sepia
Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.
Science Score: 85.0%
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 -
✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
2 of 6 committers (33.3%) from academic institutions -
✓Institutional organization owner
Organization lanl has institutional domain (www.lanl.gov) -
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (21.6%) to scientific vocabulary
Repository
Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.
Basic Info
Statistics
- Stars: 35
- Watchers: 8
- Forks: 7
- Open Issues: 22
- Releases: 2
Metadata Files
README.md
SEPIA
Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning. This is a Python adaptation of GPMSA.

What to Expect
SEPIA is intended to be a tool that enhances the collaboration between statisticians and domain scientists who are using computational models to augment observations in R&D and engineering applications. The code and the methodology it implements can be demonstrated simply, but new R&D often raises issues in analysis that are subtle and complicated. SEPIA has many options to address issues that have come up in the development team's experience in scientific applications, and it is available to be extended to address new application requirements. We recommend the domain scientist consult or partner with a statistician familiar with the methodology to ensure best outcomes.
Documentation
Current documentation is at Read the Docs. The documentation contains a workflow guide that is helpful for new users to read, and also contains a quick reference for basic commands as well as an API.
Examples
Basic usage is demonstrated in the Examples directory. After looking at the documentation, check out the examples.
Install package
For cleaner package management and to avoid conflicts between different versions of packages, we recommend installing inside an Anaconda or pip environment. However, this is not required.
First, pull down the current source code from either by downloading a zip file or using git clone.
From the command line, while in the main SEPIA directory, use the following command to install sepia::
pip install -e .[sepia]
The -e flag signals developer mode, meaning that if you update the code from Github, your installation will automatically
take those changes into account without requiring re-installation. Note: this command may not work properly with all shells; it has been tested with bash.
Some other essential packages used in SEPIA may be installed if they do not exist in your system or environment.
If you encounter problems with the above install method, you may try to install dependencies manually before installing SEPIA.
First, ensure you have a recent version of Python (greater than 3.5).
Then, install packages numpy, scipy, pandas, matplotlib, seaborn, statsmodels, and tqdm.
Citing Sepia
Using Sepia in your work? Cite as:
James Gattiker, Natalie Klein, Earl Lawrence, & Grant Hutchings. lanl/SEPIA. Zenodo. https://doi.org/10.5281/zenodo.4048801
Approved by LANL/NNSA for software release: C19159 SEPIA
© 2020. 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.
This program is open source under the BSD-3 License. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Owner
- Name: Los Alamos National Laboratory
- Login: lanl
- Kind: organization
- Email: github-register@lanl.gov
- Location: Los Alamos, New Mexico, USA
- Website: https://www.lanl.gov/
- Repositories: 224
- Profile: https://github.com/lanl
Citation (CITATION.cff)
cff-version: 1.0.0 message: "If you use this software, please cite it as below." authors: - family-names: "Gattiker" given-names: "James" - family-names: "Lawrence" given-names: "Earl" - family-names: "Klein" given-names: "Natalie" - family-names: "Hutchings" given-names: "Grant" - family-names: "Lui" given-names: "Arthur" title: "SEPIA: Simulation-Enabled Prediction, Inference, and Analysis" version: 1.1.0 doi: 10.5281/zenodo.4048801 date-released: 2020-09-24 url: "https://github.com/lanl/sepia"
GitHub Events
Total
- Issues event: 1
- Watch event: 1
Last Year
- Issues event: 1
- Watch event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Natalie Elizabeth Klein | n****n@l****v | 317 |
| gatt | g****t@l****v | 132 |
| Grant Hutchings | g****s@G****l | 118 |
| granthutchings | 4****s | 38 |
| J Gattiker | j****r@g****m | 9 |
| Arthur Lui | l****r@g****m | 7 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 80
- Total pull requests: 12
- Average time to close issues: about 1 month
- Average time to close pull requests: 3 days
- Total issue authors: 8
- Total pull request authors: 4
- Average comments per issue: 0.89
- Average comments per pull request: 1.0
- Merged pull requests: 12
- 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: 1
- 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
- jgattiker (20)
- natalieklein229 (13)
- luiarthur (5)
- granthutchings (5)
- natalieklein (1)
- thaonguyen-lanl (1)
- teja781 (1)
Pull Request Authors
- luiarthur (3)
- jgattiker (1)
- natalieklein229 (1)
- granthutchings (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- jupyter 1.0.0.*
- matplotlib 3.2.1.*
- nbformat 5.0.7.*
- numpy 1.16.4.*
- pip 20.1.1.*
- pyparsing 2.4.7.*
- python 3.7.0.*
- scipy 1.2.1.*
- seaborn 0.10.1.*
- setuptools 47.3.1.*
- statsmodels 0.11.1.*
- tqdm 4.46.1.*
- matplotlib *
- numpy *
- pandas *
- scipy *
- seaborn *
- statsmodels *
- tqdm *