# pyFADO
in processing.... Beta version
<p align="center">
<img align="center" src="https://github.com/neutrinomuon/AST4001-Extragalactic-Astronomy/blob/master/figures/FADO%20logo%20F-01.jpg?raw=true" width="260">
</p>
Description: This repository contains modules for reading and plotting the output files of FADO in python 3. It also contains a Jupyter Notebook with a <b>quickstart</b> guide on how to run the FADO spectral synthesis code.
You can start by clicking at: <a href='FADO_QuickStartGuide-GITHUB.ipynb'>Quick Start Guide</a>
pyFADO is a Python package for performing spectral synthesis analysis with FADO.
Overview
FADO is a popular code for performing stellar population synthesis modeling of galaxies. pyFADO extends FADO by providing a Python interface for constructing, fitting, and analyzing synthetic spectra.
Some key features of pyFADO include:
Efficient implementation of FADO's spectral synthesis algorithm in Python
Ability to easily load and manipulate observational data
Automatic parameter optimization using Markov Chain Monte Carlo (MCMC) methods
Flexible and customizable model construction and fitting options
Extensive plotting capabilities for visualizing model fits and results
Installation
To install pyFADO, simply clone the repository and install the required dependencies:
bash
Copy code
git clone https://github.com/username/pyfado.git
cd pyfado
pip install -r requirements.txt
Usage
To use pyFADO, you will need to provide a set of observational data and a model grid of synthetic spectra. Once you have loaded your data and model, you can run the fitting algorithm using the pyfado.fit() function.
Here's an example of how to use pyFADO to fit a model to some mock data:
python
Copy code
import pyfado
# Load observational data
data = pyfado.load_data('data.fits')
# Load model grid
model = pyfado.load_model('model.fits')
# Fit model to data
result = pyfado.fit(data, model)
# Plot the results
result.plot()
Documentation
For more information on how to use pyFADO, please refer to the documentation. You can generate the documentation locally by running:
go
Copy code
cd docs
make html
The documentation will then be available in docs/_build/html/index.html.
Contributing
If you would like to contribute to pyFADO, please submit a pull request with your changes. We welcome contributions of all kinds, including bug fixes, new features, and documentation improvements.
License
pyFADO is released under the MIT License. See the LICENSE file for details.
author: Jean Michel Gomes<br>
published: 04/06/2019
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"description": "# pyFADO\n\nin processing.... Beta version\n\n<p align=\"center\">\n <img align=\"center\" src=\"https://github.com/neutrinomuon/AST4001-Extragalactic-Astronomy/blob/master/figures/FADO%20logo%20F-01.jpg?raw=true\" width=\"260\">\n</p>\n\nDescription: This repository contains modules for reading and plotting the output files of FADO in python 3. It also contains a Jupyter Notebook with a <b>quickstart</b> guide on how to run the FADO spectral synthesis code.\n\nYou can start by clicking at: <a href='FADO_QuickStartGuide-GITHUB.ipynb'>Quick Start Guide</a>\n\npyFADO is a Python package for performing spectral synthesis analysis with FADO.\n\nOverview\nFADO is a popular code for performing stellar population synthesis modeling of galaxies. pyFADO extends FADO by providing a Python interface for constructing, fitting, and analyzing synthetic spectra.\n\nSome key features of pyFADO include:\n\nEfficient implementation of FADO's spectral synthesis algorithm in Python\nAbility to easily load and manipulate observational data\nAutomatic parameter optimization using Markov Chain Monte Carlo (MCMC) methods\nFlexible and customizable model construction and fitting options\nExtensive plotting capabilities for visualizing model fits and results\nInstallation\nTo install pyFADO, simply clone the repository and install the required dependencies:\n\nbash\nCopy code\ngit clone https://github.com/username/pyfado.git\ncd pyfado\npip install -r requirements.txt\nUsage\nTo use pyFADO, you will need to provide a set of observational data and a model grid of synthetic spectra. Once you have loaded your data and model, you can run the fitting algorithm using the pyfado.fit() function.\n\nHere's an example of how to use pyFADO to fit a model to some mock data:\n\npython\nCopy code\nimport pyfado\n\n# Load observational data\ndata = pyfado.load_data('data.fits')\n\n# Load model grid\nmodel = pyfado.load_model('model.fits')\n\n# Fit model to data\nresult = pyfado.fit(data, model)\n\n# Plot the results\nresult.plot()\nDocumentation\nFor more information on how to use pyFADO, please refer to the documentation. You can generate the documentation locally by running:\n\ngo\nCopy code\ncd docs\nmake html\nThe documentation will then be available in docs/_build/html/index.html.\n\nContributing\nIf you would like to contribute to pyFADO, please submit a pull request with your changes. We welcome contributions of all kinds, including bug fixes, new features, and documentation improvements.\n\nLicense\npyFADO is released under the MIT License. See the LICENSE file for details.\n\nauthor: Jean Michel Gomes<br>\npublished: 04/06/2019\n",
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