power-spectrum-classification
Classification of source power spectra.
https://github.com/emmatassone/power-spectrum-classification
Science Score: 44.0%
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✓CITATION.cff file
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✓codemeta.json file
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○Scientific vocabulary similarity
Low similarity (12.6%) to scientific vocabulary
Repository
Classification of source power spectra.
Basic Info
- Host: GitHub
- Owner: Emmatassone
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 3.42 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Power-spectrum-classificator
Machine Learning techniques that classificate Black hole or Neutron star power spectra.
Report Bug
Table of Contents
About The Project
power-spectrum-classification is a project focused on automating the classification of power spectra sources.
Built With
The project is built with python, with the help of the following libraries.
Train your own model
To classificate a new source or observation, the observation file (.asc) should be placed in data directory. The file should indicate the binning after the file extension (e.g., .asc_100 for rebin=100).
Train the Random Forest Model
To train the Random Forest Model write in the terminal
py
python PSC.py --RF --n_estimators=N_ESTIMATORS min_samples_leaf=MIN_SAMPLES_LEAF min_samples_split=MIN_SAMPLES_SPLIT
where the optional arguments nestimators, minsamplesleaf and minsamples_split are by default 200,20 and 50 respectively.
Train the Long Short Term Memory Model
To train the Long Short Term Memory model write in the terminal
py
python PSC.py --RNN --batch_size=BATCH_SIZE --epochs=EPOCHS
Train the Convolutional Neural Network Model
Similary, to traub the CNN model
py
python PSC.py --CNN --batch_size=BATCH_SIZE --epochs=EPOCHS
There are also intrinsic parameter of the models that have to be modified specifically in the function that builds the specific model. These function can be found at models/models.py.
Load a trained model
Roadmap
- [ ] Add Sources
- [ ] Black Holes
- [ ] Neutron Stars
- [ ] Galaxies
- [ ] Add Neural Network
Contributing
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
Distributed under the GNU GPLv3 License. See LICENSE for more information.
Contact
Emmanuel A. Tassone - @EmmanuelTassone - emmanueltassone@gmail.com
Project Link: https://github.com/Emmatassone/power-spectrum-classification
Acknowledgments
A special thanks should be done to the following open-source projects.
Owner
- Name: Emma
- Login: Emmatassone
- Kind: user
- Location: Córdoba, Argentina
- Company: Universidad Nacional de Cordoba
- Repositories: 2
- Profile: https://github.com/Emmatassone
PhD physics. I'm currently researching on general relativity.
Citation (citation.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
preferred-citation:
type: article
authors:
- family-names: "Mancuso"
given-names: "G.C."
- family-names: "Tassone"
given-names: "E.A."
title: "Machine Learning as a Complementary Tool for Low-Mass X-Ray Binaries Classification"
version: "1.0.0"
date-released: "2024-08-10"
url: "https://github.com/Emmatassone/power-spectrum-classification"
license: "MIT"
GitHub Events
Total
- Push event: 3
Last Year
- Push event: 3