Sapsan
Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning - Published in JOSS (2021)
Science Score: 95.0%
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Found 4 DOI reference(s) in README and JOSS metadata -
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
ML-based turbulence modeling for astrophysics
Basic Info
- Host: GitHub
- Owner: pikarpov-LANL
- License: other
- Language: Python
- Default Branch: master
- Homepage: https://sapsan-wiki.github.io
- Size: 77.6 MB
Statistics
- Stars: 14
- Watchers: 4
- Forks: 4
- Open Issues: 2
- Releases: 40
Topics
Metadata Files
README.md
Sapsan 
Sapsan is a pipeline for Machine Learning (ML) based turbulence modeling. While turbulence is important in a wide range of mediums, the pipeline primarily focuses on astrophysical applications. With Sapsan, one can create their own custom models or use either conventional or physics-informed ML approaches for turbulence modeling included with the pipeline (estimators). Sapsan is designed to take out all the hard work from data preparation and analysis, leaving you focused on ML model design, layer by layer.
Feel free to check out a website version at sapsan.app. The interface is identical to the GUI of the local version of Sapsan, except lacking the ability to edit the model code on the fly.
Documentation
Please refer to Sapsan's Wiki for detailed installation, tutorials, troubleshooting, and API, as well as to learn more about the framework's capabilities.
Quick Start
1. Install PyTorch (prerequisite)
Sapsan can be run on both CPU and GPU. Please follow the instructions on PyTorch to install the latest version (torch>=1.7.1 & CUDA>=11.0).
2. Install via pip (recommended)
pip install sapsan
OR Clone from git
git clone https://github.com/pikarpov-LANL/Sapsan.git
cd Sapsan/
python setup.py install
Note: see Installation Page on the Wiki for complete instructions with Graphviz and Docker installation.
3. Test Installation
To make sure everything is alright, run a test of your setup:
sapsan test
4. Run Examples
To get started and familiarize yourself with the interface, feel free to run the included examples (CNN, PIMLTurb, PICAE or on 3D data, and KRR on 2D data). To copy the examples, type:
sapsan get_examples
This will create a folder ./sapsan_examples with appropriate example jupyter notebooks.
5. Create Custom Projects!
To start a custom project, designing your own custom estimator, i.e., network, go ahead and run:
sapsan create {name}
where {name} should be replaced with your custom project name. As a result, a pre-filled template for the estimator, a jupyter notebook to run everything from, and Docker will be initialized.
Sapsan has a BSD-style license, as found in the LICENSE file.
© (or copyright) 2019. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
Owner
- Name: Platon I. Karpov
- Login: pikarpov-LANL
- Kind: user
- Company: Los Alamos National Laboratory
- Website: https://people.ucsc.edu/~plkarpov/
- Repositories: 2
- Profile: https://github.com/pikarpov-LANL
For my Ph.D. thesis, I am working on modeling turbulence with ML in core-collapse supernovae.
JOSS Publication
Sapsan: Framework for Supernovae Turbulence Modeling with Machine Learning
Authors
Department of Astronomy & Astrophysics, University of California, Santa Cruz, CA, Los Alamos National Laboratory, Los Alamos, NM
Provectus IT Inc., Palo Alto, CA
Los Alamos National Laboratory, Los Alamos, NM
Los Alamos National Laboratory, Los Alamos, NM
Editor
Dan Foreman-Mackey
Tags
machine learning astronomy supernovae turbulenceGitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Platon I Karpov | 5****L | 268 |
| pikarpov | p****v@u****u | 126 |
| Iskandar Sitdikov | I****3 | 30 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 10
- Total pull requests: 95
- Average time to close issues: over 1 year
- Average time to close pull requests: 5 days
- Total issue authors: 6
- Total pull request authors: 6
- Average comments per issue: 1.6
- Average comments per pull request: 0.05
- Merged pull requests: 82
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- pikarpov-LANL (5)
- MilesCranmer (1)
- IceKhan13 (1)
- kburns (1)
- Agnes-U (1)
- joanna-pk (1)
Pull Request Authors
- pikarpov-LANL (85)
- pikarpov (5)
- dfm (2)
- MilesCranmer (1)
- dependabot[bot] (1)
- IceKhan13 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 90 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 38
- Total maintainers: 1
pypi.org: sapsan
Sapsan project
- Homepage: https://github.com/pikarpov-LANL/Sapsan
- Documentation: https://sapsan.readthedocs.io/
- License: BSD
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Latest release: 0.6.5
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- Click >=7.1.2
- Pillow >=8.1.0
- catalyst >=21.5,<=21.12
- graphviz >=0.14
- h5py >=2.10.0
- jupyter >=1.0.0
- jupytext >=1.11
- matplotlib >=3.3.2
- mlflow >=1.20.1
- notebook >=6.4.3
- numpy >=v1.21.0
- opencv-python >=4.5.4
- pandas >=1.1.0
- plotly >=5.2.0
- protobuf ==3.20.
- pytest >=6.2
- safitty >=1.3
- scikit-image >=0.19.3
- scikit-learn >=1.0.2
- scipy >=1.7.3
- six >=1.15.0
- streamlit ==0.84.2
- tornado >=6.1.0
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