DeepBench

DeepBench: A simulation package for physical benchmarking data - Published in JOSS (2025)

https://github.com/deepskies/deepbench

Science Score: 95.0%

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

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 4 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org, zenodo.org
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Committers with academic emails
2 of 9 committers (22.2%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

benchmark simulation

Scientific Fields

Mathematics Computer Science - 84% confidence

Last synced: 4 months ago · JSON representation

Repository

Simulation library for very simple simulations to benchmark machine learning algorithms

Basic Info

Host: GitHub
Owner: deepskies
License: apache-2.0
Language: HTML
Default Branch: main
Homepage:
Size: 5.74 MB

Statistics

Stars: 5
Watchers: 10
Forks: 2
Open Issues: 12
Releases: 4

Topics

benchmark simulation

Created over 7 years ago · Last pushed 9 months ago

Metadata Files

Readme License

What is it?

Simulation library for very simple simulations to benchmark machine learning algorithms.

Why do we need it? Why is it useful?

There are very universally recognized scientifically meaningful benchmark data sets, or methods with which to generate them.
A very simple data set will have objects, patterns, and signals that are intuitively quantifiable and will be fast to generate.
A very simple data set will be a great testing ground for new networks and for newcomers to practice with the technology.

Documentation

To build from source

pip install sphinx cd docs make html

The folder docs/_build/html will be populated with the documentation. Navigate to file:///<Path To DeepBench>/docs/_build/html/index.html in any web browser to view.

Requirements

python = ">=3.12,<4.0," numpy = ">=1.25.0" matplotlib = ">=3.7.1" scikit-image = "^0.23.0" astropy = ">=7.0.0" autograd = ">=1.5" pyyaml = ">=6.0" h5py = ">=3.9.0"

Install

From PyPi

pip install deepbench

From Source

git clone https://github.com/deepskies/DeepBench.git pip install poetry poetry install poetry run pytest --cov

General Features

very fast to generate
Mimics in a very basic / toy way what is in astro images
Be fully controllable parametrically

DeepBench Logo

Included Simulations

Astronomy Objects - simple astronomical object simulation
Galaxy, Spiral Galaxy, Star
Shapes - simple 2D geometric shapes
Rectangle, Regular Polygon, Arc, Line, Ellipse
Physics Objects - simple physics simulations
Newtonian Pendulum, Hamiltonian Pendulum

Example

Standalone

Produce 3 instance of a pendulum over 10 different times with some level of noise. ``` import numpy as np from deepbench.collection import Collection

configuration = { "objecttype": "physics", "objectname": "Pendulum", "totalruns": 3, "parameternoise": 0.2, "imageparameters": { "pendulumarmlength": 2, "startingangleradians": 0.25, "accelerationduetogravity": 9.8, "noisestdpercent":{ "accelerationduetogravity": 0 } }, "objectparameters":{ "time": np.linspace(0, 1, 10) } }

phy_objects = Collection(configuration)

phy_objects()

objects = phyobjects.objects parameters = phyobjects.object_params ```

Produce a noisy shape image with a rectangle and an arc

``` import numpy as np from deepbench.collection import Collection

configuration = { "objecttype": "shape", "objectname": "ShapeImage", "totalruns": 1, "imageparameters": { "imageshape": (28, 28), "objectnoiselevel": 0.6 }, "objectparameters": {

    "rectangle":{
        "object": {
            "width": np.random.default_rng().integers(2, 28),
            "height": np.random.default_rng().integers(2, 28),
            "fill": True
        },
        "instance": {}
    },
    "arc":{
        "object": {
            "radius": np.random.default_rng().integers(2, 28),
            "theta1":np.random.default_rng().integers(0, 20),
            "theta2":np.random.default_rng().integers(21, 180)
        },
        "instance":{}
    }
}

}

shapeimage = Collection(configuration) shapeimage()

objects = shapeimage.objects parameters = shapeimage.object_params ```

Fine-Grained Control

Make a whole bunch of stars ``` from deepbench.astro_object import StarObject import numpy as np

star = StarObject( imagedimensions = (28,28), noiselevel = 0.3, radius= 0.8, amplitude = 1.0 )

generatedstars = [] xposition, yposition = np.random.defaultrng().uniform(low=1, high=27, size=(2, 50)) for xpos, ypos in zip(xposition, yposition): starobject = star.createobject(xpos, ypos) generatedstars.append(starobject) ```

Contributions

Original Team

Craig Brechmos
Renee Hlozek
Brian Nord

Refactor and Deployment

Ashia Livaudais
M. Voetberg

Pendulum Team

Becky Nevin
Omari Paul

Contributing

Please view the deepskies contribution guidelines before submitting a code addition

Acknowledgement

This work was produced by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy. Publisher acknowledges the U.S. Government license to provide public access under the DOE Public Access Plan DOE Public Access Plan. Neither the United States nor the United States Department of Energy, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

We acknowledge the Deep Skies Lab as a community of multi-domain experts and collaborators who’ve facilitated an environment of open discussion, idea-generation, and collaboration. This community was important for the development of this project.

Owner

Name: Deep Skies Lab
Login: deepskies
Kind: organization
Email: deepskieslab@gmail.com

Website: www.deepskieslab.com
Twitter: deepskieslab
Repositories: 5
Profile: https://github.com/deepskies

Building community and making discoveries since 2017

JOSS Publication

DeepBench: A simulation package for physical benchmarking data

Published

February 11, 2025

DOI

10.21105/joss.06774

Volume 10, Issue 106, Page 6774

Authors

M. Voetberg

Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510

Ashia Livaudais

Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510

Becky Nevin

Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510

Omari Paul

Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510

Brian Nord

Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, Department of Astronomy and Astrophysics, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, Kavli Institute for Cosmological Physics, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637

Editor

Paul La Plante

GitHub Events

Total

Issues event: 6
Watch event: 2
Issue comment event: 5
Push event: 4
Pull request review event: 1
Pull request event: 7
Fork event: 1
Create event: 1

Last Year

Issues event: 6
Watch event: 2
Issue comment event: 5
Push event: 4
Pull request review event: 1
Pull request event: 7
Fork event: 1
Create event: 1

Committers

Last synced: 5 months ago

All Time

Total Commits: 365
Total Committers: 9
Avg Commits per committer: 40.556
Development Distribution Score (DDS): 0.666

Past Year

Commits: 7
Committers: 2
Avg Commits per committer: 3.5
Development Distribution Score (DDS): 0.143

Top Committers

Name	Email	Commits
voetberg	m**7@g**m	122
beckynevin	b**n@g**m	81
Ashia Lewis	6****s	78
Brian Nord	1****d	56
Omari Paul	o**3@g**m	20
Tristan Schefke	t**3@g**m	3
Kevin Pedro	k**8@g**m	3
anandijain	a**j@u**u	1
Paul La Plante	p**t@s**u	1

Committer Domains (Top 20 + Academic)

sas.upenn.edu: 1 uchicago.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 58
Total pull requests: 48
Average time to close issues: about 1 month
Average time to close pull requests: 14 days
Total issue authors: 7
Total pull request authors: 5
Average comments per issue: 1.88
Average comments per pull request: 0.4
Merged pull requests: 45
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 7
Pull requests: 9
Average time to close issues: about 2 months
Average time to close pull requests: about 2 months
Issue authors: 2
Pull request authors: 2
Average comments per issue: 0.71
Average comments per pull request: 0.89
Merged pull requests: 7
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

voetberg (19)
beckynevin (15)
bnord (12)
omaripaul57 (4)
jwuphysics (2)
paxsonswierc (1)

Pull Request Authors

voetberg (34)
beckynevin (7)
omaripaul57 (6)
plaplant (2)
bnord (2)

Top Labels

Issue Labels

enhancement (21) bug (13) joss paper (7) documentation (6) help wanted (6) high priority (4) priority (3) question (3) duplicate (3) wishlist (3) low priority (2) good first issue (1) wontfix (1) invalid (1) tests (1)

Pull Request Labels

enhancement (1)

Packages

Total packages: 1
Total downloads:
- pypi 34 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 6
Total maintainers: 2

pypi.org: deepbench

Physics Benchmark Dataset Generator

Homepage: https://github.com/deepskies/DeepBench
Documentation: https://deepbench.readthedocs.io/en/latest/
License: Apache 2.0
Latest release: 0.2.3
published over 1 year ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 34 Last month

Rankings

Dependent packages count: 10.1%

Average: 19.9%

Dependent repos count: 21.6%

Downloads: 28.0%

Maintainers (2)

AeRabelais voetberg

Last synced: 4 months ago

Dependencies

.github/workflows/build-bench.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/make_pdf.yml actions

actions/checkout v3 composite
actions/upload-artifact v1 composite
openjournals/openjournals-draft-action master composite

.github/workflows/publish.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/test-bench.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

poetry.lock pypi

alabaster 0.7.13 develop
babel 2.12.1 develop
certifi 2023.5.7 develop
cfgv 3.3.1 develop
charset-normalizer 3.1.0 develop
colorama 0.4.6 develop
coverage 7.2.7 develop
distlib 0.3.6 develop
docutils 0.20.1 develop
exceptiongroup 1.1.1 develop
filelock 3.12.2 develop
identify 2.5.24 develop
idna 3.4 develop
imagesize 1.4.1 develop
importlib-metadata 6.7.0 develop
iniconfig 2.0.0 develop
jinja2 3.1.2 develop
markupsafe 2.1.3 develop
nodeenv 1.8.0 develop
platformdirs 3.8.0 develop
pluggy 1.2.0 develop
pre-commit 3.3.3 develop
pygments 2.15.1 develop
pytest 7.4.0 develop
pytest-cov 4.1.0 develop
pytz 2023.3 develop
requests 2.31.0 develop
setuptools 68.0.0 develop
snowballstemmer 2.2.0 develop
sphinx 7.0.1 develop
sphinx-autodoc-typehints 1.23.2 develop
sphinxcontrib-applehelp 1.0.4 develop
sphinxcontrib-devhelp 1.0.2 develop
sphinxcontrib-htmlhelp 2.0.1 develop
sphinxcontrib-jsmath 1.0.1 develop
sphinxcontrib-qthelp 1.0.3 develop
sphinxcontrib-serializinghtml 1.1.5 develop
tomli 2.0.1 develop
urllib3 2.0.3 develop
virtualenv 20.23.1 develop
astropy 5.2.2
autograd 1.6.2
contourpy 1.1.0
cycler 0.11.0
fonttools 4.40.0
future 0.18.3
h5py 3.9.0
imageio 2.31.1
importlib-resources 5.12.0
kiwisolver 1.4.4
lazy-loader 0.2
matplotlib 3.7.1
networkx 3.1
numpy 1.24.4
packaging 23.1
pillow 9.5.0
pyerfa 2.0.0.3
pyparsing 3.1.0
python-dateutil 2.8.2
pywavelets 1.4.1
pyyaml 6.0
scikit-image 0.20.0
scipy 1.9.3
scipy 1.9.1
six 1.16.0
tifffile 2023.4.12
zipp 3.15.0

pyproject.toml pypi

pre-commit ^3.2.2 develop
pytest ^7.3.1 develop
pytest-cov ^4.0.0 develop
sphinx ^7.0.1 develop
sphinx-autodoc-typehints ^1.23.2 develop
astropy ^5.2.2
autograd ^1.5
h5py >=3.9.0
matplotlib ^3.7.1
numpy ^1.24.3
python >=3.8,<4.0,
pyyaml ^6.0
scikit-image ^0.20.0

DeepBench

Science Score: 95.0%

Keywords

Scientific Fields

Basic Info

Statistics

Topics

Metadata Files

What is it?

Why do we need it? Why is it useful?

Documentation

To build from source

Requirements

Install

From PyPi

From Source

General Features

Included Simulations

Example

Standalone

Fine-Grained Control

Contributions

Original Team

Refactor and Deployment

Pendulum Team

Contributing

Acknowledgement

JOSS Publication

DeepBench: A simulation package for physical benchmarking data

Authors

Editor

Tags

GitHub Events

Total

Last Year

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: deepbench

Rankings

Maintainers (2)

Dependencies