Numerical Python in astronomy and astrophysics a practical guide to astrophysical problem solving / [electronic resource] :

• 作者： Schmidt, Wolfram.
• 其他作者：
  • Volschow, Marcel.
  • SpringerLink (Online service)
• 其他題名：
  • Undergraduate lecture notes in physics.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Undergraduate lecture notes in physics,
• 主題： Astronomy--Data processing. , Python (Computer program language) , Numerical and Computational Physics, Simulation. , Astronomy, Astrophysics and Cosmology. , Python. , Numeric Computing.
• ISBN： 9783030703479 (electronic bk.) 、 9783030703462 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter 1. Introduction -- Chapter 2. Getting Started with Python -- Chapter 3. Computing and Displaying Data -- Chapter 4. Functions and Numerical Methods -- Chapter 5. Solving Differential Equations -- Chapter 6. Astronomical Data Analysis.
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• 摘要註: This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler's laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks.
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