Name: Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving (Undergraduate Lecture Notes in Physics) (en Inglés)
Price: 76540.00 CLP
Availability: InStock
Author: Wolfram Schmidt
ISBN: 9783030703462

portada Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving (Undergraduate Lecture Notes in Physics) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Wolfram Schmidt

Año

2021

Idioma

Inglés

N° páginas

260

Encuadernación

Tapa Blanda

ISBN13

9783030703462

N° edición

Categorías

Lenguajes de programación scripting

Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving (Undergraduate Lecture Notes in Physics) (en Inglés)

Wolfram Schmidt (Autor) · Springer · Tapa Blanda

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Reseña del libro "Numerical Python in Astronomy and Astrophysics: A Practical Guide to Astrophysical Problem Solving (Undergraduate Lecture Notes in Physics) (en Inglés)"

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.