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# Julia Essentials¶

Contents

Having covered a few examples, let’s now turn to a more systematic exposition of the essential features of the language

## Overview¶

Topics:

- Common data types
- Basic file I/O
- Iteration
- More on user-defined functions
- Comparisons and logic

## Common Data Types¶

Like most languages, Julia language defines and provides functions for operating on standard data types such as

- integers
- floats
- strings
- arrays, etc…

Let’s learn a bit more about them

### Primitive Data Types¶

A particularly simple data type is a Boolean value, which can be either `true`

or
`false`

```
x = true
```

```
true
```

```
typeof(x)
```

```
Bool
```

```
y = 1 > 2 # Now y = false
```

```
false
```

Under addition, `true`

is converted to `1`

and `false`

is converted to `0`

```
true + false
```

```
1
```

```
sum([true, false, false, true])
```

```
2
```

The two most common data types used to represent numbers are integers and floats

(Computers distinguish between floats and integers because arithmetic is handled in a different way)

```
typeof(1.0)
```

```
Float64
```

```
typeof(1)
```

```
Int64
```

If you’re running a 32 bit system you’ll still see `Float64`

, but you will see `Int32`

instead of `Int64`

(see the section on Integer types from the Julia manual)

Arithmetic operations are fairly standard

```
x = 2; y = 1.0
```

```
1.0
```

```
x * y
```

```
2.0
```

```
x^2
```

```
4
```

```
y / x
```

```
0.5
```

Although the `*`

can be omitted for multiplication between a numeric literal and a variable

```
2x - 3y
```

```
1.0
```

Also, you can use function (instead of infix) notation if you so desire

```
+(10, 20)
```

```
30
```

```
*(10, 20)
```

```
200
```

Complex numbers are another primitive data type, with the imaginary part being specified by `im`

```
x = 1 + 2im
```

```
1 + 2im
```

```
y = 1 - 2im
```

```
1 - 2im
```

```
x * y # Complex multiplication
```

```
5 + 0im
```

There are several more primitive data types that we’ll introduce as necessary

### Strings¶

A string is a data type for storing a sequence of characters

```
x = "foobar"
```

```
"foobar"
```

```
typeof(x)
```

```
String
```

You’ve already seen examples of Julia’s simple string formatting operations

```
x = 10; y = 20
```

```
20
```

```
"x = $x"
```

```
"x = 10"
```

```
"x + y = $(x + y)"
```

```
"x + y = 30"
```

To concatenate strings use `*`

```
"foo" * "bar"
```

```
"foobar"
```

Julia provides many functions for working with strings

```
s = "Charlie don't surf"
```

```
"Charlie don't surf"
```

```
split(s)
```

```
3-element Array{SubString{String},1}:
"Charlie"
"don't"
"surf"
```

```
replace(s, "surf", "ski")
```

```
"Charlie don't ski"
```

```
split("fee,fi,fo", ",")
```

```
3-element Array{SubString{String},1}:
"fee"
"fi"
"fo"
```

```
strip(" foobar ") # Remove whitespace
```

```
"foobar"
```

Julia can also find and replace using regular expressions (see the documentation on regular expressions for more info)

```
match(r"(\d+)", "Top 10") # Find digits in string
```

```
RegexMatch("10", 1="10")
```

### Containers¶

Julia has several basic types for storing collections of data

We have already discussed arrays

A related data type is **tuples**, which can act like “immutable” arrays

```
x = ("foo", "bar")
```

```
("foo","bar")
```

```
typeof(x)
```

```
Tuple{String,String}
```

An immutable object is one that cannot be altered once it resides in memory

In particular, tuples do not support item assignment:

```
x[1] = 42
```

```
MethodError: no method matching setindex!(::Tuple{String,String}, ::Int64, ::Int64)
```

This is similar to Python, as is the fact that the parenthesis can be omitted

```
x = "foo", "bar"
```

```
("foo","bar")
```

Another similarity with Python is tuple unpacking, which means that the following convenient syntax is valid

```
x = ("foo", "bar")
```

```
("foo","bar")
```

```
word1, word2 = x
```

```
("foo","bar")
```

```
word1
```

```
"foo"
```

```
word2
```

```
"bar"
```

#### Referencing Items¶

The last element of a sequence type can be accessed with the keyword `end`

```
x = [10, 20, 30, 40]
```

```
4-element Array{Int64,1}:
10
20
30
40
```

```
x[end]
```

```
40
```

```
x[end-1]
```

```
30
```

To access multiple elements of an array or tuple, you can use slice notation

```
x[1:3]
```

```
3-element Array{Int64,1}:
10
20
30
```

```
x[2:end]
```

```
3-element Array{Int64,1}:
20
30
40
```

The same slice notation works on strings

```
"foobar"[3:end]
```

```
"obar"
```

#### Dictionaries¶

Another container type worth mentioning is dictionaries

Dictionaries are like arrays except that the items are named instead of numbered

```
d = Dict("name" => "Frodo", "age" => 33)
```

```
Dict{String,Any} with 2 entries:
"name" => "Frodo"
"age" => 33
```

```
d["age"]
```

```
33
```

The strings `name`

and `age`

are called the **keys**

The objects that the keys are mapped to (`"Frodo"`

and `33`

) are called the **values**

They can be accessed via `keys(d)`

and `values(d)`

respectively

## Input and Output¶

Let’s have a quick look at reading from and writing to text files

We’ll start with writing

```
f = open("newfile.txt", "w") # "w" for writing
```

```
IOStream(<file newfile.txt>)
```

```
write(f, "testing\n") # \n for newline
```

```
8
```

```
write(f, "more testing\n")
```

```
13
```

```
close(f)
```

The effect of this is to create a file called `newfile.txt`

in your present
working directory with contents

```
testing
more testing
```

We can read the contents of `newline.txt`

as follows

```
f = open("newfile.txt", "r") # Open for reading
```

```
IOStream(<file newfile.txt>)
```

```
print(readstring(f))
```

```
testing
more testing
```

```
close(f)
```

Often when reading from a file we want to step through the lines of a file, performing an action on each one

There’s a neat interface to this in Julia, which takes us to our next topic

## Iterating¶

One of the most important tasks in computing is stepping through a sequence of data and performing a given action

Julia’s provides neat, flexible tools for iteration as we now discuss

### Iterables¶

An iterable is something you can put on the right hand side of `for`

and loop over

These include sequence data types like arrays

```
actions = ["surf", "ski"]
for action in actions
println("Charlie don't $action")
end
```

```
Charlie don't surf
Charlie don't ski
```

They also include so-called **iterators**

You’ve already come across these types of objects

```
for i in 1:3 print(i) end
```

```
123
```

If you ask for the keys of dictionary you get an iterator

```
d = Dict("name" => "Frodo", "age" => 33)
```

```
Dict{String,Any} with 2 entries:
"name" => "Frodo"
"age" => 33
```

```
keys(d)
```

```
Base.KeyIterator for a Dict{String,Any} with 2 entries. Keys:
"name"
"age"
```

This makes sense, since the most common thing you want to do with keys is loop over them

The benefit of providing an iterator rather than an array, say, is that the former is more memory efficient

Should you need to transform an iterator into an array you can always use `collect()`

```
collect(keys(d))
```

```
2-element Array{String,1}:
"name"
"age"
```

### Looping without Indices¶

You can loop over sequences without explicit indexing, which often leads to neater code

For example compare

```
x_values = linspace(0, 3, 10)
```

```
for x in x_values
println(x * x)
end
```

```
0.0
0.1111111111111111
0.4444444444444444
1.0
1.7777777777777777
2.777777777777778
4.0
5.4444444444444455
7.111111111111111
9.0
```

```
for i in 1:length(x_values)
println(x_values[i] * x_values[i])
end
```

```
0.0
0.1111111111111111
0.4444444444444444
1.0
1.7777777777777777
2.777777777777778
4.0
5.4444444444444455
7.111111111111111
9.0
```

Julia provides some functional-style helper functions (similar to Python) to facilitate looping without indices

One is `zip()`

, which is used for stepping through pairs from two sequences

For example, try running the following code

```
countries = ("Japan", "Korea", "China")
cities = ("Tokyo", "Seoul", "Beijing")
for (country, city) in zip(countries, cities)
println("The capital of $country is $city")
end
```

```
The capital of Japan is Tokyo
The capital of Korea is Seoul
The capital of China is Beijing
```

If we happen to need the index as well as the value, one option is to use `enumerate()`

The following snippet will give you the idea

```
countries = ("Japan", "Korea", "China")
cities = ("Tokyo", "Seoul", "Beijing")
for (i, country) in enumerate(countries)
city = cities[i]
println("The capital of $country is $city")
end
```

```
The capital of Japan is Tokyo
The capital of Korea is Seoul
The capital of China is Beijing
```

### Comprehensions¶

Comprehensions are an elegant tool for creating new arrays or dictionaries from iterables

Here’s some examples

```
doubles = [2i for i in 1:4]
```

```
4-element Array{Int64,1}:
2
4
6
8
```

```
animals = ["dog", "cat", "bird"]; # Semicolon suppresses output
```

```
plurals = [animal * "s" for animal in animals]
```

```
3-element Array{String,1}:
"dogs"
"cats"
"birds"
```

```
[i + j for i in 1:3, j in 4:6]
```

```
3×3 Array{Int64,2}:
5 6 7
6 7 8
7 8 9
```

```
[i + j + k for i in 1:3, j in 4:6, k in 7:9]
```

```
3×3×3 Array{Int64,3}:
[:, :, 1] =
12 13 14
13 14 15
14 15 16
[:, :, 2] =
13 14 15
14 15 16
15 16 17
[:, :, 3] =
14 15 16
15 16 17
16 17 18
```

The same kind of expression works for dictionaries

```
Dict("$i" => i for i in 1:3)
```

```
Dict{String,Int64} with 3 entries:
"1" => 1
"2" => 2
"3" => 3
```

## Comparisons and Logical Operators¶

### Comparisons¶

As we saw earlier, when testing for equality we use `==`

```
x = 1
```

```
1
```

```
x == 2
```

```
false
```

For “not equal” use `!=`

```
x != 3
```

```
true
```

We can chain inequalities:

```
1 < 2 < 3
```

```
true
```

```
1 <= 2 <= 3
```

```
true
```

In many languages you can use integers or other values when testing conditions but Julia is more fussy

```
while 0 println("foo") end
```

```
ERROR: TypeError: non-boolean (Int64) used in boolean context
Stacktrace:
[1] anonymous at ./<missing>:?
```

```
if 1 print("foo") end
```

```
TypeError: non-boolean (Int64) used in boolean context
```

### Combining Expressions¶

Here are the standard logical connectives (conjunction, disjunction)

```
true && false
false
```

```
false
```

```
true || false
```

```
true
```

Remember

`P && Q`

is`true`

if both are`true`

, otherwise it’s`false`

`P || Q`

is`false`

if both are`false`

, otherwise it’s`true`

## User-Defined Functions¶

Let’s talk a little more about user-defined functions

User-defined functions are important for improving the clarity of your code by

- separating different strands of logic
- facilitating code reuse (writing the same thing twice is always a bad idea)

Julia functions are convenient:

- Any number of functions can be defined in a given file
- Any “value” can be passed to a function as an argument, including other functions
- Functions can be (and often are) defined inside other functions
- A function can return any kind of value, including functions

We’ll see many examples of these structures in the following lectures

For now let’s just cover some of the different ways of defining functions

### Return Statement¶

In Julia, the `return`

statement is optional, so that the following functions
have identical behavior

```
function f1(a, b)
return a * b
end
function f2(a, b)
a * b
end
```

When no return statement is present, the last value obtained when executing the code block is returned

Although some prefer the second option, we often favor the former on the basis that explicit is better than implicit

A function can have arbitrarily many `return`

statements, with execution terminating when the first return is hit

You can see this in action when experimenting with the following function

```
function foo(x)
if x > 0
return "positive"
end
return "nonpositive"
end
```

### Other Syntax for Defining Functions¶

For short function definitions Julia offers some attractive simplified syntax

First, when the function body is a simple expression, it can be defined
without the `function`

keyword or `end`

```
ff(x) = sin(1 / x)
```

Let’s check that it works

```
ff(1 / pi)
```

```
1.2246467991473532e-16
```

Julia also allows for you to define anonymous functions

For example, to define `f(x) = sin(1 / x)`

you can use `x -> sin(1 / x)`

The difference is that the second function has no name bound to it

How can you use a function with no name?

Typically it’s as an argument to another function

```
map(x -> sin(1 / x), randn(3)) # Apply function to each element
```

```
3-element Array{Float64,1}:
0.694055
-0.814733
-0.481659
```

### Optional and Keyword Arguments¶

Function arguments can be given default values

```
function fff(x, a=1)
return exp(cos(a * x))
end
```

If the argument is not supplied the default value is substituted

```
fff(pi)
```

```
0.36787944117144233
```

```
fff(pi, 2)
```

```
2.718281828459045
```

Another option is to use **keyword** arguments

The difference between keyword and standard (positional) arguments is that they are parsed and bound by name rather than order in the function call

For example, in the call

```
simulate(param1, param2, max_iterations=100, error_tolerance=0.01)
```

the last two arguments are keyword arguments and their order is irrelevant (as long as they come after the positional arguments)

To define a function with keyword arguments you need to use `;`

like so

```
function simulate_kw(param1, param2; max_iterations=100, error_tolerance=0.01)
# Function body here
end
```

## Vectorized Functions¶

A common scenario in computing is that

- we have a function
`f`

such that`f(x)`

returns a number for any number`x`

- we wish to apply
`f`

to every element of a vector`x_vec`

to produce a new vector`y_vec`

In Julia loops are fast and we can do this easily enough with a loop

For example, suppose that we want to apply `sin`

to `x_vec = [2.0, 4.0, 6.0, 8.0]`

The following code will do the job

```
x_vec = [2.0, 4.0, 6.0, 8.0]
y_vec = similar(x_vec)
for (i, x) in enumerate(x_vec)
y_vec[i] = sin(x)
end
```

But this is a bit unwieldy so Julia offers the alternative syntax

```
y_vec = sin.(x_vec)
```

More generally, if `f`

is any Julia function, then `f.`

references the vectorized version

Conveniently, this applies to user-defined functions as well

To illustrate, let’s write a function `chisq`

such that `chisq(k)`

returns a chi-squared random variable with `k`

degrees of freedom when `k`

is an integer

In doing this we’ll exploit the fact that, if we take `k`

independent standard normals, square them all and sum, we get a chi-squared with `k`

degrees of freedom

```
function chisq(k::Integer)
@assert k > 0 "k must be a natural number"
z = randn(k)
return sum(z.^2)
end
```

```
chisq(3)
```

```
1.5841392760511817
```

Note that calls with integers less than 1 will trigger an assertion failure inside the function body

```
chisq(-2)
```

```
AssertionError: k must be a natural number
```

Let’s try this out on an array of integers, adding the vectorized notation

```
chisq.([2, 4, 6])
```

```
3-element Array{Float64,1}:
0.992351
3.03434
3.29578
```

## Exercises¶

### Exercise 1¶

Part 1: Given two numeric arrays or tuples `x_vals`

and `y_vals`

of equal length, compute
their inner product using `zip()`

Part 2: Using a comprehension, count the number of even numbers between 0 and 99

- Hint:
`x % 2`

returns 0 if`x`

is even, 1 otherwise

Part 3: Using a comprehension, take `pairs = ((2, 5), (4, 2), (9, 8), (12, 10))`

and count the number of pairs `(a, b)`

such that both `a`

and `b`

are even

### Exercise 2¶

Consider the polynomial

Using `enumerate()`

in your loop, write a function `p`

such that `p(x, coeff)`

computes the value in (1) given a point `x`

and an array of coefficients `coeff`

### Exercise 3¶

Write a function that takes a string as an argument and returns the number of capital letters in the string

Hint: `uppercase("foo")`

returns `"FOO"`

### Exercise 4¶

Write a function that takes two sequences `seq_a`

and `seq_b`

as arguments and
returns `true`

if every element in `seq_a`

is also an element of `seq_b`

, else
`false`

- By “sequence” we mean an array, tuple or string

### Exercise 5¶

The Julia libraries include functions for interpolation and approximation

Nevertheless, let’s write our own function approximation routine as an exercise

In particular, write a function `linapprox`

that takes as arguments

- A function
`f`

mapping some interval \([a, b]\) into \(\mathbb R\) - two scalars
`a`

and`b`

providing the limits of this interval - An integer
`n`

determining the number of grid points - A number
`x`

satisfying`a <= x <= b`

and returns the piecewise linear interpolation of `f`

at `x`

, based on `n`

evenly spaced grid points `a = point[1] < point[2] < ... < point[n] = b`

Aim for clarity, not efficiency

### Exercise 6¶

The following data lists US cities and their populations

```
new york: 8244910
los angeles: 3819702
chicago: 2707120
houston: 2145146
philadelphia: 1536471
phoenix: 1469471
san antonio: 1359758
san diego: 1326179
dallas: 1223229
```

Copy this text into a text file called `us_cities.txt`

and save it in your present working directory

- That is, save it in the location Julia returns when you call
`pwd()`

This can also be achieved by running the following Julia code:

```
open("us_cities.txt", "w") do f
write(f,
"new york: 8244910
los angeles: 3819702
chicago: 2707120
houston: 2145146
philadelphia: 1536471
phoenix: 1469471
san antonio: 1359758
san diego: 1326179
dallas: 1223229")
end
```

Write a program to calculate total population across these cities

Hints:

- If
`f`

is a file object then`eachline(f)`

provides an iterable that steps you through the lines in the file `parse(Int, "100")`

converts the string`"100"`

into an integer

## Solutions¶

### Exercise 1¶

Part 1 solution:

Here’s one possible solution

```
x_vals = [1, 2, 3]
y_vals = [1, 1, 1]
sum([x * y for (x, y) in zip(x_vals, y_vals)])
```

```
6
```

Part 2 solution:

One solution is

```
sum([x % 2 == 0 for x in 0:99])
```

```
50
```

This also works

```
sum(map(x -> x % 2 == 0, 0:99))
```

```
50
```

Part 3 solution:

Here’s one possibility

```
pairs = ((2, 5), (4, 2), (9, 8), (12, 10))
sum([(x % 2 == 0) && (y % 2 == 0) for (x, y) in pairs])
```

```
2
```

### Exercise 3¶

Here’s one solutions:

```
function f_ex3(string)
count = 0
for letter in string
if (letter == uppercase(letter)) && isalpha(letter)
count += 1
end
end
return count
end
f_ex3("The Rain in Spain")
```

```
3
```

### Exercise 4¶

Here’s one solutions:

```
function f_ex4(seq_a, seq_b)
is_subset = true
for a in seq_a
if !(a in seq_b)
is_subset = false
end
end
return is_subset
end
# == test == #
println(f_ex4([1, 2], [1, 2, 3]))
println(f_ex4([1, 2, 3], [1, 2]))
```

```
true
false
```

if we use the Set data type then the solution is easier

```
f_ex4_2(seq_a, seq_b) = issubset(Set(seq_a), Set(seq_b))
println(f_ex4_2([1, 2], [1, 2, 3]))
println(f_ex4_2([1, 2, 3], [1, 2]))
```

```
true
false
```

### Exercise 5¶

```
function linapprox(f, a, b, n, x)
#=
Evaluates the piecewise linear interpolant of f at x on the interval
[a, b], with n evenly spaced grid points.
=#
length_of_interval = b - a
num_subintervals = n - 1
step = length_of_interval / num_subintervals
# === find first grid point larger than x === #
point = a
while point <= x
point += step
end
# === x must lie between the gridpoints (point - step) and point === #
u, v = point - step, point
return f(u) + (x - u) * (f(v) - f(u)) / (v - u)
end
```

Let’s test it

```
f_ex5(x) = x^2
g_ex5(x) = linapprox(f_ex5, -1, 1, 3, x)
```

```
using Plots
pyplot()
```

```
x_grid = linspace(-1, 1, 100)
y_vals = map(f_ex5, x_grid)
y_approx = map(g_ex5, x_grid)
plot(x_grid, y_vals, label="true")
plot!(x_grid, y_approx, label="approximation")
```

### Exercise 6¶

```
f_ex6 = open("us_cities.txt", "r")
total_pop = 0
for line in eachline(f_ex6)
city, population = split(line, ':') # Tuple unpacking
total_pop += parse(Int, population)
end
close(f_ex6)
println("Total population = $total_pop")
```

```
Total population = 23831986
```