# LISA Glitch
LISA Glitch is a Python package that generates glitch files compatible with [LISA Instrument](https://gitlab.in2p3.fr/lisa-simulation/instrument) and [LISANode](https://gitlab.in2p3.fr/j2b.bayle/LISANode). A glitch files contain one or more signals, which are injected in the instrumental simulation at various injections points (see below).
## Contributing
### Report an issue
We use the issue-tracking management system associated with the project provided by Gitlab. If you want to report a bug or request a feature, open an issue at <https://gitlab.in2p3.fr/lisa-simulation/glitch/-/issues>. You may also thumb-up or comment on existing issues.
### Development environment
We strongly recommend to use [Python virtual environments](https://docs.python.org/3/tutorial/venv.html).
To setup the development environment, use the following commands:
```shell
git clone git@gitlab.in2p3.fr:lisa-simulation/glitch.git
cd glitch
python -m venv .
source ./bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
python -m pip install -e .
```
### Workflow
The project's development workflow is based on the issue-tracking system provided by Gitlab, as well as peer-reviewed merge requests. This ensures high-quality standards.
Issues are solved by creating branches and opening merge requests. Only the assignee of the related issue and merge request can push commits on the branch. Once all the changes have been pushed, the "draft" specifier on the merge request is removed, and the merge request is assigned to a reviewer. He can push new changes to the branch, or request changes to the original author by re-assigning the merge request to them. When the merge request is accepted, the branch is merged onto master, deleted, and the associated issue is closed.
### Pylint and unittest
We enforce [PEP 8 (Style Guide for Python Code)](https://www.python.org/dev/peps/pep-0008/) with Pylint syntax checking, and correction of the code using the [pytest](https://docs.pytest.org/) testing framework. Both are implemented in the continuous integration system.
You can run them locally
```shell
pylint lisaglitch/*.py
python -m pytest
```
## Acknowledgements
The implementation of the flow, which is used for learning and sampling from the LISA Pathfinder distribution, is heavily based on the [neural spline flows](https://arxiv.org/abs/1906.04032) implementation provided by the authors: [nsflows](https://github.com/bayesiains/nflows) and also its orignal implentation: [nsf](https://github.com/bayesiains/nsf).
With some simplifications borrowed from [here](https://github.com/karpathy/pytorch-normalizing-flows) and [here](https://github.com/tonyduan/normalizing-flows/blob/master/nf/flows.py).
## Use policy
The project is distributed under the 3-Clause BSD open-source license to foster open science in our community and share common tools. Please keep in mind that developing and maintaining such a tool takes time and effort. Therefore, we kindly ask you to
* Cite the DOI (see badge above) in any publication
* Acknowledge the authors (below)
* Acknowledge the LISA Simulation Expert Group in any publication
Do not hesitate to send an email to the authors for support. We always appreciate being associated with research projects.
## Contact
* Jean-Baptiste Bayle (<j2b.bayle@gmail.com>)
* Eleonora Castelli (<eleonora.castelli@nasa.gov>)
* Natalia Korsakova (<korsakova@apc.in2p3.fr>)
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"description": "# LISA Glitch\n\nLISA Glitch is a Python package that generates glitch files compatible with [LISA Instrument](https://gitlab.in2p3.fr/lisa-simulation/instrument) and [LISANode](https://gitlab.in2p3.fr/j2b.bayle/LISANode). A glitch files contain one or more signals, which are injected in the instrumental simulation at various injections points (see below).\n\n## Contributing\n\n### Report an issue\n\nWe use the issue-tracking management system associated with the project provided by Gitlab. If you want to report a bug or request a feature, open an issue at <https://gitlab.in2p3.fr/lisa-simulation/glitch/-/issues>. You may also thumb-up or comment on existing issues.\n\n### Development environment\n\nWe strongly recommend to use [Python virtual environments](https://docs.python.org/3/tutorial/venv.html).\n\nTo setup the development environment, use the following commands:\n\n```shell\ngit clone git@gitlab.in2p3.fr:lisa-simulation/glitch.git\ncd glitch\npython -m venv .\nsource ./bin/activate\npython -m pip install --upgrade pip\npython -m pip install -r requirements.txt\npython -m pip install -e .\n```\n\n### Workflow\n\nThe project's development workflow is based on the issue-tracking system provided by Gitlab, as well as peer-reviewed merge requests. This ensures high-quality standards.\n\nIssues are solved by creating branches and opening merge requests. Only the assignee of the related issue and merge request can push commits on the branch. Once all the changes have been pushed, the \"draft\" specifier on the merge request is removed, and the merge request is assigned to a reviewer. He can push new changes to the branch, or request changes to the original author by re-assigning the merge request to them. When the merge request is accepted, the branch is merged onto master, deleted, and the associated issue is closed.\n\n### Pylint and unittest\n\nWe enforce [PEP 8 (Style Guide for Python Code)](https://www.python.org/dev/peps/pep-0008/) with Pylint syntax checking, and correction of the code using the [pytest](https://docs.pytest.org/) testing framework. Both are implemented in the continuous integration system.\n\nYou can run them locally\n\n```shell\npylint lisaglitch/*.py\npython -m pytest\n```\n\n## Acknowledgements\n\nThe implementation of the flow, which is used for learning and sampling from the LISA Pathfinder distribution, is heavily based on the [neural spline flows](https://arxiv.org/abs/1906.04032) implementation provided by the authors: [nsflows](https://github.com/bayesiains/nflows) and also its orignal implentation: [nsf](https://github.com/bayesiains/nsf).\nWith some simplifications borrowed from [here](https://github.com/karpathy/pytorch-normalizing-flows) and [here](https://github.com/tonyduan/normalizing-flows/blob/master/nf/flows.py).\n\n## Use policy\n\nThe project is distributed under the 3-Clause BSD open-source license to foster open science in our community and share common tools. Please keep in mind that developing and maintaining such a tool takes time and effort. Therefore, we kindly ask you to\n\n* Cite the DOI (see badge above) in any publication\n* Acknowledge the authors (below)\n* Acknowledge the LISA Simulation Expert Group in any publication\n\nDo not hesitate to send an email to the authors for support. We always appreciate being associated with research projects.\n\n## Contact\n\n* Jean-Baptiste Bayle (<j2b.bayle@gmail.com>)\n* Eleonora Castelli (<eleonora.castelli@nasa.gov>)\n* Natalia Korsakova (<korsakova@apc.in2p3.fr>)\n",
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