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  • Book Overview & Buying Mastering Python Scientific Computing
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Mastering Python Scientific Computing

Mastering Python Scientific Computing

By : Hemant Kumar Mehta
4 (6)
Buy this Book
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Mastering Python Scientific Computing

Mastering Python Scientific Computing

4 (6)
By: Hemant Kumar Mehta
Buy this Book

Overview of this book

In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python. The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. The Landscape of Scientific Computing – and Why Python?
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1. The Landscape of Scientific Computing – and Why Python?
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Definition of scientific computing
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A simple flow of the scientific computation process
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Examples from scientific/engineering domains
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A strategy for solving complex problems
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Approximation, errors, and associated concepts and terms
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Computer arithmetic and floating-point numbers
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The background of the Python programming language
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Summary
2
2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes
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2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes
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Mathematical components of scientific computations
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Python scientific computing
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A brief idea of interactive programming using IPython
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Symbolic computing using SymPy
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Summary
3
3. Efficiently Fabricating and Managing Scientific Data
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3. Efficiently Fabricating and Managing Scientific Data
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The basic concepts of data
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Data storage software and toolkits
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Possible operations on data
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Scientific data format
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Ready-to-use standard datasets
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Data generation
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Synthetic data generation (fabrication)
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A brief note about large-scale datasets
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Summary
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4. Scientific Computing APIs for Python
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4. Scientific Computing APIs for Python
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Numerical scientific computing in Python
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Symbolic computations using SymPy
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APIs and toolkits for data analysis and visualization
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Summary
5
5. Performing Numerical Computing
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5. Performing Numerical Computing
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The NumPy fundamental objects
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Introduction to SciPy
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Summary
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6. Applying Python for Symbolic Computing
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6. Applying Python for Symbolic Computing
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Symbols, expressions, and basic arithmetic
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Equation solving
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Functions for rational numbers, exponentials, and logarithms
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Polynomials
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Trigonometry and complex numbers
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Linear algebra
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Calculus
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Vectors
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The physics module
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Pretty printing
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The cryptography module
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Parsing input
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The logic module
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The geometry module
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Symbolic integrals
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Polynomial manipulation
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Sets
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The simplify and collect operations
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Summary
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7. Data Analysis and Visualization
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7. Data Analysis and Visualization
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Matplotlib
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The pandas library
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I/O operations
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IPython
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Summary
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8. Parallel and Large-scale Scientific Computing
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8. Parallel and Large-scale Scientific Computing
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Parallel computing using IPython
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The architecture of IPython parallel computing
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Example of performing parallel computing
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Advanced features of IPython
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A note on security of IPython
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Summary
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9. Revisiting Real-life Case Studies
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9. Revisiting Real-life Case Studies
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Scientific computing applications developed in Python
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Python for developing a Blind Audio Tactile Mapping System
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Scientific computing libraries developed in Python
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Summary
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10. Best Practices for Scientific Computing
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10. Best Practices for Scientific Computing
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The best practices for designing
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The implementation of best practices
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The best practices for data management and application deployment
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The best practices to achieving high performance
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The best practices for data privacy and security
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Testing and maintenance best practices
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General Python best practices
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Summary
11
Index
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Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The functions of the random module are bound methods of a hidden instance of the random.Random class."

A block of code is set as follows:

import random
print random.random()
print random.uniform(1,9)
print random.randrange(20)
print random.randrange(0, 99, 3) 
print random.choice('ABCDEFGHIJKLMNOPQRSTUVWXYZ') # Output 'P'
items = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
random.shuffle(items)
print items
print random.sample([1, 2, 3, 4, 5, 6, 7, 8, 9, 10],  5)   
weighted_choices = [('Three', 3), ('Two', 2), ('One', 1), ('Four', 4)]
population = [val for val, cnt in weighted_choices for i in range(cnt)]
print random.choice(population)

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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