multidimensionalks

Python c extension with method for calculating multidimensional Kolmogorov-Smirnov test

multidimensionalks.test(rvs, cdf=None, counts_rvs=None, counts_cdf=None, n_jobs=1, permutation_samples=0, draw_samples=False, binomial_significance=False, use_avx=3, max_alpha_beta=True, scale_result=False, deduplicate_data=True, debug=False)

Example usage

from multidimensionalks import test
import numpy as np

test(np.array([[1, 2, 3], [1, 3, 2]]), cdf=np.array([[1,2,2]]))

Parameters

rvs: 2-dimensional numpy number array with rows representing d-dimensional observations,
cdf: 2-dimensional numpy number array with rows representing second sampled-dimensional observations,
counts_rvs: in case of rvs having multiple duplicates, an array without duplicates and a separate array of counts can be provided,
counts_cdf: in case of cdf having multiple duplicates, an array without duplicates and a separate array of counts can be provided, additionally if cdf is not given counts_cdf are taken as counts of elements of rvs array,
n_jobs: number of threads used during calculation,
binomial_significance: boolean value indicating if statistical significance should be calculated. Defaults to False,
permutation_samples: number of times data is shuffled and the statistic value is calculated to estimate pvalue,
draw_samples: boolean value indicating that samples should be drawn with replacement instead of permutating (defaults to permutating),
use_avx: integer value indicating if AVX instructions should be used during the calculations. 0 disables avx, 3 means to try the best supported set, 1 will try to use AVX512 instruction set and use no otherwise, 2 will try to use AVX2. Defaults to 3,
max_alpha_beta: boolean value indicating how λ and β values should be combined. Value True (default) results in max(λ, β). (λ+β)/2 is used otherwise.
scale_result: Whether to scale the statistic by $\sqrt{\frac{|rvs|+|cdf|}{|rvs|\times|cdf|}}$ (default False),
deduplicate_data: Whether to deduplicate data points before running the algorithms,
debug: Whether to print debug data to stdout.

Return value

If no pvalue calculation method is selected returns ks statistic value, otherwise returns a tuple:

ks statistic,
pvalue calculated using statistical method if binomial_significance is set to True,
pvalue calculated using permutation method if permutation_samples is larger than 0.

Metadata

Release files for multidimensionalks 0.2.21

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for multidimensionalks 0.2.21
File	Interpreter	ABI	Platform	Reset
multidimensionalks-0.2.21-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2.3 MB Jun 1, 2025	CPython 3.12	CPython 3.12	Linux glibc 2.17+ x86-64	Details
multidimensionalks-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2.3 MB Jun 1, 2025	CPython 3.11	CPython 3.11	Linux glibc 2.17+ x86-64	Details
multidimensionalks-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2.2 MB Jun 1, 2025	CPython 3.10	CPython 3.10	Linux glibc 2.17+ x86-64	Details
multidimensionalks-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2.2 MB Jun 1, 2025	CPython 3.9	CPython 3.9	Linux glibc 2.17+ x86-64	Details
multidimensionalks-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2.3 MB Jun 1, 2025	CPython 3.8	CPython 3.8	Linux glibc 2.17+ x86-64	Details

Total release size: 11.3 MB

Release files / multidimensionalks-0.2.21-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	multidimensionalks-0.2.21-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	2.3 MB
Tags	CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`87d0954e1bce4d670bf8e07cc4a4dcf683c1491b309c8b7e089a2ba4f9cca108`
BLAKE2b-256 checksum How to use checksums	`9eba86c9123b1fe636b210c763f2cb70ba488c79dfcaefc0aa851f2bf2b0067d`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Release files / multidimensionalks-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	multidimensionalks-0.2.21-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	2.3 MB
Tags	CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`a3b98d5088b85b9c13d9908268872912dfad2ea841feae793bac05d556d95046`
BLAKE2b-256 checksum How to use checksums	`637a1266f8a74785a24a9e5641017af4b596c1d2fbbce5ea4f5ce8f4196ea054`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Release files / multidimensionalks-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	multidimensionalks-0.2.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	2.2 MB
Tags	CPython 3.10 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`25ef49ff6423dbfe9593ab4c8d7f3464e8041496639170c4447093a5f2597a9f`
BLAKE2b-256 checksum How to use checksums	`39726bb1e67ed6c957426a460160514a09c10907e4e8f7a53caea4ca77af4eaa`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Release files / multidimensionalks-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	multidimensionalks-0.2.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	2.2 MB
Tags	CPython 3.9 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`19c2c4b3cf9906a93fa042947c9f75d152f52986634d62672741744fa220870a`
BLAKE2b-256 checksum How to use checksums	`2e1f4b81f1068849f4bd69c986144444be50911d17753162b0eb8f05ae8541a6`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Release files / multidimensionalks-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	multidimensionalks-0.2.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	2.3 MB
Tags	CPython 3.8 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`114bbeca0848cd8c67f200b72e11085abae7deaaadd4707eac96d778b9dbe06c`
BLAKE2b-256 checksum How to use checksums	`b0e1d7ed786ec37ef0fac11acebf56c68214f4d8cf67aa24111b7ac3461381db`
Upload date	Jun 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`