Python Resources
Sherpa is an importable module for the dynamic, object-oriented Python programming language, which means you can write your own Python scripts for use in Sherpa. This easily allows for creating complex analysis and modeling functions, building batch-mode analysis, and extending the provided functionality to meet required needs. Below is a list of Python resources which you may find helpful as you conduct your scientific analysis in Sherpa. To learn about the key features of Python, refer to the official Python website.
![[NOTE]](../imgs/note.png)
The Python version used in CIAO 4.15 is Python 3.9 - when using ciao-install - or your choice of Python 3.8, 3.9, or 3.10 when using the conda installation.
Tutorials
- The Python Tutorial
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Comprehensive documentation provided by the official Python website.
- Practical Python for Astronomers
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A series of hands-on workshops to explore the Python language and the analysis tools it provides. The emphasis is on using Python to solve real-world problems that astronomers are likely to encounter in research.
- Practical Python for Astronomers: Matplotlib
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A guide to using Matplotlib.
- Matplotlib tutorial
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The Matplotlib tutorial.
- AstroPy
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A community Python library for Astronomy. See also the associated paper Astropy: A community Python package for astronomy by the AstroPy collaboration.
- AstroML: Machine Learning and Data Mining for Astronomy
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The astroML project accompanies the book "Statistics, Data Mining, and Machine Learning in Astronomy".
- Sherpa Blog
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Includes useful tips and tricks on using Python in Sherpa.
- Python Perambulations
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A wide-ranging look at data analysis with Python.
- Plumber Jack
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Documents miscellaneous items relating to the Python logging package.
- Doug Hellmann
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The "Python Module of the Week" series provides examples of usage of the various modules contained in the Python standard library.
- Beginning Python: From Novice to Professional, by Magnus Lie Hetland
- A Primer on Scientific Programming with Python, by Hans Petter Langtangen
- Python Scripting for Computational Science, Hans Petter Langtangen
- Beginning Python Visualization: Crafting Visual Transformation Scripts, by Shai Vaingast
- Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray