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Learning Julia

You're reading from   Learning Julia Build high-performance applications for scientific computing

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781785883279
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Rahul Lakhanpal Rahul Lakhanpal
Author Profile Icon Rahul Lakhanpal
Rahul Lakhanpal
Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (11) Chapters Close

Preface 1. Understanding Julia's Ecosystem FREE CHAPTER 2. Programming Concepts with Julia 3. Functions in Julia 4. Understanding Types and Dispatch 5. Working with Control Flow 6. Interoperability and Metaprogramming 7. Numerical and Scientific Computation with Julia 8. Data Visualization and Graphics 9. Connecting with Databases 10. Julia’s Internals

Floating point numbers in Julia


It is easy to represent floating point numbers in Julia. They are represented in a similar fashion as they are in other languages:

# Add a decimal point
julia> 100.0
100.0

julia> 24.
24.0

# It is not required to precede a number from the decimal point
julia> .10
0.1

julia> typeof(ans)
Float64

There is a concept of positive zero and negative zero in Julia. They are equal but with different binary representations:

# equating two zeroes
julia> 0.0 == -0.0
true

julia> bits(0.0)
"0000000000000000000000000000000000000000000000000000000000000000"

# different first bit for negative zero
julia> bits(-0.0)
"1000000000000000000000000000000000000000000000000000000000000000"

Exponential notation can be very useful and convenient in various scenarios. It can be used in Julia using e:

julia> 2.99e8
2.99e8

julia> 2.99e8>999999
true

We have been using Float64 in the preceding examples. We can also use Float32 on 64-bit computers if required:

#...
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