| Name | PyKinematicalBroadening JSON |
| Version |
0.0.9
JSON |
| download |
| home_page | https://github.com/neutrinomuon/PyKinematicalBroadening |
| Summary | Extragalactic Kinematics is an exciting tool that utilizes a kernel (e.g., Gaussian) to broaden models in velocity space, resulting in a highly accurate and detailed output. With this repository, you can easily apply kinematical broadening to your models and gain valuable insights into extragalactic kinematics. |
| upload_time | 2023-03-14 20:19:55 |
| maintainer | |
| docs_url | None |
| author | Jean Gomes |
| requires_python | |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
### PyKinematicalBroadening
email: [antineutrinomuon@gmail.com](mailto:antineutrinomuon@gmail.com), [jean@astro.up.pt](mailto:jean@astro.up.pt)
last stable version: 0.0.9
github repository: <a href='https://github.com/neutrinomuon/PyKinematicalBroadening/'>PyKinematicalBroadening</a>
© Copyright ®
J.G. - Jean Gomes
<hr>
<img src='https://img.shields.io/pypi/pyversions/PyKinematicalBroadening'>
<hr>
#### <b>RESUME</b>
<img src="https://raw.githubusercontent.com/neutrinomuon/PyKinematicalBroadening/main/figures/Kinematics.png" width=120>
PyKinematicalBroadening is a Python repository for Extragalactic Kinematics that performs kinematical broadening of a spectrum by convolving it with a kernel in velocity space, which results in a broadened model. This code provides a function called 'broadening,' which uses a Gaussian kernel for convolution. However, in addition to the Gaussian kernel, the code can also use Gauss-Hermite kernels for convolution.
The Gauss-Hermite kernels provide a more general perspective than the Gaussian kernels, as they can account for higher-order velocity moments of the broadening process. The code defines the width and mean velocity of the kernel with vd\_sigma and vc0\_gals, respectively, while the number of points for the kernel is set with the Ni\_Gauss parameter. The output spectrum is defined at wavelengths lambda\_s and is returned as fluxes\_s. The fill\_val parameter defines the value to use for regions outside of the original wavelength range, and verbosity controls the level of detail of console output.
In summary, the PyKinematicalBroadening code provides a flexible way to apply kinematical broadening to a spectrum using Gaussian or Gauss-Hermite kernels. The code reads in a test spectrum, interpolates it onto a set of equally spaced wavelength values, and loops over different velocity dispersions to call the 'broadening' function, which broadens the spectrum and plots the results.
<hr>
#### <b>INSTALLATION</b>
You can easily install <a href='https://pypi.org/project/PyKinematicalBroadening/'>PyKinematicalBroadening</a> by using pip - <a href='https://pypi.org/'>PyPI - The Python Package Index</a>:
<pre>
pip install PyKinematicalBroadening
</pre>
<br>or by using a generated conda repository <a href='https://anaconda.org/neutrinomuon/PyKinematicalBroadening'>https://anaconda.org/neutrinomuon/PyKinematicalBroadening</a>:
<img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/version.svg'><img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/latest_release_date.svg'><img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/platforms.svg'>
<pre>
conda install -c neutrinomuon PyKinematicalBroadening
</pre>
<br>OBS.: Linux, OS-X ad Windows pre-compilations available in conda.
You can also clone the repository and install by yourself in your machine:
<pre>
git clone https://github.com/neutrinomuon/PyKinematicalBroadening
python setup.py install
</pre>
<hr>
#### <b>EXAMPLE</b>
Example of the test spectrum test\_spectrum.spec successively broadened by different velocity dispersions in [km/s]. The code is not optimized for cpu speed, but it shows the principle of how it works.
<img src="https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/KinematicalBroadening.png?raw=true" width="90%">
<hr>
#### <b>STRUCTURE</b>
<pre>
PyKinematicalBroadening
├── MANIFEST.in
├── dist
│ ├── PyKinematicalBroadening-0.0.3.tar.gz
│ ├── PyKinematicalBroadening-0.0.5.tar.gz
│ ├── PyKinematicalBroadening-0.0.6.tar.gz
│ └── PyKinematicalBroadening-0.0.4.tar.gz
├── README.md
├── figures
│ ├── KinematicalBroadening.png
│ └── cc_logo.png
├── PyKinematicalBroadening.egg-info
│ ├── PKG-INFO
│ ├── dependency_links.txt
│ ├── SOURCES.txt
│ ├── top_level.txt
│ └── requires.txt
├── LICENSE.txt
├── setup.py
├── tutorials
│ ├── .ipynb_checkpoints
│ │ └── Example 1 - Kinematical Broadening-checkpoint.ipynb
│ └── Example 1 - Kinematical Broadening.ipynb
├── pykinematicalbroadening
│ ├── win-32
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-armv7l
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-armv6l
│ │ ├── .projectignore
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-s390x
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-ppc64
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-aarch64
│ │ ├── .projectignore
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-32
│ │ ├── .projectignore
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── linux-64
│ │ ├── .projectignore
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── osx-64
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── meta.yaml
│ ├── win-64
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ ├── README.txt
│ ├── linux-ppc64le
│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
│ └── osx-arm64
│ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
├── Pykinematicalbroadening.egg-info
│ ├── PKG-INFO
│ ├── dependency_links.txt
│ ├── SOURCES.txt
│ ├── top_level.txt
│ └── requires.txt
├── src
│ └── python
│ ├── __pycache__
│ ├── test_spectrum.spec
│ ├── __init__.py
│ └── PyKinematicalBroadening.py
├── version.txt
└── build
└── lib
├── Pykinematicalbroadening
└── PyKinematicalBroadening
26 directories, 44 files
</pre>
<hr>
#### <b>REFERENCES</b>
- Tonry, J., & Davis, M. "A survey of galaxy redshifts. I. Data reduction techniques". 1979, AJ, 84, 1511. DOI: <a href="https://doi.org/10.1086/112569">10.1086/112569</a>. Available at: <a href="https://ui.adsabs.harvard.edu/abs/1979AJ.....84.1511T/abstract">https://ui.adsabs.harvard.edu/abs/1979AJ.....84.1511T/abstract</a>
- van der Marel, R. P., & Franx, M. "A New Method for the Identification of Non-Gaussian Line Profiles in Elliptical Galaxies"
. 1993, ApJ, 407, 525. DOI: <a href="https://doi.org/10.1086/172534">10.1086/172534</a>. Available at: <a href="https://ui.adsabs.harvard.edu/abs/1993ApJ...407..525V/abstract">https://ui.adsabs.harvard.edu/abs/1993ApJ...407..525V/abstract</a>
- Emsellem, E., et al. "The SAURON project - III. Integral-field absorption-line
kinematics of 48 elliptical and lenticular galaxies." Monthly Notices of the Royal Astronomical Society, Volume 352, Issue 3, pp. 721-743. DOI: <a href="https://doi.org/10.1111/j.1365-2966.2004.07948.x">10.1111/j.1365-2966.2004.07948.x</a> ; <a href="https://doi.org/10.48550/arXiv.astro-ph/0404034">10.48550/arXiv.astro-ph/0404034</a>. Available
at: <a href="https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..721E/abstract">https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..721E/abstract</a>
<!--[//]: # (<il>Faber, S. M. "The Stellar Population Histories of Elliptical Galaxies: A
[//]: # Review." Annual Review of Astronomy and Astrophysics, vol. 46, no. 1, 2008,
[//]: # pp. 121-157. DOI: <a
[//]: # href="https://doi.org/10.1146/annurev-astro-082708-101650">10.1146/annurev-astro-082708-101650</a>. Available
[//]: # at: <a
[//]: # href="https://www.annualreviews.org/doi/10.1146/annurev-astro-082708-101650">https://www.annualreviews.org/doi/10.1146/annurev-astro-082708-
[//]: # 101650</a>.</il>)
[//]: # (<il>Peletier, R. F., et al. "The SAURON project - XI. Stellar populations from
[//]: # absorption-line strength maps of 24 early-type spirals." Monthly Notices of
[//]: # the Royal Astronomical Society, vol. 379, no. 2, 2007, pp. 445-469. DOI: <a
[//]: # href="https://doi.org/10.1111/j.1365-2966.2007.11803.x">10.1111/j.1365-2966.2007.11803.x</a>. Available
[//]: # at: <a
[//]: # href="https://academic.oup.com/mnras/article/379/2/445/1078958">https://academic.oup.com/mnras/article/379/2/445/1078958</a>.</il>)
[//]: # (<il>Maraston, C. "Spectral Synthesis of Stellar Populations with Star Formation
[//]: # Histories." Monthly Notices of the Royal Astronomical Society, vol. 362,
[//]: # no. 3, 2005, pp. 799-825. DOI: <a
[//]: # href="https://doi.org/10.1111/j.1365-2966.2005.09340.x">10.1111/j.1365-2966.2005.09340.x</a>. Available
[//]: # at: <a
[//]: # href="https://academic.oup.com/mnras/article/362/3/799/986891">https://academic.oup.com/mnras/article/362/3/799/986891</a>.</il>)-->
<hr>
#### <b>LICENSE</b>
Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0)
<img src="https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/cc_logo.png?raw=true" width="10%">
<a href='https://creativecommons.org/licenses/by-nc-nd/4.0/'>Creative Commons Attribution-NonCommercial-NoDerivs (CC-BY-NC-ND)</a>
Raw data
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"author": "Jean Gomes",
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"platform": null,
"description": "### PyKinematicalBroadening\nemail: [antineutrinomuon@gmail.com](mailto:antineutrinomuon@gmail.com), [jean@astro.up.pt](mailto:jean@astro.up.pt)\n\nlast stable version: 0.0.9\n\ngithub repository: <a href='https://github.com/neutrinomuon/PyKinematicalBroadening/'>PyKinematicalBroadening</a>\n\n\u00a9 Copyright \u00ae\n\nJ.G. - Jean Gomes\n\n<hr>\n\n<img src='https://img.shields.io/pypi/pyversions/PyKinematicalBroadening'>\n\n<hr>\n\n#### <b>RESUME</b>\n\n<img src=\"https://raw.githubusercontent.com/neutrinomuon/PyKinematicalBroadening/main/figures/Kinematics.png\" width=120>\n\nPyKinematicalBroadening is a Python repository for Extragalactic Kinematics that performs kinematical broadening of a spectrum by convolving it with a kernel in velocity space, which results in a broadened model. This code provides a function called 'broadening,' which uses a Gaussian kernel for convolution. However, in addition to the Gaussian kernel, the code can also use Gauss-Hermite kernels for convolution.\n\nThe Gauss-Hermite kernels provide a more general perspective than the Gaussian kernels, as they can account for higher-order velocity moments of the broadening process. The code defines the width and mean velocity of the kernel with vd\\_sigma and vc0\\_gals, respectively, while the number of points for the kernel is set with the Ni\\_Gauss parameter. The output spectrum is defined at wavelengths lambda\\_s and is returned as fluxes\\_s. The fill\\_val parameter defines the value to use for regions outside of the original wavelength range, and verbosity controls the level of detail of console output.\n\nIn summary, the PyKinematicalBroadening code provides a flexible way to apply kinematical broadening to a spectrum using Gaussian or Gauss-Hermite kernels. The code reads in a test spectrum, interpolates it onto a set of equally spaced wavelength values, and loops over different velocity dispersions to call the 'broadening' function, which broadens the spectrum and plots the results.\n\n<hr>\n\n#### <b>INSTALLATION</b>\n\nYou can easily install <a href='https://pypi.org/project/PyKinematicalBroadening/'>PyKinematicalBroadening</a> by using pip - <a href='https://pypi.org/'>PyPI - The Python Package Index</a>:\n\n<pre>\npip install PyKinematicalBroadening\n</pre>\n\n<br>or by using a generated conda repository <a href='https://anaconda.org/neutrinomuon/PyKinematicalBroadening'>https://anaconda.org/neutrinomuon/PyKinematicalBroadening</a>:\n\n<img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/version.svg'><img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/latest_release_date.svg'><img src='https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/platforms.svg'>\n\n<pre>\nconda install -c neutrinomuon PyKinematicalBroadening\n</pre>\n\n<br>OBS.: Linux, OS-X ad Windows pre-compilations available in conda.\n\nYou can also clone the repository and install by yourself in your machine:\n\n<pre>\ngit clone https://github.com/neutrinomuon/PyKinematicalBroadening\npython setup.py install\n</pre>\n\n<hr>\n\n#### <b>EXAMPLE</b>\n\nExample of the test spectrum test\\_spectrum.spec successively broadened by different velocity dispersions in [km/s]. The code is not optimized for cpu speed, but it shows the principle of how it works.\n\n<img src=\"https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/KinematicalBroadening.png?raw=true\" width=\"90%\">\n\n<hr>\n\n#### <b>STRUCTURE</b>\n\n<pre>\nPyKinematicalBroadening\n\u251c\u2500\u2500 MANIFEST.in\n\u251c\u2500\u2500 dist\n\u2502 \u251c\u2500\u2500 PyKinematicalBroadening-0.0.3.tar.gz\n\u2502 \u251c\u2500\u2500 PyKinematicalBroadening-0.0.5.tar.gz\n\u2502 \u251c\u2500\u2500 PyKinematicalBroadening-0.0.6.tar.gz\n\u2502 \u2514\u2500\u2500 PyKinematicalBroadening-0.0.4.tar.gz\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 figures\n\u2502 \u251c\u2500\u2500 KinematicalBroadening.png\n\u2502 \u2514\u2500\u2500 cc_logo.png\n\u251c\u2500\u2500 PyKinematicalBroadening.egg-info\n\u2502 \u251c\u2500\u2500 PKG-INFO\n\u2502 \u251c\u2500\u2500 dependency_links.txt\n\u2502 \u251c\u2500\u2500 SOURCES.txt\n\u2502 \u251c\u2500\u2500 top_level.txt\n\u2502 \u2514\u2500\u2500 requires.txt\n\u251c\u2500\u2500 LICENSE.txt\n\u251c\u2500\u2500 setup.py\n\u251c\u2500\u2500 tutorials\n\u2502 \u251c\u2500\u2500 .ipynb_checkpoints\n\u2502 \u2502 \u2514\u2500\u2500 Example 1 - Kinematical Broadening-checkpoint.ipynb\n\u2502 \u2514\u2500\u2500 Example 1 - Kinematical Broadening.ipynb\n\u251c\u2500\u2500 pykinematicalbroadening\n\u2502 \u251c\u2500\u2500 win-32\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-armv7l\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-armv6l\n\u2502 \u2502 \u251c\u2500\u2500 .projectignore\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-s390x\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-ppc64\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-aarch64\n\u2502 \u2502 \u251c\u2500\u2500 .projectignore\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-32\n\u2502 \u2502 \u251c\u2500\u2500 .projectignore\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 linux-64\n\u2502 \u2502 \u251c\u2500\u2500 .projectignore\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 osx-64\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 meta.yaml\n\u2502 \u251c\u2500\u2500 win-64\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u251c\u2500\u2500 README.txt\n\u2502 \u251c\u2500\u2500 linux-ppc64le\n\u2502 \u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u2502 \u2514\u2500\u2500 osx-arm64\n\u2502 \u2514\u2500\u2500 pykinematicalbroadening-0.0.5-py39_0.tar.bz2\n\u251c\u2500\u2500 Pykinematicalbroadening.egg-info\n\u2502 \u251c\u2500\u2500 PKG-INFO\n\u2502 \u251c\u2500\u2500 dependency_links.txt\n\u2502 \u251c\u2500\u2500 SOURCES.txt\n\u2502 \u251c\u2500\u2500 top_level.txt\n\u2502 \u2514\u2500\u2500 requires.txt\n\u251c\u2500\u2500 src\n\u2502 \u2514\u2500\u2500 python\n\u2502 \u251c\u2500\u2500 __pycache__\n\u2502 \u251c\u2500\u2500 test_spectrum.spec\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2514\u2500\u2500 PyKinematicalBroadening.py\n\u251c\u2500\u2500 version.txt\n\u2514\u2500\u2500 build\n \u2514\u2500\u2500 lib\n \u251c\u2500\u2500 Pykinematicalbroadening\n \u2514\u2500\u2500 PyKinematicalBroadening\n\n26 directories, 44 files\n</pre>\n\n<hr>\n\n#### <b>REFERENCES</b>\n\n- Tonry, J., & Davis, M. \"A survey of galaxy redshifts. I. Data reduction techniques\". 1979, AJ, 84, 1511. DOI: <a href=\"https://doi.org/10.1086/112569\">10.1086/112569</a>. Available at: <a href=\"https://ui.adsabs.harvard.edu/abs/1979AJ.....84.1511T/abstract\">https://ui.adsabs.harvard.edu/abs/1979AJ.....84.1511T/abstract</a>\n\n- van der Marel, R. P., & Franx, M. \"A New Method for the Identification of Non-Gaussian Line Profiles in Elliptical Galaxies\"\n. 1993, ApJ, 407, 525. DOI: <a href=\"https://doi.org/10.1086/172534\">10.1086/172534</a>. Available at: <a href=\"https://ui.adsabs.harvard.edu/abs/1993ApJ...407..525V/abstract\">https://ui.adsabs.harvard.edu/abs/1993ApJ...407..525V/abstract</a>\n \n- Emsellem, E., et al. \"The SAURON project - III. Integral-field absorption-line\nkinematics of 48 elliptical and lenticular galaxies.\" Monthly Notices of the Royal Astronomical Society, Volume 352, Issue 3, pp. 721-743. DOI: <a href=\"https://doi.org/10.1111/j.1365-2966.2004.07948.x\">10.1111/j.1365-2966.2004.07948.x</a> ; <a href=\"https://doi.org/10.48550/arXiv.astro-ph/0404034\">10.48550/arXiv.astro-ph/0404034</a>. Available\nat: <a href=\"https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..721E/abstract\">https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..721E/abstract</a>\n\n<!--[//]: # (<il>Faber, S. M. \"The Stellar Population Histories of Elliptical Galaxies: A\n[//]: # Review.\" Annual Review of Astronomy and Astrophysics, vol. 46, no. 1, 2008,\n[//]: # pp. 121-157. DOI: <a\n[//]: # href=\"https://doi.org/10.1146/annurev-astro-082708-101650\">10.1146/annurev-astro-082708-101650</a>. Available\n[//]: # at: <a\n[//]: # href=\"https://www.annualreviews.org/doi/10.1146/annurev-astro-082708-101650\">https://www.annualreviews.org/doi/10.1146/annurev-astro-082708-\n[//]: # 101650</a>.</il>)\n\n[//]: # (<il>Peletier, R. F., et al. \"The SAURON project - XI. Stellar populations from\n[//]: # absorption-line strength maps of 24 early-type spirals.\" Monthly Notices of\n[//]: # the Royal Astronomical Society, vol. 379, no. 2, 2007, pp. 445-469. DOI: <a\n[//]: # href=\"https://doi.org/10.1111/j.1365-2966.2007.11803.x\">10.1111/j.1365-2966.2007.11803.x</a>. Available\n[//]: # at: <a\n[//]: # href=\"https://academic.oup.com/mnras/article/379/2/445/1078958\">https://academic.oup.com/mnras/article/379/2/445/1078958</a>.</il>)\n\n[//]: # (<il>Maraston, C. \"Spectral Synthesis of Stellar Populations with Star Formation\n[//]: # Histories.\" Monthly Notices of the Royal Astronomical Society, vol. 362,\n[//]: # no. 3, 2005, pp. 799-825. DOI: <a\n[//]: # href=\"https://doi.org/10.1111/j.1365-2966.2005.09340.x\">10.1111/j.1365-2966.2005.09340.x</a>. Available\n[//]: # at: <a\n[//]: # href=\"https://academic.oup.com/mnras/article/362/3/799/986891\">https://academic.oup.com/mnras/article/362/3/799/986891</a>.</il>)-->\n\n<hr>\n\n#### <b>LICENSE</b>\n\nAttribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0)\n\n<img src=\"https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/cc_logo.png?raw=true\" width=\"10%\">\n\n<a href='https://creativecommons.org/licenses/by-nc-nd/4.0/'>Creative Commons Attribution-NonCommercial-NoDerivs (CC-BY-NC-ND)</a>\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Extragalactic Kinematics is an exciting tool that utilizes a kernel (e.g., Gaussian) to broaden models in velocity space, resulting in a highly accurate and detailed output. With this repository, you can easily apply kinematical broadening to your models and gain valuable insights into extragalactic kinematics.",
"version": "0.0.9",
"split_keywords": [],
"urls": [
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