deeplenstronomy
deeplenstronomy: A dataset simulation package for strong gravitational lensing - Published in JOSS (2021)
Science Score: 95.0%
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○CITATION.cff file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 5 DOI reference(s) in README and JOSS metadata -
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Links to: arxiv.org, sciencedirect.com, joss.theoj.org -
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5 of 13 committers (38.5%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
A pipeline for versatile strong lens sample simulations
Basic Info
Statistics
- Stars: 33
- Watchers: 8
- Forks: 8
- Open Issues: 31
- Releases: 12
Topics
Metadata Files
README.md
Welcome to deeplenstronomy!
deeplenstronomy is a tool for simulating large datasets for applying deep learning to strong gravitational lensing.
It works by wrapping the functionalities of lenstronomy in a convenient yaml-style interface, allowing users to embrace the astronomer part of their brain rather than their programmer part when generating training datasets.
Installation
With conda (Recommended)
- Step 0: Set up an environment. This can be done straightforwardly with a
condainstallation:
conda create -n deeplens python=3.7 jupyter scipy pandas numpy matplotlib astropy h5py PyYAML mpmath future
conda activate deeplens
- Step 1:
pip install lenstronomy - Step 2:
pip install deeplenstronomy
With pip
- Step 1:
pip install deeplenstronomy
Getting Started and Example Notebooks
Start by reading the Getting Started Guide to familiarize yourself with the deeplenstronomy style.
After that, check out the example notebooks below:
Notebooks for deeplenstronomy Utilities
- Creating
deeplenstronomyConfiguration Files - Generating Datasets
- Visualizing
deeplenstronomyImages - Utilizing Astronomical Surveys
- Defining Your Own Probability Distributions
- Using Your Own Images as Backgrounds
- Simulating Time-Series Datasets
Notebooks for Applying deeplenstronomy to Machine Learning Analyses
Notebooks for Suggested Science Cases
API Documentation
deeplenstronomy is designed so that users only need to work with their personal configuration files and the dataset generatation and image visualization functions.
However, if you would like to view the full API documentation, you can visit the docs page.
Citation
If you use deeplenstronomy in your work, please include the following citations:
```
@article{deeplenstronomy,
doi = {10.21105/joss.02854},
url = {https://doi.org/10.21105/joss.02854},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {58},
pages = {2854},
author = {Robert Morgan and Brian Nord and Simon Birrer and Joshua Yao-Yu Lin and Jason Poh},
title = {deeplenstronomy: A dataset simulation package for strong gravitational lensing},
journal = {Journal of Open Source Software}
}
@article{lenstronomy, title = "lenstronomy: Multi-purpose gravitational lens modelling software package", journal = "Physics of the Dark Universe", volume = "22", pages = "189 - 201", year = "2018", issn = "2212-6864", doi = "10.1016/j.dark.2018.11.002", url = "http://www.sciencedirect.com/science/article/pii/S2212686418301869", author = "Simon Birrer and Adam Amara", keywords = "Gravitational lensing, Software, Image simulations" } ```
Contact
If you have any questions or run into any errors with the beta release of deeplenstronomy, please don't hesitate to reach out:
Rob Morgan
robert [dot] morgan [at] wisc.edu
You can also message me on the DES, DELVE, LSSTC, deepskies, or lenstronomers Slack workspaces
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
deeplenstronomy: A dataset simulation package for strong gravitational lensing
Authors
University of Wisconsin-Madison, Legacy Survey of Space and Time Data Science Fellowship Program
Fermi National Accelerator Laboratory, University of Chicago
Stanford University
University of Illinois Urbana-Champaign
University of Chicago
Editor
Pierre de Buyl
Tags
astronomy strong lensing simulationGitHub Events
Total
- Issues event: 2
- Watch event: 6
- Issue comment event: 1
- Fork event: 1
Last Year
- Issues event: 2
- Watch event: 6
- Issue comment event: 1
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Robert Morgan | r****n@w****u | 216 |
| Brian Nord | n****d@f****v | 49 |
| sibirrer | s****r@g****m | 41 |
| Robert Morgan | 3****0 | 31 |
| Joao Caldeira | j****a@g****m | 15 |
| Jason Poh | j****h@u****u | 13 |
| joshualin24 | j****4@g****m | 7 |
| Michael Martinez | m****z@w****u | 5 |
| Nathan Musoke | n****e@g****m | 3 |
| paxsonswierc | p****c@u****u | 2 |
| Michael Martinez | m****z@M****l | 2 |
| voetberg | m****7@g****m | 1 |
| Erik Zaborowski | e****i@E****n | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 93
- Total pull requests: 24
- Average time to close issues: 10 months
- Average time to close pull requests: about 1 month
- Total issue authors: 18
- Total pull request authors: 9
- Average comments per issue: 1.35
- Average comments per pull request: 0.75
- Merged pull requests: 20
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bnord (41)
- rmorgan10 (16)
- musoke (7)
- jsv1206 (5)
- AeRabelais (3)
- voetberg (3)
- Jasonpoh (2)
- Catarina-Alves (2)
- joaocaldeira (1)
- AleksCipri (1)
- ShuyuW12 (1)
- shreyasbapat (1)
- ShrihanSolo (1)
- jiwoncpark (1)
- nicolopinci (1)
Pull Request Authors
- rmorgan10 (6)
- jsv1206 (5)
- voetberg (4)
- musoke (4)
- paxsonswierc (2)
- egorssed (1)
- erikzaborowski (1)
- bnord (1)
- michael7198 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 68 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 22
- Total maintainers: 2
pypi.org: deeplenstronomy
wrap lenstronomy for efficient simulation generation
- Homepage: https://github.com/deepskies/deeplenstronomy
- Documentation: https://deeplenstronomy.readthedocs.io/
- License: MIT
-
Latest release: 0.0.2.3
published about 4 years ago
Rankings
Maintainers (2)
Dependencies
- astropy >=4.0.1.post1
- future >=0.18.2
- h5py >=2.10.0
- lenstronomy >=1.6.0
- matplotlib >=3.3.2
- mpmath >=1.1.0
- numpy >=1.19.1
- pandas >=1.1.2
- pyyaml >=5.3.1
- scipy >=1.5.2
- wheel >=0.22