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‎PyKinematicalBroadening.egg-info/PKG-INFO‎

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Metadata-Version: 2.1
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Name: PyKinematicalBroadening
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Version: 0.0.7
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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.
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Home-page: https://github.com/neutrinomuon/PyKinematicalBroadening
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Author: Jean Gomes
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Author-email: antineutrinomuon@gmail.com
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License: UNKNOWN
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Platform: UNKNOWN
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Classifier: Programming Language :: Python :: 3
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Classifier: Operating System :: OS Independent
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Description-Content-Type: text/markdown
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License-File: LICENSE.txt
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### PyKinematicalBroadening
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email: [antineutrinomuon@gmail.com](mailto:antineutrinomuon@gmail.com), [jean@astro.up.pt](mailto:jean@astro.up.pt)
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© Copyright ®
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J.G. - Jean Gomes
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<hr>
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![python3](https://img.shields.io/pypi/pyversions/pyfluxconserving)
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<hr>
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#### <b>RESUME</b>
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PyKinematicalBroadening is an Extragalactic Kinematics repository for applying
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a kernel in velocity space to models in order to obtain the respective
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broadened model. This is a python code that performs kinematical broadening of
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a spectrum by applying a kernel in velocity space to a model, and obtaining
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the respective broadened model. The code defines the function broadening,
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which performs the convolution with a Gaussian kernel. The kernel is generated
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using a certain number of points, which can be set with the Ni_Gauss
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parameter. The code then reads in a test spectrum from a file, interpolates it
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onto a set of equally spaced wavelength values, and then plots the original
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and broadened spectra for different velocity dispersions.
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In detail, the GaussianConvolution function convolves a given input spectrum
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fluxes_o defined at wavelengths lambda_o with a Gaussian kernel of width
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vd_sigma and mean velocity vc0_gals. The kernel is defined with Ni_Gauss
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points, which should be at least as large as vd_sigma. The output spectrum is
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defined at wavelengths lambda_s, and is returned as fluxes_s. The fill_val
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parameter defines the value to use for regions outside of the original
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wavelength range, and verbosity controls the level of detail of console
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output.
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The main code reads in a test spectrum from a file and interpolates it onto a
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set of equally spaced wavelength values. It then loops over different velocity
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dispersions and calls 'broadening' for each one, broadening the spectrum and
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plotting the results.
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<hr>
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#### <b>INSTALLATION</b>
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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>:
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<pre>
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pip install PyKinematicalBroadening
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</pre>
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<br>or by using a generated conda repository <a href='https://anaconda.org/neutrinomuon/PyKinematicalBroadening'>https://anaconda.org/neutrinomuon/PyKinematicalBroadening</a>:
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[![badgetanaconda](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/version.svg)](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/version.svg)
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[![badgetreleasedate](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/latest_release_date.svg)](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/latest_release_date.svg)
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[![badgetplatforms](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/platforms.svg
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)](https://anaconda.org/neutrinomuon/PyKinematicalBroadening/badges/platforms.svg)
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<pre>
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conda install -c neutrinomuon PyKinematicalBroadening
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</pre>
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<br>OBS.: Linux, OS-X ad Windows pre-compilations available in conda.
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You can also clone the repository and install by yourself in your machine:
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<pre>
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git clone https://github.com/neutrinomuon/PyKinematicalBroadening
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python setup.py install
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</pre>
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<hr>
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#### <b>EXAMPLE</b>
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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.
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<img src="https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/KinematicalBroadening.png?raw=true" width="90%">
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<hr>
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#### <b>STRUCTURE</b>
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<pre>
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PyKinematicalBroadening
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├── MANIFEST.in
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├── dist
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│ ├── PyKinematicalBroadening-0.0.3.tar.gz
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│ ├── PyKinematicalBroadening-0.0.5.tar.gz
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│ ├── PyKinematicalBroadening-0.0.6.tar.gz
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│ └── PyKinematicalBroadening-0.0.4.tar.gz
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├── README.md
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├── figures
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│ ├── KinematicalBroadening.png
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│ └── cc_logo.png
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├── PyKinematicalBroadening.egg-info
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│ ├── PKG-INFO
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│ ├── dependency_links.txt
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│ ├── SOURCES.txt
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│ ├── top_level.txt
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│ └── requires.txt
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├── LICENSE.txt
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├── setup.py
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├── tutorials
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│ ├── .ipynb_checkpoints
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│ │ └── Example 1 - Kinematical Broadening-checkpoint.ipynb
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│ └── Example 1 - Kinematical Broadening.ipynb
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├── pykinematicalbroadening
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│ ├── win-32
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-armv7l
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-armv6l
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│ │ ├── .projectignore
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-s390x
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-ppc64
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-aarch64
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│ │ ├── .projectignore
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-32
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│ │ ├── .projectignore
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── linux-64
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│ │ ├── .projectignore
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── osx-64
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── meta.yaml
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│ ├── win-64
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ ├── README.txt
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│ ├── linux-ppc64le
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│ │ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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│ └── osx-arm64
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│ └── pykinematicalbroadening-0.0.5-py39_0.tar.bz2
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├── Pykinematicalbroadening.egg-info
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│ ├── PKG-INFO
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│ ├── dependency_links.txt
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│ ├── SOURCES.txt
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│ ├── top_level.txt
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│ └── requires.txt
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├── src
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│ └── python
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│ ├── __pycache__
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│ ├── test_spectrum.spec
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│ ├── __init__.py
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│ └── PyKinematicalBroadening.py
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├── version.txt
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└── build
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└── lib
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├── Pykinematicalbroadening
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└── PyKinematicalBroadening
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26 directories, 44 files
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</pre>
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<hr>
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#### <b>REFERENCES</b>
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<ol>
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<il>Bureau, M., et al. "The SAURON project - III. Integral-field absorption-line
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kinematics of 48 elliptical and lenticular galaxies." Monthly Notices of the
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Royal Astronomical Society, vol. 370, no. 2, 2006, pp. 753-771. DOI: <a
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href="https://doi.org/10.1111/j.1365-2966.2006.10998.x">10.1111/j.1365-2966.2006.10998.x</a>. Available
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at: <a
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href="https://academic.oup.com/mnras/article/370/2/753/1004246">https://academic.oup.com/mnras/article/370/2/753/1004246</a>.</il>
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<il>Faber, S. M. "The Stellar Population Histories of Elliptical Galaxies: A
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Review." Annual Review of Astronomy and Astrophysics, vol. 46, no. 1, 2008,
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pp. 121-157. DOI: <a
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href="https://doi.org/10.1146/annurev-astro-082708-101650">10.1146/annurev-astro-082708-101650</a>. Available
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at: <a
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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>
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<il>Peletier, R. F., et al. "The SAURON project - XI. Stellar populations from
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absorption-line strength maps of 24 early-type spirals." Monthly Notices of
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the Royal Astronomical Society, vol. 379, no. 2, 2007, pp. 445-469. DOI: <a
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href="https://doi.org/10.1111/j.1365-2966.2007.11803.x">10.1111/j.1365-2966.2007.11803.x</a>. Available
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at: <a
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href="https://academic.oup.com/mnras/article/379/2/445/1078958">https://academic.oup.com/mnras/article/379/2/445/1078958</a>.</il>
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<il>Maraston, C. "Spectral Synthesis of Stellar Populations with Star Formation
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Histories." Monthly Notices of the Royal Astronomical Society, vol. 362,
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no. 3, 2005, pp. 799-825. DOI: <a
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href="https://doi.org/10.1111/j.1365-2966.2005.09340.x">10.1111/j.1365-2966.2005.09340.x</a>. Available
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at: <a
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href="https://academic.oup.com/mnras/article/362/3/799/986891">https://academic.oup.com/mnras/article/362/3/799/986891</a>.</il>
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</ol>
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<hr>
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#### <b>LICENSE</b>
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Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0)
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<img src="https://github.com/neutrinomuon/PyKinematicalBroadening/blob/main/figures/cc_logo.png?raw=true" width="10%">
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<a href='https://creativecommons.org/licenses/by-nc-nd/4.0/'>Creative Commons Attribution-NonCommercial-NoDerivs (CC-BY-NC-ND)</a>
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‎PyKinematicalBroadening.egg-info/SOURCES.txt‎

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LICENSE.txt
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MANIFEST.in
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README.md
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setup.py
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version.txt
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PyKinematicalBroadening.egg-info/PKG-INFO
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PyKinematicalBroadening.egg-info/SOURCES.txt
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PyKinematicalBroadening.egg-info/dependency_links.txt
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PyKinematicalBroadening.egg-info/requires.txt
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PyKinematicalBroadening.egg-info/top_level.txt
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src/python/PyKinematicalBroadening.py
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src/python/__init__.py
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src/python/test_spectrum.spec

‎PyKinematicalBroadening.egg-info/dependency_links.txt‎

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‎PyKinematicalBroadening.egg-info/requires.txt‎

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numpy
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matplotlib

‎PyKinematicalBroadening.egg-info/top_level.txt‎

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PyKinematicalBroadening

‎dist/PyKinematicalBroadening-0.0.7.tar.gz‎

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