| Name | sagenetgw JSON |
| Version |
0.1.11
JSON |
| download |
| home_page | None |
| Summary | A Python package for gravitational wave analysis using neural networks |
| upload_time | 2025-07-12 12:38:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Y Luo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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SOFTWARE. |
| keywords |
gravitational waves
machine learning
neural networks
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
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# SageNetGW
## Overview
SageNet+ is an advanced Python package for emulating the stochastic
gravitational wave background (SGWB) spectra from inflation, extending
the SageNet framework described in Zhang et al. (2025). It leverages
deep learning models (LSTM, Transformer, CosmicNet2, or RNN)
and numerical solvers from stiffGWpy to predict the energy density spectrum
with high accuracy and computational efficiency.
SageNet+ supports a wide range of cosmological parameters and achieves a
~10,000-fold speedup over traditional numerical methods.
For more details, see https://github.com/YifangLuo/SageNet
and https://github.com/bohuarolandli/stiffGWpy
## Installation
SageNet+ is available on PyPI and can be installed using pip:
```bash
pip install sagenetgw
```
### Dependencies
- Python 3.8+
- PyTorch (>=2.0.0)
- NumPy (>=1.20.0)
- scikit-learn (>=1.0.0)
## Quick Start
Below is a simple example to predict an SGWB spectrum using SageNet+:
```python
from sagenetgw.classes import GWPredictor
import numpy as np
from matplotlib import pyplot as plt
predictor = GWPredictor(
model_type='Transformer',
device="cpu"
)
prediction = predictor.predict({
"r":3.9585109e-05,
"n_t":1.0116972,
"kappa10":110.42477,
"T_re":0.17453859,
"DN_re":39.366618,
"Omega_bh2":0.0223828,
"Omega_ch2":0.1201075,
"H0":67.32117,
"A_s":2.100549e-9
})
pred_coords = np.column_stack((prediction['f'], prediction['log10OmegaGW']))
plt.plot(pred_coords[:, 0], pred_coords[:, 1], '--', color="royalblue", marker='.')
```
Ensure CUDA is installed if using GPU acceleration (by `device='cuda'`).
## Parameter Ranges
The following cosmological parameters are supported:
| Parameter | Range | Scale |
|-----------|----------------------------------|-------------|
| r | [1e-40, 1] | Logarithmic |
| n_t | [-1, 6] | Linear |
| kappa10 | [1e-7, 1e3] | Logarithmic |
| T_re | [1e-3, 1e7] GeV | Logarithmic |
| DN_re | [0, 40] | Linear |
| Omega_bh2 | [0.005, 0.1] | Linear |
| Omega_ch2 | [0.001, 0.99] | Linear |
| H0 | [20, 100] km/s/Mpc | Linear |
| A_s | [exp(1.61)/1e10, exp(3.91)/1e10] | Linear |
## Citation
If you use SageNet+ in your research, please cite:
> Zhang, F., Luo, Y., Li, B., et al. (2025). SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational
> Waves from Inflation. arXiv:2504.04054.
## License
SageNet+ is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
Raw data
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"description": "# SageNetGW\n\n\n## Overview\n\nSageNet+ is an advanced Python package for emulating the stochastic\ngravitational wave background (SGWB) spectra from inflation, extending\nthe SageNet framework described in Zhang et al. (2025). It leverages\ndeep learning models (LSTM, Transformer, CosmicNet2, or RNN)\nand numerical solvers from stiffGWpy to predict the energy density spectrum\nwith high accuracy and computational efficiency.\nSageNet+ supports a wide range of cosmological parameters and achieves a\n~10,000-fold speedup over traditional numerical methods.\n\nFor more details, see https://github.com/YifangLuo/SageNet \nand https://github.com/bohuarolandli/stiffGWpy\n\n## Installation\n\nSageNet+ is available on PyPI and can be installed using pip:\n\n```bash\npip install sagenetgw\n```\n\n### Dependencies\n\n- Python 3.8+\n- PyTorch (>=2.0.0)\n- NumPy (>=1.20.0)\n- scikit-learn (>=1.0.0)\n\n## Quick Start\n\nBelow is a simple example to predict an SGWB spectrum using SageNet+:\n\n```python\nfrom sagenetgw.classes import GWPredictor\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\npredictor = GWPredictor(\n model_type='Transformer',\n device=\"cpu\"\n )\n\nprediction = predictor.predict({\n \"r\":3.9585109e-05, \n \"n_t\":1.0116972, \n \"kappa10\":110.42477, \n \"T_re\":0.17453859, \n \"DN_re\":39.366618,\n \"Omega_bh2\":0.0223828, \n \"Omega_ch2\":0.1201075, \n \"H0\":67.32117, \n \"A_s\":2.100549e-9\n})\npred_coords = np.column_stack((prediction['f'], prediction['log10OmegaGW']))\nplt.plot(pred_coords[:, 0], pred_coords[:, 1], '--', color=\"royalblue\", marker='.')\n```\n\nEnsure CUDA is installed if using GPU acceleration (by `device='cuda'`).\n\n\n## Parameter Ranges\n\nThe following cosmological parameters are supported:\n\n| Parameter | Range | Scale |\n|-----------|----------------------------------|-------------|\n| r | [1e-40, 1] | Logarithmic |\n| n_t | [-1, 6] | Linear |\n| kappa10 | [1e-7, 1e3] | Logarithmic |\n| T_re | [1e-3, 1e7] GeV | Logarithmic |\n| DN_re | [0, 40] | Linear |\n| Omega_bh2 | [0.005, 0.1] | Linear |\n| Omega_ch2 | [0.001, 0.99] | Linear |\n| H0 | [20, 100] km/s/Mpc | Linear |\n| A_s | [exp(1.61)/1e10, exp(3.91)/1e10] | Linear |\n\n## Citation\n\nIf you use SageNet+ in your research, please cite:\n\n> Zhang, F., Luo, Y., Li, B., et al. (2025). SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational\n> Waves from Inflation. arXiv:2504.04054.\n\n## License\n\nSageNet+ is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.",
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"license": "MIT License\n \n Copyright (c) 2025 Y Luo\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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