Python is a computer programming language that is rapidly gaining popularity throughout the sciences. A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This tutorial focuses on fundamentals and introduces a wide range of useful techniques, including: * Basic Python programming and scripting* Numerical arrays* Two- and three-dimensional graphics* Monte Carlo simulations* Numerical methods, including solving ordinary differential equations* Image processing* Animation Numerous code samples and exercises-with solutions-illustrate new ideas as they are introduced. A website that accompanies this guide provides additional resources, including data sets and sample code.
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Jesse M. Kinder earned his PhD in physics and astronomy at the University of Pennsylvania, completed a postdoctoral fellowship in quantum chemistry at Cornell University, and taught physics at Case Western Reserve University. He currently works as a consultant in Rio Rancho, New Mexico. Philip Nelson is professor of physics at the University of Pennsylvania. He is the author of Biological Physics and Physical Models of Living Systems.Dalla quarta di copertina:
“This book covers the basics of Python programming language, with an emphasis on physical modeling. It provides a very useful introduction to Python for undergraduate students and others who have never programmed before.”—Zeljko Ivezic, University of Washington
“This is an excellent introductory text, aimed at those with little to no experience in programming. In a clear and concise manner, the authors cover or touch upon all the important aspects of computational science in Python. They guide readers by explaining how to best perform certain common tasks in scientific computing. The book’s examples and user exercises are well selected.”—Quentin Caudron, Princeton University
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Descrizione libro Princeton University Press, 2015. Hardcover. Condizione libro: New. Codice libro della libreria P110691169586