Computational nuclear engineering and radiological science using python /

Computational Nuclear Engineering and Radiological Science Using Python provides the necessary knowledge users need to embed more modern computing techniques into current practices, while also helping practitioners replace Fortran-based implementations with higher level languages. The book is especi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: McClarren, Ryan G. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, England : Academic Press, 2018.
Temas:
Nuclear engineering.
Radiology.
Python (Computer program language)
Radiology
G�enie nucl�eaire.
Radiologie.
Python (Langage de programmation)
radiology.
TECHNOLOGY & ENGINEERING > Mechanical.
Nuclear engineering
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Computational Nuclear Engineering and Radiological Science Using PythonTM
  • Copyright
  • Contents
  • About the Author
  • Preface
  • Acknowledgment
  • Part I Introduction to Python for Scienti c Computing
  • 1 Getting Started in Python
  • 1.1 Why Python?
  • 1.1.1 Comments
  • 1.1.2 Errors
  • 1.1.3 Indentation
  • 1.2 Numeric Variables
  • 1.2.1 Integers
  • 1.2.2 Floating Point Numbers
  • 1.2.2.1 Built-in Mathematical Functions
  • 1.2.3 Complex Numbers
  • 1.3 Strings and Overloading
  • 1.4 Input
  • 1.5 Branching (If Statements)
  • 1.6 Iteration The Great Beyond
  • Further Reading
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Harriot's Method for Solving Cubics
  • 2 Digging Deeper Into Python
  • 2.1 A First Numerical Program
  • 2.2 For Loops
  • 2.3 Lists and Tuples
  • 2.3.1 Lists
  • 2.3.2 Tuples
  • 2.4 Floats and Numerical Precision
  • Further Reading
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Nuclear Reaction Q Values
  • 2. Calculating e, the Base of the Natural Logarithm
  • 3 Functions, Scoping, Recursion, and Other Miscellany
  • 3.1 Functions3.1.1 Calling Functions and Default Arguments
  • 3.1.2 Return Values
  • 3.2 Docstrings and Help
  • 3.3 Scope
  • 3.4 Recursion
  • 3.5 Modules
  • 3.6 Files
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Monte Carlo Integration
  • 4 NumPy and Matplotlib
  • 4.1 NumPy Arrays
  • 4.1.1 Creating Arrays in Neat Ways
  • 4.1.2 Operations on Arrays
  • 4.1.3 Universal Functions
  • 4.1.4 Copying Arrays and Scope
  • 4.1.5 Indexing, Slicing, and Iterating
  • 4.1.6 NumPy and Complex Numbers
  • 4.2 Matplotlib Basics
  • 4.2.1 Customizing Plots Further Reading
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Inhour Equation
  • 2. Fractal Growth
  • 3. Charges in a Plane
  • 5 Dictionaries and Functions as Arguments
  • 5.1 Dictionaries
  • 5.2 Functions Passed to Functions
  • 5.3 Lambda Functions
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Plutonium Decay Chain
  • 2. Simple Cryptographic Cipher
  • 6 Testing and Debugging
  • 6.1 Testing Your Code
  • 6.2 Debugging
  • 6.3 Assertions
  • 6.4 Error Handling
  • Further Reading
  • Problems Short Exercises
  • Programming Projects
  • 1. Test Function for k-Eigenvalue
  • Part II Numerical Methods
  • 7 Gaussian Elimination
  • 7.1 A Motivating Example
  • 7.2 A Function for Solving 3x3 Systems
  • 7.3 Gaussian Elimination for a General System
  • 7.4 Round off and Pivoting
  • 7.5 Time to Solution for Gaussian Elimination
  • Further Reading
  • Problems
  • Short Exercises
  • Programming Projects
  • 1. Xenon Poisoning
  • 2. Flux Capacitor Waste
  • 3. Four-Group Reactor Theory
  • 4. Matrix Inverse

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