Book Image

Learning Julia

By : Anshul Joshi, Rahul Lakhanpal
Book Image

Learning Julia

By: Anshul Joshi, Rahul Lakhanpal

Overview of this book

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Data Visualization and Graphics

Type introspection and reflection capabilities


Type introspection and reflection capabilities form a very useful part of any modern-day programming language. Their need arises due to the fact that many a time while coding, we come across a situation where we need to understand the types of objects or data that we are dealing with. Sometimes, we may need to find the type of an object, and other times we may end up coding some logic based on that object's types and properties.

Type introspection

As we know from the starting chapters that Julia supports multiple dispatch and we can create a new data type from any abstract data type, let's define a new type, named Student, and then create two sample objects for this type:

julia> type Student
           name::String
           age::Int64
       end

julia> alpha = Student("alpha",24)
Student("alpha", 24)

julia> beta = Student("beta",25)
Student("beta", 25)

Pretty simple! Now that we have two of these students, with the names alpha and beta...