PoissonRandom

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

https://github.com/sciml/poissonrandom.jl

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
✓
Committers with academic emails
1 of 14 committers (7.1%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (12.7%) to scientific vocabulary

Keywords

high-performance-computing julia poisson poisson-distribution poisson-processes scientific-machine-learning sciml tau-leaping

Keywords from Contributors

matrix-exponential pdes sde ode neural-ode differential-equations stochastic-differential-equations neural-differential-equations differentialequations ordinary-differential-equations

Last synced: 6 months ago · JSON representation ·

Repository

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

Basic Info

Host: GitHub
Owner: SciML
License: other
Language: Julia
Default Branch: master
Homepage: https://docs.sciml.ai/PoissonRandom/stable/
Size: 971 KB

Statistics

Stars: 18
Watchers: 6
Forks: 6
Open Issues: 2
Releases: 11

Topics

high-performance-computing julia poisson poisson-distribution poisson-processes scientific-machine-learning sciml tau-leaping

Created almost 8 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

PoissonRandom.jl: Fast Poisson Random Numbers

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Usage

```julia Pkg.add("PoissonRandom")

Simple Poisson random

pois_rand(λ)

Using another RNG

using RandomNumbers rng = Xorshifts.Xoroshiro128Plus() pois_rand(rng, λ) ```

Implementation

It mixes two methods. A simple count method and a method from a normal approximation. See this blog post for details.

Benchmark

```julia using RandomNumbers, Distributions, BenchmarkTools, StaticArrays, RecursiveArrayTools, Plots, PoissonRandom labels = ["countrand", "adrand", "pois_rand", "Distributions.jl"] rng = Xorshifts.Xoroshiro128Plus()

function ncount(rng, λ, n) tmp = 0 for i in 1:n tmp += PoissonRandom.countrand(rng, λ) end end

function npois(rng, λ, n) tmp = 0 for i in 1:n tmp += poisrand(rng, λ) end end

function nad(rng, λ, n) tmp = 0 for i in 1:n tmp += PoissonRandom.adrand(rng, λ) end end

function n_dist(λ, n) tmp = 0 for i in 1:n tmp += rand(Poisson(λ)) end end

function timeλ(rng, λ, n) t1 = @elapsed ncount(rng, λ, n) t2 = @elapsed nad(rng, λ, n) t3 = @elapsed npois(rng, λ, n) t4 = @elapsed n_dist(λ, n) @SArray [t1, t2, t3, t4] end

Compile

time_λ(rng, 5, 5000000)

Run with a bunch of λ

times = VectorOfArray([time_λ(rng, n, 5000000) for n in 1:20])' plot(times, labels = labels, lw = 3) ```

benchmark result

So this package ends up about 30% or so faster than Distributions.jl (the method at the far edge is λ-independent so that goes on forever).

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

Citation (CITATION.bib)

@article{DifferentialEquations.jl-2017,
 author = {Rackauckas, Christopher and Nie, Qing},
 doi = {10.5334/jors.151},
 journal = {The Journal of Open Research Software},
 keywords = {Applied Mathematics},
 note = {Exported from https://app.dimensions.ai on 2019/05/05},
 number = {1},
 pages = {},
 title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
 url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
 volume = {5},
 year = {2017}
}

GitHub Events

Total

Create event: 6
Release event: 2
Issues event: 2
Watch event: 2
Delete event: 2
Issue comment event: 14
Push event: 51
Pull request review event: 17
Pull request review comment event: 15
Pull request event: 7
Fork event: 1

Last Year

Create event: 6
Release event: 2
Issues event: 2
Watch event: 2
Delete event: 2
Issue comment event: 14
Push event: 51
Pull request review event: 17
Pull request review comment event: 15
Pull request event: 7
Fork event: 1

Committers

Last synced: 8 months ago

All Time

Total Commits: 80
Total Committers: 14
Avg Commits per committer: 5.714
Development Distribution Score (DDS): 0.638

Past Year

Commits: 16
Committers: 3
Avg Commits per committer: 5.333
Development Distribution Score (DDS): 0.625

Top Committers

Name	Email	Commits
Christopher Rackauckas	C**t@C**m	29
Arno Strouwen	a**n@t**e	21
dependabot[bot]	4****]	10
Anant Thazhemadam	a**m@g**m	6
David Widmann	d**b@d**e	3
Krishna Bhogaonker	c**q@g**m	2
Chris de Graaf	me@c****v	2
github-actions[bot]	4****]	1
femtocleaner[bot]	f****]	1
Yingbo Ma	m**5@g**m	1
Julia TagBot	5****t	1
Hendrik Ranocha	m**l@r**e	1
Daniel VandenHeuvel	9****H	1
Anshul Singhvi	a**7@s**u	1

Committer Domains (Top 20 + Academic)

simons-rock.edu: 1 ranocha.de: 1 cdg.dev: 1 devmotion.de: 1 telenet.be: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 6
Total pull requests: 47
Average time to close issues: about 2 months
Average time to close pull requests: 2 days
Total issue authors: 5
Total pull request authors: 16
Average comments per issue: 6.67
Average comments per pull request: 0.19
Merged pull requests: 45
Bot issues: 0
Bot pull requests: 12

Past Year

Issues: 1
Pull requests: 3
Average time to close issues: about 1 month
Average time to close pull requests: about 20 hours
Issue authors: 1
Pull request authors: 3
Average comments per issue: 17.0
Average comments per pull request: 1.0
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

ChrisRackauckas (2)
devmotion (1)
sivasathyaseeelan (1)
ArnoStrouwen (1)
JuliaTagBot (1)

Pull Request Authors

ArnoStrouwen (21)
dependabot[bot] (12)
ChrisRackauckas (4)
thazhemadam (4)
devmotion (3)
00krishna (2)
christopher-dG (2)
oscardssmith (2)
sivasathyaseeelan (2)
github-actions[bot] (1)
femtocleaner[bot] (1)
asinghvi17 (1)
ranocha (1)
YingboMa (1)
DanielVandH (1)

Top Labels

Issue Labels

Pull Request Labels

dependencies (12)

Packages

Total packages: 1
Total downloads:
- julia 4,680 total

Total dependent packages: 8
Total dependent repositories: 0
Total versions: 11

juliahub.com: PoissonRandom

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

Homepage: https://docs.sciml.ai/PoissonRandom/stable/
Documentation: https://docs.juliahub.com/General/PoissonRandom/stable/
License: MIT
Latest release: 0.4.6
published 7 months ago

Versions: 11
Dependent Packages: 8
Dependent Repositories: 0
Downloads: 4,680 Total

Rankings

Dependent packages count: 7.0%

Dependent repos count: 9.9%

Average: 16.1%

Forks count: 17.4%

Stargazers count: 29.9%

Last synced: 6 months ago

Dependencies

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PoissonRandom

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

PoissonRandom.jl: Fast Poisson Random Numbers

Tutorials and Documentation

Usage

Simple Poisson random

Using another RNG

Implementation

Benchmark

Compile

Run with a bunch of λ

Owner

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: PoissonRandom

Rankings

Dependencies