Julia and IJulia Overview

Assistant Professor Justin Dressel

Faculty of Mathematics, Physics, and Computation

Schmid College of Science and Technology



Task 1:

  • Open a Terminal.
  • Run the command "julia"
    You should be greeted with the following julia prompt:

Why Julia?

  • Easy to learn, but well-designed; new, and rapidly growing
  • Philosophy:  code should be as easy to write and read as Python and MATLAB, but execute (almost) as efficiently as FORTRAN and C
  • Rapid growth in the past few years, with great promise
  • As a just-in-time (JIT) compiled language using the LLVM, it is a nice middle ground between interpreted (Python) and compiled (C, Fortran)
   _       _ _(_)_     |  A fresh approach to technical computing
  (_)     | (_) (_)    |  Documentation: https://docs.julialang.org
   _ _   _| |_  __ _   |  Type "?help" for help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 0.6.0 (2017-06-19 13:05 UTC)
 _/ |\__'_|_|_|\__'_|  |  Official http://julialang.org/ release
|__/                   |  x86_64-pc-linux-gnu

The julia prompt acts like an interpreter, but runs every line of code through a compiler before executing that compiled code.  This makes execution significantly faster after the compilation step than an interpreted language like Python. 

JIT Compilation

julia> x = linspace(-2,2,10000)

julia> x .^ 2

julia> x .^ 2

julia> x .^ 3

julia> typeof(x)                                                                                                                                                                                                              
julia> typeof(x .^ 2)                                                                                                                                                                                                         

(define x as a range of values)

(compiles x here to square each element,

Note: .^ applies ^ across the whole array)

(subsequent operations that use the compiled x are significantly faster)

(StepRangeLen in Julia is like a generator in Python)

(Array in Julia is like a NumPy Array in Python)

Try running these!

(Note: syntax deliberately similar to MATLAB)

Type Inference

Structurally, julia is primarily a functional language with a multiple dispatch inferential type system

julia> square(x) = x .^ 2                                                                                                                                                                                                     
square (generic function with 1 method)
julia> square(3)
julia> square(1:10)

julia> square(s :: AbstractString) = s * s                                                                                                                                                                                    
square (generic function with 2 methods)
julia> square("foo")

julia> square(3)

julia> square2(x :: Real) = x .^ 2                                                                                                                                                                                            
square2 (generic function with 1 method)
julia> square2(3)                                                                                                                                                                                                             
julia> square2(1:10)                                                                                                                                                                                                          
ERROR: MethodError: `square2` has no method matching square2(::UnitRange{Int64})

(The type of x is inferred to be the most general possible type that supports the element-wise power operation .^)

(Here the type of x is explicitly specified to a Real number only. More specificity can mean better compiler optimization, but restricts generality.  Use sparingly)

(Here the type of x is restricted to a string.  When called, square will pick the most restricted type-match to execute first.)

Multiple Dispatch

Notably, julia is not object-oriented like Python.

The multiple-dispatch type system is much more flexible.

abstract Number
abstract Real     <: Number
abstract AbstractFloat <: Real
abstract Integer  <: Real
abstract Signed   <: Integer
abstract Unsigned <: Integer

Abstract Types themselves have a hierarchy, like classes in Python.  Here <: means "is a subtype of"

Methods are not attached to a single type as with Python classes.  Instead, all functions become "methods" by specifying the argument types they act on.

julia> methods(square)                                                                                                                                                                                                        
# 2 methods for generic function "square":
square(s::AbstractString) at REPL[9]:1
square(x) at REPL[6]:1

Composite Types

julia> type Foo                                                                                                                                                                                                               
         x :: Int                                                                                                                                                                                                             
         y :: Float64                                                                                                                                                                                                         
julia> f = Foo(3, 2.0)                                                                                                                                                                                                        
julia> f.x                                                                                                                                                                                                                    
julia> f.y                                                                                                                                                                                                                    
julia> g = Foo(3,4)                                                                                                                                                                                                           
julia> g.x                                                                                                                                                                                                                    
julia> g.y                                                                                                                                                                                                                    

Defining structured data types (known as structs in C) is simple. Such composite types behave in the same way as Python object attributes

Specifying the type automatically coerces the type in constructor (if it can) to ensure type safety and optimized compilation

How to Learn a Language Quickly

  1. Identify basic syntax
  2. Identify primary data types
  3. Identify primary control structures
  4. Identify primary organizational structures
  5. Find and emulate idiomatic examples
  6. Don't be afraid to play until you understand

Task 2 :

  • Open up these links as tabs in your browser for reference

Task 3 :

  • Create a new Jupyter Notebook:  learnjulia.ipynb
  • In the Kernel menu, go to Change kernel => Julia
    ​​    (You are now using Julia inside a Jupyter notebook!)
  • Create a Markdown cell at the top, add a title, then a subtitle with your name(s).
  • By investigating the links on the previous slide, playing with the interpreter, and discussion, answer the following questions in your notebook:
    1. What is the julia syntax for the following?
          comments, variable declarations, printing output
    2. What are the basic julia data types?  How do you define and use them?
    3. What basic control structures are available in julia? (for, while, etc.)
    4. What are some notable differences between Python and julia?  What are some notable similarities?
  • For each of these questions, create an example cell of working julia code that illustrates the answer, as well as descriptive Markdown cells.  After you are done, there should be no extra fluff in the notebook, and it should run without errors.
  • Try writing a function that generates the first 100 Fibonacci numbers in julia.
    (The start of this sequence is 1, 1, 2, 3, 5, 8, 13, ..., obtained by successive pair-wise sums.)

Jupyter Notebooks

juliamap(c,z; maxiter) :
  Implement the iteration algorithm for a Julia Set.

**Returns:** integer number of iterations, or zero
if the iteration never diverges.

 - c : complex constant definining the set
 - z : complex number being iterated
 - maxiter : maximum iteration number, defaults to 100
function juliamap(c, z; maxiter=100)
    for n = 1:maxiter
        z = z^2 + c
        if abs(z) > 2
            return n
    return 0

@doc juliamap

Julia Sets in Julia

Task 4 :

  • Create a new julia notebook: juliainjulia.ipynb
  • Add the following cell

(Note the different docstring convention from Python, which renders as Markdown.)

(The @doc macro finds and prints the docstring.)

# Specialize juliamap to c=0
j0(z) = juliamap(0,z)

# Evaluate j0 on single complex points. Note: im is the imaginary unit in Julia
print( j0( complex(1.1, 0.3) ) )  # Recommended construction for complex numbers
print( j0( 1.1 + 0.3im ) )       # Equivalent result, but constructs z in 2 steps

# Evaluate j0 across an array - the . notation automatically vectorizes any function
a = linspace(complex(0.1,0.3), complex(1.5,0.3), 100)
print( j0.(a) )

Create the following cell and execute it.  Discuss what it is doing in your notebook.

Note in particular the line showing that the convention f.(a) for a function f vectorizes the function f across an entire array of arguments

Complex Plane:  Take 1

Create the following cell and run it:

# Create a complex plane
function complex_plane(xmin=-2, xmax=2, ymin=-2, ymax=2; xpoints=2000, ypoints=2000)
    # y is a column vector
    y = linspace(ymin, ymax, ypoints)

    # x uses a transpose, yielding a row vector
    x = linspace(xmin, xmax, xpoints)'

    # z uses broadcasted addition and multiplication to create a plane
    z = x .+ y.*im;

    # The final line of a block is treated as the return value, in the absence
    # of an explicit return statement

Create the following cell and run it:

# The vectorized function can be applied directly to the plane
@time cplane = complex_plane()
@time j0p = j0.(cplane)

Discuss exactly how this code works.  What's the difference between the comma and the semicolon in the list of arguments?

Complex Plane:  Take 2

Create the following cell and run it:

mutable struct ComplexPlane
    x :: LinSpace{Float64}
    y :: LinSpace{Float64}
    z :: Array{Number,2}
    function ComplexPlane(xmin=-2, xmax=2, ymin=-2, ymax=2;
                            xpoints=2000, ypoints=2000)
        x = linspace(xmin, xmax, xpoints)
        y = linspace(ymin, ymax, ypoints)
        z = x' .+ y.*im

Create the following cells and run them:

cplane = ComplexPlane(xpoints=200,ypoints=200);

How does a mutable struct compare to a Python class?

How is the constructor like the __init__ method of a Python class?

cplane.z = j0.(cplane.z)

Plotting with PyPlot

# Run the following in a terminal julia interpreter, not the notebook!
julia> Pkg.add("PyPlot")    #  This downloads the package via git and installs it
julia> Pkg.build("PyPlot)   #  Check that everything is built properly
# This should only need to be done once to verify it is installed properly

For plotting, we can call Python's matplotlib directly from within Julia

c = -1.037 + 0.17im                  #  Set starting point of julia set
j(z) = juliamap(c, z)                #  Create julia map
cplane = ComplexPlane()              #  Create 2000x2000 point complex plane
jp = j.(cplane.z);                   #  Apply julia map to entire plane

using PyPlot                         #  Load PyPlot package into the current namespace
title("Julia Set: c = " * string(c))
pcolormesh(cplane.x, cplane.y, jp, cmap=PyPlot.cm_get_cmap("hot"))
savefig("julia.png")                 #  Also output figure to png file

Julia can conveniently install packages automatically via git and the web.


Julia first appeared in 2012, so it is only ~5 years old.  However, it is very rapidly becoming a mature and promising scientific programming language.

It will reach version 1.0 in 2018.

My prediction is that julia will become a major language for data science and scientific computing.

Evidence: https://lectures.quantecon.org/jl

Task 5 :

  • Change the seed value (c) in the code for generating Julia Sets to see what happens.  Find values of c that you like.
  • Plot 5 of your favorite Julia Sets in your notebook.
  • Commit the notebook in GitHub.