Kernel Density Estimation in Python.

These details have not been verified by PyPI

Project links

Repository

Project description

Want to cite KDEpy in your work? See the bottom right part of this website for citation information.

KDEpy

About

This Python 3.8+ package implements various kernel density estimators (KDE). Three algorithms are implemented through the same API: NaiveKDE, TreeKDE and FFTKDE. The class FFTKDE outperforms other popular implementations, see the comparison page. The code is stable and in widespread use by practitioners and in other packages.

Plot

The code generating the above graph is found in examples.py.

Installation

KDEpy is available through PyPI, and may be installed using pip:

pip install KDEpy

If you have trouble on Ubuntu, try running sudo apt install libpython3.X-dev, where 3.X is your Python version.

Example code and documentation

Below is an example showing an unweighted and weighted kernel density. From the code below, it should be clear how to set the kernel, bandwidth (variance of the kernel) and weights. See the documentation for more examples.

from KDEpy import FFTKDE
import matplotlib.pyplot as plt

customer_ages = [40, 56, 20, 35, 27, 24, 29, 37, 39, 46]

# Distribution of customers
x, y = FFTKDE(kernel="gaussian", bw="silverman").fit(customer_ages).evaluate()
plt.plot(x, y)

# Distribution of customer income (weight each customer by their income)
customer_income = [152, 64, 24, 140, 88, 64, 103, 148, 150, 132]

# The `bw` parameter can be manually set, e.g. `bw=5`
x, y = FFTKDE(bw="silverman").fit(customer_ages, weights=customer_income).evaluate()
plt.plot(x, y)

Plot

The package consists of three algorithms. Here's a brief explanation:

NaiveKDE - A naive computation. Supports d-dimensional data, variable bandwidth, weighted data and many kernel functions. Very slow on large data sets.
TreeKDE - A tree-based computation. Supports the same features as the naive algorithm, but is faster at the expense of small inaccuracy when using a kernel without finite support. Good for evaluation on non-uniform, arbitrary grids.
FFTKDE - A very fast convolution-based computation. Supports weighted d-dimensional data and many kernels, but not variable bandwidth. Must be evaluated on an equidistant grid, the finer the grid the higher the accuracy. Data points may not be outside of the grid.

Issues and contributing

Issues

If you are having trouble using the package, please let me know by creating an Issue on GitHub and I'll get back to you.

Contributing

Whatever your mathematical and Python background is, you are very welcome to contribute to KDEpy. To contribute, fork the project, create a branch and submit and Pull Request. Please follow these guidelines:

Import as few external dependencies as possible.
Use test driven development, have tests and docs for every method.
Cite literature and implement recent methods.
Unless it's a bottleneck computation, readability trumps speed.
Employ object orientation, but resist the temptation to implement many methods -- stick to the basics.
Follow PEP8.

Project details

These details have not been verified by PyPI

Project links

Repository

Release history Release notifications | RSS feed

This version

1.1.11

Oct 29, 2024

1.1.10

Sep 6, 2024

1.1.9

Feb 29, 2024

1.1.8

Oct 9, 2023

1.1.7

Sep 21, 2023

1.1.6

Sep 21, 2023

1.1.5

Jul 26, 2023

1.1.4

Jul 12, 2023

1.1.3

May 23, 2023

1.1.2

May 7, 2023

1.1.1

Mar 18, 2023

1.1.0

May 26, 2021

1.0.11

May 19, 2021

1.0.10

Oct 31, 2020

1.0.9

Oct 31, 2020

1.0.8

Oct 31, 2020

1.0.7

Oct 28, 2020

1.0.6

Oct 27, 2020

1.0.5

May 16, 2020

1.0.4

May 16, 2020

1.0.3

Mar 19, 2020

1.0.2

Jul 20, 2019

0.6.11

Feb 23, 2019

0.6.10

Feb 23, 2019

0.6.9

Nov 23, 2018

0.6

Sep 20, 2018

0.5.6

Aug 29, 2018

0.5.5

Aug 27, 2018

0.5.4

Aug 26, 2018

0.5.3

Aug 26, 2018

0.5.2

Aug 26, 2018

0.5.1

Aug 26, 2018

0.5

Aug 25, 2018

0.4

Aug 19, 2018

0.3

Aug 8, 2018

0.2

Jul 28, 2018

0.1

Apr 1, 2018

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kdepy-1.1.11.tar.gz (168.3 kB view details)

Uploaded Oct 29, 2024 Source

Built Distributions

KDEpy-1.1.11-cp313-cp313-win_amd64.whl (258.7 kB view details)

Uploaded Oct 29, 2024 CPython 3.13 Windows x86-64

KDEpy-1.1.11-cp313-cp313-win32.whl (246.3 kB view details)

Uploaded Oct 29, 2024 CPython 3.13 Windows x86

KDEpy-1.1.11-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (675.8 kB view details)

Uploaded Oct 29, 2024 CPython 3.13 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp313-cp313-macosx_11_0_arm64.whl (261.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.13 macOS 11.0+ ARM64

KDEpy-1.1.11-cp313-cp313-macosx_10_13_x86_64.whl (266.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.13 macOS 10.13+ x86-64

KDEpy-1.1.11-cp312-cp312-win_amd64.whl (259.0 kB view details)

Uploaded Oct 29, 2024 CPython 3.12 Windows x86-64

KDEpy-1.1.11-cp312-cp312-win32.whl (246.4 kB view details)

Uploaded Oct 29, 2024 CPython 3.12 Windows x86

KDEpy-1.1.11-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (682.2 kB view details)

Uploaded Oct 29, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp312-cp312-macosx_11_0_arm64.whl (262.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.12 macOS 11.0+ ARM64

KDEpy-1.1.11-cp312-cp312-macosx_10_13_x86_64.whl (267.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.12 macOS 10.13+ x86-64

KDEpy-1.1.11-cp311-cp311-win_amd64.whl (258.0 kB view details)

Uploaded Oct 29, 2024 CPython 3.11 Windows x86-64

KDEpy-1.1.11-cp311-cp311-win32.whl (245.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.11 Windows x86

KDEpy-1.1.11-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (687.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp311-cp311-macosx_11_0_arm64.whl (261.1 kB view details)

Uploaded Oct 29, 2024 CPython 3.11 macOS 11.0+ ARM64

KDEpy-1.1.11-cp311-cp311-macosx_10_9_x86_64.whl (265.8 kB view details)

Uploaded Oct 29, 2024 CPython 3.11 macOS 10.9+ x86-64

KDEpy-1.1.11-cp310-cp310-win_amd64.whl (257.7 kB view details)

Uploaded Oct 29, 2024 CPython 3.10 Windows x86-64

KDEpy-1.1.11-cp310-cp310-win32.whl (245.8 kB view details)

Uploaded Oct 29, 2024 CPython 3.10 Windows x86

KDEpy-1.1.11-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (652.9 kB view details)

Uploaded Oct 29, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp310-cp310-macosx_11_0_arm64.whl (261.1 kB view details)

Uploaded Oct 29, 2024 CPython 3.10 macOS 11.0+ ARM64

KDEpy-1.1.11-cp310-cp310-macosx_10_9_x86_64.whl (265.6 kB view details)

Uploaded Oct 29, 2024 CPython 3.10 macOS 10.9+ x86-64

KDEpy-1.1.11-cp39-cp39-win_amd64.whl (258.3 kB view details)

Uploaded Oct 29, 2024 CPython 3.9 Windows x86-64

KDEpy-1.1.11-cp39-cp39-win32.whl (246.3 kB view details)

Uploaded Oct 29, 2024 CPython 3.9 Windows x86

KDEpy-1.1.11-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (655.8 kB view details)

Uploaded Oct 29, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp39-cp39-macosx_11_0_arm64.whl (261.6 kB view details)

Uploaded Oct 29, 2024 CPython 3.9 macOS 11.0+ ARM64

KDEpy-1.1.11-cp39-cp39-macosx_10_9_x86_64.whl (266.2 kB view details)

Uploaded Oct 29, 2024 CPython 3.9 macOS 10.9+ x86-64

KDEpy-1.1.11-cp38-cp38-win_amd64.whl (258.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.8 Windows x86-64

KDEpy-1.1.11-cp38-cp38-win32.whl (246.5 kB view details)

Uploaded Oct 29, 2024 CPython 3.8 Windows x86

KDEpy-1.1.11-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (667.3 kB view details)

Uploaded Oct 29, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

KDEpy-1.1.11-cp38-cp38-macosx_11_0_arm64.whl (261.6 kB view details)

Uploaded Oct 29, 2024 CPython 3.8 macOS 11.0+ ARM64

KDEpy-1.1.11-cp38-cp38-macosx_10_9_x86_64.whl (266.0 kB view details)

Uploaded Oct 29, 2024 CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file kdepy-1.1.11.tar.gz.

File metadata

Download URL: kdepy-1.1.11.tar.gz
Upload date: Oct 29, 2024
Size: 168.3 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.11.10

File hashes

Hashes for kdepy-1.1.11.tar.gz
Algorithm	Hash digest
SHA256	`603cc3cd1c80bb0a7a9b0b74ebae117d312e37cee8fc1c637b46888e48d91085`
MD5	`ece48ab2ba749faa9b6194aae7c87ba9`
BLAKE2b-256	`13a89206112add5d915338e744f4be378cfab72a6e4244feb3f0eef7827ea934`

Algorithm	Hash digest
SHA256	`46548cad3742511dae94f3566b7f9a44b75b52f3081010143a85b7b857d45bc0`
MD5	`8761ac06c3f41295cda4ffe6b5217060`
BLAKE2b-256	`c032837b161b0acffefff36f8802b0b79f45d588d68a4012386867371aa22664`

Algorithm	Hash digest
SHA256	`1ae504070d555bf8477ae4f2765c63c1401862ee2be664888a6f5115dd2a4927`
MD5	`75bcdf7539b56a724fc8bccb7549f815`
BLAKE2b-256	`654983840f1772a4b8df8d69427ef08f30017b46a9915a2a3cf4be2320af0474`

Algorithm	Hash digest
SHA256	`e8973ca83ba92f3a8e068ff810d299ffa5a6d9774c606ab03a319236f2bba24d`
MD5	`47928aa951d5e36780172b5c9adc9d50`
BLAKE2b-256	`32d6b171f878ac899bf920d660930253afcb693298b55dbff027b7aacce11b28`

Algorithm	Hash digest
SHA256	`6215e0519f9660372c44ef1945980d6c025f10bdceb0954ed8cd79c0492ef52f`
MD5	`58dbbca6f6065cfb8debd2b02d8827eb`
BLAKE2b-256	`48344ff30dd74c837d6297b2cc8e1df61ed42a90299ab8392b18c99507893fce`

Algorithm	Hash digest
SHA256	`f87a71ab32f2c3472baf7e585b990cc93bc5e3e19f8054d4f900f86c2b422c20`
MD5	`8b0462fb1061e6b848af2bd2655c28ae`
BLAKE2b-256	`bc67848dd3391c07f18634c97962c2c071e3b6c0a19de52f2b94e9dbe4bf8dca`

Algorithm	Hash digest
SHA256	`b38086530f26f91a55232c23ba89a3d9816cc4160fb5b15fe586b0d7c6532486`
MD5	`da0947b2b21cd7287fcfbd4b38b43f93`
BLAKE2b-256	`f95a84068026817166cc3bacace0b2e271ada4ebfc1c495570cfdfa75740e9bc`

Algorithm	Hash digest
SHA256	`c606ae984f4daf6315d17dacc070f19cb2e2dc1847ab774ab3365e7c73feb216`
MD5	`04da054fb9d2236e4276f4b53676b60f`
BLAKE2b-256	`dababb2f22b3c62b0cd081ebe90b2f12baf40d57d0f2048974d92114c8c3e52e`

Algorithm	Hash digest
SHA256	`59d94166875d895eb18be03e09d2e62611f1c3f58de1a504c1b8d050a055c840`
MD5	`2cb6bd9ddf8032b325a8c8b65319c924`
BLAKE2b-256	`89da7082202e21b89adc5ea86156cebe8714cadde5a1cbc16032d11518e3411e`

Algorithm	Hash digest
SHA256	`58748907cb47804cfced6ad22fc9732b08995eb350555336bcd125497cde8fb1`
MD5	`d011708873cb7ee22c5d15a5d8a84588`
BLAKE2b-256	`baca5112e3ca6de36a2cd0deb8c917f8f710d017b1b956d3e9527f34dd0cc0d1`

Algorithm	Hash digest
SHA256	`6bc0e684c43b519fe9768c6bbd3a82e722234045ed7aed4343ab320d1769a98f`
MD5	`351e14c976e76a8b93bb6f47b30f3f86`
BLAKE2b-256	`42f1c5d4bf4a1114f57ae4a6fdebcff28c3140785ad48bf17a8ce81e45bd0bed`

Algorithm	Hash digest
SHA256	`a810a347a169bbfcf76d10742b61c3e7aaf32d8b616571dd9232356cf2e17960`
MD5	`c95ba3ce5d410f5952e4c23905155b03`
BLAKE2b-256	`1f0557777502c3e2a9d64e29bd3d7c2887643d9c71f49091949e379f3f8339c0`

Algorithm	Hash digest
SHA256	`e7a15de8c43f1aa677de30f20443d3de6002f5eac2bb66d018ce209a7bd202c0`
MD5	`dc38d04452fddeb273b25bbe1105ed1a`
BLAKE2b-256	`44c802912c29ba0f694f49489dae65a45d21747c5d6818383f41c3d2b9d302f0`

Algorithm	Hash digest
SHA256	`a44fef7a1338a91d86b20a6268d401b26097b9754141387d4603d5f2fb478df8`
MD5	`dd7e119791b1311f89bccb2b8c745e93`
BLAKE2b-256	`03a440a2c2986e111c8771e9be9fdd3c2b3fc2ebd49e7964e8a27649701a12e0`

Algorithm	Hash digest
SHA256	`66359d1936c05868acaff5ebdb10e9f92c789b06c5441fcc3addedbb7c483b16`
MD5	`05964a201d4ae32424bb8c82bebdd49e`
BLAKE2b-256	`1ad9b41471b67e17d90f636d98388ba4c72d03ed6cc9bf845902b85a258585d2`

Algorithm	Hash digest
SHA256	`c9a2e744c7009b3a6f92934719f9f9acd23f0e95e4702297d8dbcf6d8ce56252`
MD5	`3b24f69b90dc94d0532c13ccb83cf720`
BLAKE2b-256	`e0a256ad1e81db96be76cfc3f0c550700d20ec22b18375080c0293df029218e2`

Algorithm	Hash digest
SHA256	`f95978a0db7a71b9aab29c79d1a5d0c3b907f1388ef84659beabbcf603a43f85`
MD5	`1261ce75742d0527fe09a5a6b66a238d`
BLAKE2b-256	`45af92a26ac78d5b5db272c6efc71b27bc531861efbfc3a9c50cb73f74e48847`

Algorithm	Hash digest
SHA256	`3aa3feead617dced7973bb28c11b286c272c933011466c810913b39ee9965d67`
MD5	`cc8ba01d6f2fc253c9a695a5195fdbd6`
BLAKE2b-256	`245c62148dc26e8d0ed762d1eef1d5f5645ae3df527439a5b0b67e809331f979`

Algorithm	Hash digest
SHA256	`66ae5d37dbb947da4052b1a8edc054e24801d846b2b081f5720c314a1b594d8c`
MD5	`f158450d1d56061b4de972cac8fa1373`
BLAKE2b-256	`dc5dc23305a6bd7d4fc250acb4310e51f8e8c1d4ec16716521ca65090daf0999`

Algorithm	Hash digest
SHA256	`ab85e07fbc521630481ae59c43d9eb9ec6741b8922d36a44677b7fe117094e5c`
MD5	`abf4a6af75c2ac6b12600383a5641f6f`
BLAKE2b-256	`3b16979a20ad73b27aa66d36baaf57bbb7d878d7da6f366a9922ab0e45a64c3a`

Algorithm	Hash digest
SHA256	`9250d9b0ce1f869698e33926d102989087e0b5f894512d11ff54f9aa3daef926`
MD5	`6b1881b19ec306bd193bf5d244baba08`
BLAKE2b-256	`80330b3d30f1dcd9ac1e31b96a5b2e1ac80cd5bc5f48b4aa8d21fff3bdeb65d8`

KDEpy 1.1.11

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

KDEpy

About

Installation

Example code and documentation

Issues and contributing

Issues

Contributing

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

Algorithm	Hash digest
SHA256	`79f2038e6991986e63caa295a49e3cb0f53abfa393626c3527bd71afb1013a71`
MD5	`1ac8615fbb90bb1c4a216daf9c333059`
BLAKE2b-256	`ae0496d66e02ea66bcb345dc976f5dbe4a8fde5bfd209f15ad3dbe0fc224ffb5`

Algorithm	Hash digest
SHA256	`8e18492460625eba4f5a1cd29255f50e3ab7df71d2a48e77d8d262f5a252a1c6`
MD5	`2967c017dd78ca83ad00c3ae1fba9ac7`
BLAKE2b-256	`caff053fa0d683e1e6c257b4b9ded2246f77a748656e48ae17ed55198e1f06ff`

Algorithm	Hash digest
SHA256	`a33273e1c7d0b92d177f6d1077c1306e7a89aec6a0a6b4df27650dd4ddccf510`
MD5	`4d21f4eaa6dd897cf8040c401fc085fd`
BLAKE2b-256	`684a033988b48b8944ce4a01b0de4e16c86e20d22abd886ddef903a0a8de9fbe`

Algorithm	Hash digest
SHA256	`60563fc7a5058c5aaf597752103686f34fec44b6802e64eca6c298af64e8621a`
MD5	`e754d5ea69ccdd564667efe6ee40fa0a`
BLAKE2b-256	`bb39db123847045ae372060d30172f8e31a87963a12e49a04b1542d30eaa495e`

Algorithm	Hash digest
SHA256	`ca34d9be9961f0ec4ca77ea9c98d96c2944dfade6c8a08f5e9fa866b5130e662`
MD5	`4a00ee5692c8d0107ca7e34e12d5ec2b`
BLAKE2b-256	`3074ef8c6b34141b6f7743a6e66909ecbb1f85abe9eb984081a419ec1375a550`

Algorithm	Hash digest
SHA256	`358103378a5df1941926dcca796d3c981e84ad6ffde87116a859e785d89d4158`
MD5	`f9d116c83eb5bbf0a8927761810df779`
BLAKE2b-256	`acac72ac327a9cc326122d00f30b833bc113ff90573d95f04aefa46f19a878f8`

Algorithm	Hash digest
SHA256	`0c60e8b39f8724b6e8e6e91c73fd4327a90a594fa4d3804d7378ee2a48c402f4`
MD5	`a22877b572ba5f7c8db8de243cb94513`
BLAKE2b-256	`9ade7c326b5dce8d5046f0c58e977b2a9600c3d9543fe4e4d81314a633ad6746`

Algorithm	Hash digest
SHA256	`407dd205422ca0cf19cd4d08adaab5861105879e159c3e61dc5f28e2d0b6a132`
MD5	`b8e2089464f0ebb2678eaace76670183`
BLAKE2b-256	`627710714d2cc950815be98b2e3e78db03113bb96e33d1e384d49e75e138b435`

Algorithm	Hash digest
SHA256	`88a00a3242b7885a79285e66420561cf7655b3caa974c0137911b841e7d1277d`
MD5	`cfc39772e8643f1994a6f57e53d44420`
BLAKE2b-256	`fe086355063fbb54fe0ef11201e7df5b255486ac5f8b75d347fdc65e439118b9`

Algorithm	Hash digest
SHA256	`744231ff80d91ac3c81c1b20acf37589240207137d76d0f4f8f787edaff0d5b9`
MD5	`7488c23eddab2483566fc3f75086ff8f`
BLAKE2b-256	`0801a91d1c0f2257f750313080c17fb62c207a4449951cda0a5e614861148d48`