adaptivekde 1.2.0

Optimal fixed or locally adaptive kernel density estimation

This package implements adaptive kernel density estimation algorithms for 1-dimensional signals developed by Hideaki Shimazaki. This enables the generation of smoothed histograms that preserve important density features at multiple scales, as opposed to naive single-bandwidth kernel density methods that can either over or under smooth density estimates. These methods are described in Shimazaki's paper:

H. Shimazaki and S. Shinomoto, "Kernel Bandwidth Optimization in Spike Rate Estimation," in Journal of Computational Neuroscience 29(1-2): 171-182, 2010 http://dx.doi.org/10.1007/s10827-009-0180-4.

License: All software in this package is licensed under the Apache License 2.0. See LICENSE.txt for more details.

Authors: Hideaki Shimazaki ([e-mail]) shimazaki on Github Lee A.D. Cooper ([e-mail]) cooperlab on GitHub Subhasis Ray ([e-mail])

Three methods are implemented in this package:

1. sshist - can be used to determine the optimal number of histogram bins for independent identically distributed samples from an underlying one-dimensional distribution. The principal here is to minimize the L2 norm of the difference between the histogram and the underlying distribution.

2. sskernel - implements kernel density estimation with a single globally-optimized bandwidth.

3. ssvkernel - implements kernel density estimation with a locally variable bandwidth.

Requirements: Python >= 3.9 NumPy >= 1.24

Tested on Python 3.9 (NumPy 1.26) and Python 3.11 (NumPy 2.4).

Installation: pip install adaptivekde

Or from source: pip install -e .

Running tests: python -m pytest tests/ -v python tests/run_tests.py # standalone summary with golden checks python tests/run_benchmarks.py # benchmark timing (appends to tests/benchmarks.json)