Berry M High-Performance Scientific Computing Algorithms and Applications 2012

Berry M High-Performance Scientific Computing Algorithms and Applications 2012 | 5.8 MB
English | 361 Pages

Title: High-Performance Scientific Computing: Algorithms and Applications
Author: Michael W. Berry, Kyle A. Gallivan, Efstratios Gallopoulos, Ananth Grama, Bernard Philippe, Yousef Saad, Faisal Saied
Year: 2021

Description:
Leverage this example-packed, comprehensive guide for all your Python computational needs

Key Features

• Learn the first steps within Python to highly specialized concepts
  
• Explore examples and code snippets taken from typical programming situations within scientific computing.
  
• Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.
  

Book Description
Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.
This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.
By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.

What you will learn

• Understand the building blocks of computational mathematics, linear algebra, and related Python objects
  
• Use Matplotlib to create high-quality figures and graphics to draw and visualize results
  
• Apply object-oriented programming (OOP) to scientific computing in Python
  
• Discover how to use pandas to enter the world of data processing
  
• Handle exceptions for writing reliable and usable code
  
• Cover manual and automatic aspects of testing for scientific programming
  
• Get to grips with parallel computing to increase computation speed
  

Who this book is for
This book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.

Table of Contents

• Getting Started
  
• Variables and Basic Types
  
• Container Types
  
• Linear Algebra – Arrays
  
• Advanced Array Concepts
  
• Plotting
  
• Functions
  
• Classes
  
• Iterating
  
• Series and DataframeWorking With Pandas
  
• Communication by a Graphical User Interface
  
• Error and Exception Handling
  
• Namespaces, Scopes, and Modules
  
• Input and Output
  
• Testing
  
• Symbolic ComputationSymPy
  
• Interacting with the Operating System
  
• Python for Parallel Computing
  
• Comprehensive Examples
  

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