities used in advanced visualization and machine learning workflows.

These modules wrap native backends (e.g. Annoy, NumCpp, F2PY-generated code) through Python extension interfaces to ensure consistent performance and availability across environments, without relying on separate system installs.

User guide. See the C-Externals (experimental) section for further details.

Spotify annoy as submodule.#

Spotify Annoy (Approximate Nearest Neighbors Oh Yeah)#

A high-dimensional Approximate Nearest Neighbors (ANN) library implemented in C++ with Python bindings, optimized for memory usage, speed, and large-scale disk-based indices that can be shared across processes via memory mapping (mmap).

Annoy is designed to find points in space that are closest to a given query point using different distance metrics. It is particularly efficient for high-dimensional vector data such as embeddings (word, image, or user vectors).

Key Features:

Build trees for high-dimensional data using multiple distance metrics.
Serialize/deserialize indexes to bytes or disk for persistence.
Support for memory-mapped indexes for read-only sharing across processes.
Optional multi-threaded building for faster index construction.
Provides approximate nearest neighbors with tunable accuracy vs speed tradeoff.

Examples

>>> from annoy import AnnoyIndex
>>> f = 40  # vector dimensionality
>>> t = AnnoyIndex(f, 'angular')  # Length of item vector and metric
>>> t.add_item(0, [1]*f)
>>> t.build(10)  # Build 10 trees
>>> t.get_nns_by_item(0, 1)  # Find nearest neighbor

References

[1]

Spotify AB. (2013, Feb 20). “Approximate Nearest Neighbors Oh Yeah” Github. https://pypi.org/project/annoy

User guide. See the Annoy (experimental) section for further details.

`annoy`	Spotify Annoy (Approximate Nearest Neighbors Oh Yeah)
`annoy.Annoy`	Compiled on GCC.
`annoy.AnnoyIndex`	alias of `Index`
`annoy.Index`	A robust, pickle-safe, MLOps-ready wrapper around `AnnoyIndex`.

astropy stats as submodule.#

Astropy is a package intended to contain core functionality and some common tools needed for performing astronomy and astrophysics research with Python. It also provides an index for other astronomy packages and tools for managing them.

Documentation is available in the docstrings and online at https://www.astropy.org/.

User guide. See the Astropy (experimental) section for further details.

`_astropy`	Astropy is a package intended to contain core functionality and some common tools needed for performing astronomy and astrophysics research with Python.
`_astropy.stats`	This subpackage contains statistical tools provided for or used by Astropy.

NumPy f2py as submodule.#

Fortran to Python Interface Generator.

Copyright 1999 – 2011 Pearu Peterson all rights reserved. Copyright 2011 – present NumPy Developers. Permission to use, modify, and distribute this software is given under the terms of the NumPy License.

NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.

NumCpp header’s as submodule.#

User guide. See the NumCpp Header Only C++ (experimental) section for further details.

_numcpp

`_f2py`	Fortran to Python Interface Generator.
`_f2py.get_include`	Return the directory that contains the `fortranobject.c` and `.h` files.