| Name | s3fit JSON |
| Version |
2.0.4
JSON |
| download |
| home_page | None |
| Summary | S3Fit: a Simultaneous Spectrum and photometric-SED Fitting code for observation of galaxies |
| upload_time | 2025-02-10 05:50:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
sed fitting
astronomy
galaxy
spectral fitting
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# S<sup>3</sup>Fit
**S<sup>3</sup>Fit**: a <ins>**S**</ins>imultaneous <ins>**S**</ins>pectrum and photometric-<ins>**S**</ins>ED <ins>**Fit**</ins>ting code for observation of galaxies
S<sup>3</sup>Fit is a Python-based tool for analyzing observational data of galaxies.
It offers powerful capabilities for decomposing spectroscopic data
by supporting multiple continuum and emission line models with multiple components,
making it well-suited for complex systems with mixed contributions
from Active Galactic Nuclei (AGNs) and their host galaxies.
By simultaneously fitting the spectrum and multi-band photometric Spectral Energy Distribution (SED),
S<sup>3</sup>Fit improves constraints on continuum model properties,
which may be poorly determined when fitting spectral data alone due to its limited wavelength coverage.
With an optimized fitting strategy,
S<sup>3</sup>Fit efficiently derives the best-fit solution for dozens of model parameters.
Additionally, it provides an extensible and user-friendly framework,
allowing users to modify model configurations and incorporate new model components as needed.
## Features of S<sup>3</sup>Fit
- Easy switch between pure spectral fitting and joint spectrum+SED fitting modes.
- Support for flexible combination of multiple stellar populations with different star formation histories (SFH).
- Support for flexible combination of multiple emission line components.
- Support for AGN continuum models across UV/optical and IR wavelength ranges.
- User-friendly functions for outputting and visualizing fitting results.
- Highly extensible framework, allowing users to add new features such as new SFH functions, emission lines, and custom model types.
## Fitting strategy
The full fitting pipeline of S<sup>3</sup>Fit is shown in the following flowchart,
with a detailed description of the [fitting strategy](https://github.com/xychcz/S3Fit/blob/main/manuals/fitting_strategy.md) in
[manuals](https://github.com/xychcz/S3Fit/blob/main/manuals/).
<p align="center"> <img src="https://github.com/user-attachments/assets/658a29b3-a2d8-470a-a08f-e4a045ef3ec3" width="1200">
An example of the fitting result of S<sup>3</sup>Fit is shown in the following plots.
<p align="center"> <img src="https://github.com/user-attachments/assets/683f5837-d364-4a53-8113-a05d56f9ef5b" width="600">
## Usage
Please find guides in [manuals](https://github.com/xychcz/S3Fit/blob/main/manuals/)
for [basic](https://github.com/xychcz/S3Fit/blob/main/manuals/basic_usage.md)
and [advanced](https://github.com/xychcz/S3Fit/blob/main/manuals/advanced_usage.md) usages of this code.
An example of the usage of S<sup>3</sup>Fit is provided in the
[example](https://github.com/xychcz/S3Fit/blob/main/example/example.ipynb).
## Installation
You can install S<sup>3</sup>Fit with [pip](https://pypi.org/project/s3fit/):
```
pip install s3fit
```
S<sup>3</sup>Fit mainly depends on several most widely utilized repositories for science calculation, `scipy`, `numpy`, and `astropy`.
The core requirement of S<sup>3</sup>Fit is the two functions `least_squares` and `lsq_linear` in `scipy.optimize`
(please read the
[fitting strategy](https://github.com/xychcz/S3Fit/blob/main/manuals/fitting_strategy.md) for details).
A strong dependency of S<sup>3</sup>Fit on these repositories is not expected.
It is optional to run S<sup>3</sup>Fit with [PyNeb](http://research.iac.es/proyecto/PyNeb/),
which is used to calculate intrinsic flux ratios of emission lines:
```
pip install s3fit[pyneb]
```
The test environment is list as follows.
```
python = 3.10
scipy = 1.12.0
numpy = 1.26.4
astropy = 6.0.0
matplotlib = 3.9.1
pyneb = 1.1.23 (optional)
```
## Future updating
- Add ISM dust and synchrotron models (you may also add them or other models by yourself following the
[advanced usage](https://github.com/xychcz/S3Fit/blob/main/manuals/advanced_usage.md) manual).
- Add iron pseudo continuum templates and Balmer continuum of type-1 AGN.
- Test support for absorption lines.
## Citation
If you would like to use S<sup>3</sup>Fit, please cite the paper [Chen et al. (2025)][1], in which a pure-spectral-fit mode of this code is firstly utilized.
Please find details of the configuration of star formation history and kinematic parameters of emission lines in the paper.
## Reference
S<sup>3</sup>Fit uses the Single Stellar Population (SSP) library [HR-pyPopStar][2] ([paper][3]).
Please download the [HR-pyPopStar library][2] and run the
[converting code](https://github.com/xychcz/S3Fit/blob/main/model_libraries/convert_popstar_ssp.py)
to create the SSP models used for S<sup>3</sup>Fit.
You may also want to download an example of the converted SSP model for test in [this link][7].
S<sup>3</sup>Fit uses the [SKIRTor][4] ([paper1][5], [paper2][6]) AGN torus model.
Please download the [SKIRTor library][4] and run the
[converting code](https://github.com/xychcz/S3Fit/blob/main/model_libraries/convert_skirtor_torus.py)
to create the torus models used for S<sup>3</sup>Fit.
Example of this library is also provided in
[model libraries](https://github.com/xychcz/S3Fit/blob/main/model_libraries/) for a test of S<sup>3</sup>Fit,
which contains the templates with a fixed dust density gradient in radial (p = 1) and angular direction (q = 0.5).
Please refer to [SKIRTor][4] website for details of the model parameters.
[1]: <https://iopscience.iop.org/article/10.3847/1538-4357/ad93ab>
[2]: <https://www.fractal-es.com/PopStar/>
[3]: <https://academic.oup.com/mnras/article/506/4/4781/6319511>
[4]: https://sites.google.com/site/skirtorus/sed-library?authuser=0
[5]: http://adsabs.harvard.edu/abs/2012MNRAS.420.2756S
[6]: http://adsabs.harvard.edu/abs/2016MNRAS.458.2288S
[7]: https://drive.google.com/file/d/1JwdBOnl6APwFmadIX8BYLcLyFNZvnuYg/view?usp=share_link
Raw data
{
"_id": null,
"home_page": null,
"name": "s3fit",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "SED fitting, astronomy, galaxy, spectral fitting",
"author": null,
"author_email": "Xiaoyang Chen <xiaoyang.chen.cz@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/19/88/1479a2b06fa9c65ba3d3660b70eabae383d61fa43eb6353a0139d7d0e67e/s3fit-2.0.4.tar.gz",
"platform": null,
"description": "# S<sup>3</sup>Fit\n**S<sup>3</sup>Fit**: a <ins>**S**</ins>imultaneous <ins>**S**</ins>pectrum and photometric-<ins>**S**</ins>ED <ins>**Fit**</ins>ting code for observation of galaxies\n\nS<sup>3</sup>Fit is a Python-based tool for analyzing observational data of galaxies.\nIt offers powerful capabilities for decomposing spectroscopic data \nby supporting multiple continuum and emission line models with multiple components, \nmaking it well-suited for complex systems with mixed contributions \nfrom Active Galactic Nuclei (AGNs) and their host galaxies.\nBy simultaneously fitting the spectrum and multi-band photometric Spectral Energy Distribution (SED), \nS<sup>3</sup>Fit improves constraints on continuum model properties, \nwhich may be poorly determined when fitting spectral data alone due to its limited wavelength coverage.\nWith an optimized fitting strategy, \nS<sup>3</sup>Fit efficiently derives the best-fit solution for dozens of model parameters. \nAdditionally, it provides an extensible and user-friendly framework, \nallowing users to modify model configurations and incorporate new model components as needed.\n\n## Features of S<sup>3</sup>Fit\n- Easy switch between pure spectral fitting and joint spectrum+SED fitting modes.\n- Support for flexible combination of multiple stellar populations with different star formation histories (SFH). \n- Support for flexible combination of multiple emission line components.\n- Support for AGN continuum models across UV/optical and IR wavelength ranges.\n- User-friendly functions for outputting and visualizing fitting results.\n- Highly extensible framework, allowing users to add new features such as new SFH functions, emission lines, and custom model types.\n\n## Fitting strategy\nThe full fitting pipeline of S<sup>3</sup>Fit is shown in the following flowchart, \nwith a detailed description of the [fitting strategy](https://github.com/xychcz/S3Fit/blob/main/manuals/fitting_strategy.md) in \n[manuals](https://github.com/xychcz/S3Fit/blob/main/manuals/). \n<p align=\"center\"> <img src=\"https://github.com/user-attachments/assets/658a29b3-a2d8-470a-a08f-e4a045ef3ec3\" width=\"1200\">\n\nAn example of the fitting result of S<sup>3</sup>Fit is shown in the following plots. \n<p align=\"center\"> <img src=\"https://github.com/user-attachments/assets/683f5837-d364-4a53-8113-a05d56f9ef5b\" width=\"600\">\n\n## Usage\nPlease find guides in [manuals](https://github.com/xychcz/S3Fit/blob/main/manuals/) \nfor [basic](https://github.com/xychcz/S3Fit/blob/main/manuals/basic_usage.md) \nand [advanced](https://github.com/xychcz/S3Fit/blob/main/manuals/advanced_usage.md) usages of this code. \nAn example of the usage of S<sup>3</sup>Fit is provided in the \n[example](https://github.com/xychcz/S3Fit/blob/main/example/example.ipynb). \n\n## Installation\n\nYou can install S<sup>3</sup>Fit with [pip](https://pypi.org/project/s3fit/):\n```\npip install s3fit\n```\nS<sup>3</sup>Fit mainly depends on several most widely utilized repositories for science calculation, `scipy`, `numpy`, and `astropy`. \nThe core requirement of S<sup>3</sup>Fit is the two functions `least_squares` and `lsq_linear` in `scipy.optimize`\n(please read the \n[fitting strategy](https://github.com/xychcz/S3Fit/blob/main/manuals/fitting_strategy.md) for details). \nA strong dependency of S<sup>3</sup>Fit on these repositories is not expected. \nIt is optional to run S<sup>3</sup>Fit with [PyNeb](http://research.iac.es/proyecto/PyNeb/), \nwhich is used to calculate intrinsic flux ratios of emission lines:\n```\npip install s3fit[pyneb]\n```\n\nThe test environment is list as follows. \n```\npython = 3.10\nscipy = 1.12.0\nnumpy = 1.26.4\nastropy = 6.0.0\nmatplotlib = 3.9.1\npyneb = 1.1.23 (optional)\n```\n\n## Future updating\n- Add ISM dust and synchrotron models (you may also add them or other models by yourself following the\n [advanced usage](https://github.com/xychcz/S3Fit/blob/main/manuals/advanced_usage.md) manual).\n- Add iron pseudo continuum templates and Balmer continuum of type-1 AGN.\n- Test support for absorption lines. \n\n## Citation\nIf you would like to use S<sup>3</sup>Fit, please cite the paper [Chen et al. (2025)][1], in which a pure-spectral-fit mode of this code is firstly utilized. \nPlease find details of the configuration of star formation history and kinematic parameters of emission lines in the paper. \n\n## Reference\nS<sup>3</sup>Fit uses the Single Stellar Population (SSP) library [HR-pyPopStar][2] ([paper][3]). \nPlease download the [HR-pyPopStar library][2] and run the \n[converting code](https://github.com/xychcz/S3Fit/blob/main/model_libraries/convert_popstar_ssp.py) \nto create the SSP models used for S<sup>3</sup>Fit. \nYou may also want to download an example of the converted SSP model for test in [this link][7].\n\nS<sup>3</sup>Fit uses the [SKIRTor][4] ([paper1][5], [paper2][6]) AGN torus model. \nPlease download the [SKIRTor library][4] and run the \n[converting code](https://github.com/xychcz/S3Fit/blob/main/model_libraries/convert_skirtor_torus.py) \nto create the torus models used for S<sup>3</sup>Fit. \nExample of this library is also provided in \n[model libraries](https://github.com/xychcz/S3Fit/blob/main/model_libraries/) for a test of S<sup>3</sup>Fit, \nwhich contains the templates with a fixed dust density gradient in radial (p = 1) and angular direction (q = 0.5). \nPlease refer to [SKIRTor][4] website for details of the model parameters. \n\n[1]: <https://iopscience.iop.org/article/10.3847/1538-4357/ad93ab>\n[2]: <https://www.fractal-es.com/PopStar/>\n[3]: <https://academic.oup.com/mnras/article/506/4/4781/6319511>\n[4]: https://sites.google.com/site/skirtorus/sed-library?authuser=0\n[5]: http://adsabs.harvard.edu/abs/2012MNRAS.420.2756S\n[6]: http://adsabs.harvard.edu/abs/2016MNRAS.458.2288S\n[7]: https://drive.google.com/file/d/1JwdBOnl6APwFmadIX8BYLcLyFNZvnuYg/view?usp=share_link\n\n",
"bugtrack_url": null,
"license": null,
"summary": "S3Fit: a Simultaneous Spectrum and photometric-SED Fitting code for observation of galaxies",
"version": "2.0.4",
"project_urls": {
"Changelog": "https://github.com/xychcz/S3Fit/releases",
"Documentation": "https://github.com/xychcz/S3Fit/tree/main/manuals",
"Homepage": "https://github.com/xychcz",
"Repository": "https://github.com/xychcz/S3Fit"
},
"split_keywords": [
"sed fitting",
" astronomy",
" galaxy",
" spectral fitting"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "b5f1bdfa21f6c33e733fb42dff8186db998182019ea53dedd05ac673ce07eec1",
"md5": "325ede7b8cc57cd9c3863db26f18deb2",
"sha256": "33c592d95118b86a8f96248aaba5c37ac6d5da19679aa34cdf4a0a0b04008ba2"
},
"downloads": -1,
"filename": "s3fit-2.0.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "325ede7b8cc57cd9c3863db26f18deb2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 52702,
"upload_time": "2025-02-10T05:50:25",
"upload_time_iso_8601": "2025-02-10T05:50:25.989502Z",
"url": "https://files.pythonhosted.org/packages/b5/f1/bdfa21f6c33e733fb42dff8186db998182019ea53dedd05ac673ce07eec1/s3fit-2.0.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "19881479a2b06fa9c65ba3d3660b70eabae383d61fa43eb6353a0139d7d0e67e",
"md5": "a059575ab614618414854132d7d60cd4",
"sha256": "75d31947c9cb2ae6a3abbe05292cd63dde900a49cf790898acfff0b668152cf6"
},
"downloads": -1,
"filename": "s3fit-2.0.4.tar.gz",
"has_sig": false,
"md5_digest": "a059575ab614618414854132d7d60cd4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 4515806,
"upload_time": "2025-02-10T05:50:27",
"upload_time_iso_8601": "2025-02-10T05:50:27.840118Z",
"url": "https://files.pythonhosted.org/packages/19/88/1479a2b06fa9c65ba3d3660b70eabae383d61fa43eb6353a0139d7d0e67e/s3fit-2.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-10 05:50:27",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "xychcz",
"github_project": "S3Fit",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "s3fit"
}
