pyordstat 1.0.0

Order statistics for Python

pyordstat

Order statistics for Python 📊

Introduction

A package providing order statistics for Python. The package implements the well-known formulas for order statistics in a format that is amenable to being used with either infinite distributions expressed as functions, or finite distributions expressed as lists of values and their probability mass. It is also compatible with the format of SciPy's stats module.

What are order statistics?

Order statistics are the statistics of the k-th variable in a sample of N elements. Given a probability distribution from which identical and independent samples are drawn, the k-th order statistic is the k-th smallest value in the sample. For example, the median is the 50th order statistic in a sample of 100. This package provides functions for calculating the probability density function (PDF) and cumulative distribution function (CDF) of order statistics for arbitrary distributions.

Installation

To add and install this package as a dependency of your project, run poetry add pyordstat.

Usage

Continuous distributions

Use the class ContinuousOrderStatistics to calculate the PDF and CDF of order statistics defined on a continuous support. The class takes callable PDF and CDF functions as its first arguments, and any successive parameters are passed to the PDF and CDF functions. For example, to calculate the PDF and CDF of the median of a standard normal distribution, you would do the following:

from pyordstat import ContinuousOrderStatistics

def normal_pdf(x, mu, sigma):
    return 1 / (2 * np.pi) ** 0.5 * np.exp(-0.5 * (x/mu) ** 2/ sigma ** 2)

def normal_cdf(x, mu, sigma):
    return 0.5 * (1 + erf((x - mu) / (2 ** 0.5 * sigma)))

order_stats = ContinuousOrderStatistics(normal_pdf, normal_cdf, mu, sigma)


pdf_4_2 = order_stats.order_statistics_pdf(4, 2)

Discrete distributions

For discrete distributions, use the class DiscreteOrderStatistics. This class takes callable PMF (Probability Mass Function) and CDF functions as its first arguments, and any successive parameters are passed to the PMF and CDF functions. It works the same as ContinuousOrderStatistics, but the _pdf in methods is replaced by _pmf (as it's more appropriate to talk about probability mass functions for discrete distributions).

Finite distributions

For finite distributions, use the class FiniteOrderStatistics. This class allows you to just pass the values of the support and their probability mass as arrays, and it will calculate the CDF for you. For example, to calculate the PDF and CDF of the median for a sample of 4 drawn from a discrete distribution with support [1, 2, 3, 4, 5] and probability mass [0.1, 0.2, 0.3, 0.2, 0.2], you would do the following:

from pyordstat import FiniteOrderStatistics

support = np.array([1, 2, 3, 4, 5])
pmf = np.array([0.1, 0.2, 0.3, 0.2, 0.2])

order_stats = FiniteOrderStatistics(support, pmf)

pdf_4_2 = order_stats.order_statistics_pdf(4, 2)

Scipy compatibility

The classes RVContOrderStatistics and RVDiscrOrderStatistics will accept an instance of a SciPy rv_continuous or rv_discrete distribution respectively. For convenience, a few common use cases of this are included in the pyordstat.functions module.

Contributing

Contributions are welcome! This project was based off the Poetry Cookiecutter template found here. Recommended steps when developing are:

• set up your poetry environment with poetry install
• set up the pre-commit hooks with pre-commit install (make sure to have Ruff, Black, and MyPy installed in the main environment)
• lint the code with poe lint
• run the tests with poe test
• rebuild the docs with poe docs

Any contributions should be sent as a PR to the develop branch. Please make sure to include tests for any new functionality, and to update the docs accordingly.

License

This project is licensed under the terms of the MIT license.