1628263694
Build CLI Application to Detect Plagiarism in Source Code Files
A CLI Application to detect plagiarism in Source Code Files.
Prerequisites
Installation
- Clone this repository.
- Run the following in the terminal.
cd <path_to_repository>
python setup.py install
This will install the package on your local system.
Commands
- Check the version of Plag.
plag --version
- Comparing Source Code Files for similarity.
plag compare ../data/file1.cpp ../data --filetype cpp
- Extracting features of a Source Code file.
plag extract ../data/file1.cpp
- Handling user preferences
- Show current preferences
- Set a preference
- Reset all preferences
Issues
- In case of permission errors, try running the terminal as administrator.
Download Details:
Author: DivyanshFalodiya
Official Website: https://github.com/DivyanshFalodiya/plagiarism-detector
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Swift Tips: A Collection Useful Tips for The Swift Language
SwiftTips
The following is a collection of tips I find to be useful when working with the Swift language. More content is available on my Twitter account!
Property Wrappers as Debugging Tools
Property Wrappers allow developers to wrap properties with specific behaviors, that will be seamlessly triggered whenever the properties are accessed.
While their primary use case is to implement business logic within our apps, it's also possible to use Property Wrappers as debugging tools!
For example, we could build a wrapper called @History, that would be added to a property while debugging and would keep track of all the values set to this property.
import Foundation
@propertyWrapper
struct History<Value> {
private var value: Value
private(set) var history: [Value] = []
init(wrappedValue: Value) {
self.value = wrappedValue
}
var wrappedValue: Value {
get { value }
set {
history.append(value)
value = newValue
}
}
var projectedValue: Self {
return self
}
}
// We can then decorate our business code
// with the `@History` wrapper
struct User {
@History var name: String = ""
}
var user = User()
// All the existing call sites will still
// compile, without the need for any change
user.name = "John"
user.name = "Jane"
// But now we can also access an history of
// all the previous values!
user.$name.history // ["", "John"]
Localization through String interpolation
Swift 5 gave us the possibility to define our own custom String interpolation methods.
This feature can be used to power many use cases, but there is one that is guaranteed to make sense in most projects: localizing user-facing strings.
import Foundation
extension String.StringInterpolation {
mutating func appendInterpolation(localized key: String, _ args: CVarArg...) {
let localized = String(format: NSLocalizedString(key, comment: ""), arguments: args)
appendLiteral(localized)
}
}
/*
Let's assume that this is the content of our Localizable.strings:
"welcome.screen.greetings" = "Hello %@!";
*/
let userName = "John"
print("\(localized: "welcome.screen.greetings", userName)") // Hello John!
Implementing pseudo-inheritance between structs
If youโve always wanted to use some kind of inheritance mechanism for your structs, Swift 5.1 is going to make you very happy!
Using the new KeyPath-based dynamic member lookup, you can implement some pseudo-inheritance, where a type inherits the API of another one ๐
(However, be careful, Iโm definitely not advocating inheritance as a go-to solution ๐)
import Foundation
protocol Inherits {
associatedtype SuperType
var `super`: SuperType { get }
}
extension Inherits {
subscript<T>(dynamicMember keyPath: KeyPath<SuperType, T>) -> T {
return self.`super`[keyPath: keyPath]
}
}
struct Person {
let name: String
}
@dynamicMemberLookup
struct User: Inherits {
let `super`: Person
let login: String
let password: String
}
let user = User(super: Person(name: "John Appleseed"), login: "Johnny", password: "1234")
user.name // "John Appleseed"
user.login // "Johnny"
Composing NSAttributedString through a Function Builder
Swift 5.1 introduced Function Builders: a great tool for building custom DSL syntaxes, like SwiftUI. However, one doesn't need to be building a full-fledged DSL in order to leverage them.
For example, it's possible to write a simple Function Builder, whose job will be to compose together individual instances of NSAttributedString through a nicer syntax than the standard API.
import UIKit
@_functionBuilder
class NSAttributedStringBuilder {
static func buildBlock(_ components: NSAttributedString...) -> NSAttributedString {
let result = NSMutableAttributedString(string: "")
return components.reduce(into: result) { (result, current) in result.append(current) }
}
}
extension NSAttributedString {
class func composing(@NSAttributedStringBuilder _ parts: () -> NSAttributedString) -> NSAttributedString {
return parts()
}
}
let result = NSAttributedString.composing {
NSAttributedString(string: "Hello",
attributes: [.font: UIFont.systemFont(ofSize: 24),
.foregroundColor: UIColor.red])
NSAttributedString(string: " world!",
attributes: [.font: UIFont.systemFont(ofSize: 20),
.foregroundColor: UIColor.orange])
}
Using switch and if as expressions
Contrary to other languages, like Kotlin, Swift does not allow switch and if to be used as expressions. Meaning that the following code is not valid Swift:
let constant = if condition {
someValue
} else {
someOtherValue
}
A common solution to this problem is to wrap the if or switch statement within a closure, that will then be immediately called. While this approach does manage to achieve the desired goal, it makes for a rather poor syntax.
To avoid the ugly trailing () and improve on the readability, you can define a resultOf function, that will serve the exact same purpose, in a more elegant way.
import Foundation
func resultOf<T>(_ code: () -> T) -> T {
return code()
}
let randomInt = Int.random(in: 0...3)
let spelledOut: String = resultOf {
switch randomInt {
case 0:
return "Zero"
case 1:
return "One"
case 2:
return "Two"
case 3:
return "Three"
default:
return "Out of range"
}
}
print(spelledOut)
Avoiding double negatives within guard statements
A guard statement is a very convenient way for the developer to assert that a condition is met, in order for the execution of the program to keep going.
However, since the body of a guard statement is meant to be executed when the condition evaluates to false, the use of the negation (!) operator within the condition of a guard statement can make the code hard to read, as it becomes a double negative.
A nice trick to avoid such double negatives is to encapsulate the use of the ! operator within a new property or function, whose name does not include a negative.
import Foundation
extension Collection {
var hasElements: Bool {
return !isEmpty
}
}
let array = Bool.random() ? [1, 2, 3] : []
guard array.hasElements else { fatalError("array was empty") }
print(array)
Defining a custom init without loosing the compiler-generated one
It's common knowledge for Swift developers that, when you define a struct, the compiler is going to automatically generate a memberwise init for you. That is, unless you also define an init of your own. Because then, the compiler won't generate any memberwise init.
Yet, there are many instances where we might enjoy the opportunity to get both. As it turns out, this goal is quite easy to achieve: you just need to define your own init in an extension rather than inside the type definition itself.
import Foundation
struct Point {
let x: Int
let y: Int
}
extension Point {
init() {
x = 0
y = 0
}
}
let usingDefaultInit = Point(x: 4, y: 3)
let usingCustomInit = Point()
Implementing a namespace through an empty enum
Swift does not really have an out-of-the-box support of namespaces. One could argue that a Swift module can be seen as a namespace, but creating a dedicated Framework for this sole purpose can legitimately be regarded as overkill.
Some developers have taken the habit to use a struct which only contains static fields to implement a namespace. While this does the job, it requires us to remember to implement an empty private init(), because it wouldn't make sense for such a struct to be instantiated.
It's actually possible to take this approach one step further, by replacing the struct with an enum. While it might seem weird to have an enum with no case, it's actually a very idiomatic way to declare a type that cannot be instantiated.
import Foundation
enum NumberFormatterProvider {
static var currencyFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .currency
formatter.roundingIncrement = 0.01
return formatter
}
static var decimalFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .decimal
formatter.decimalSeparator = ","
return formatter
}
}
NumberFormatterProvider() // โ impossible to instantiate by mistake
NumberFormatterProvider.currencyFormatter.string(from: 2.456) // $2.46
NumberFormatterProvider.decimalFormatter.string(from: 2.456) // 2,456
Using Never to represent impossible code paths
Never is quite a peculiar type in the Swift Standard Library: it is defined as an empty enum enum Never { }.
While this might seem odd at first glance, it actually yields a very interesting property: it makes it a type that cannot be constructed (i.e. it possesses no instances).
This way, Never can be used as a generic parameter to let the compiler know that a particular feature will not be used.
import Foundation
enum Result<Value, Error> {
case success(value: Value)
case failure(error: Error)
}
func willAlwaysSucceed(_ completion: @escaping ((Result<String, Never>) -> Void)) {
completion(.success(value: "Call was successful"))
}
willAlwaysSucceed( { result in
switch result {
case .success(let value):
print(value)
// the compiler knows that the `failure` case cannot happen
// so it doesn't require us to handle it.
}
})
Providing a default value to a Decodable enum
Swift's Codable framework does a great job at seamlessly decoding entities from a JSON stream. However, when we integrate web-services, we are sometimes left to deal with JSONs that require behaviors that Codable does not provide out-of-the-box.
For instance, we might have a string-based or integer-based enum, and be required to set it to a default value when the data found in the JSON does not match any of its cases.
We might be tempted to implement this via an extensive switch statement over all the possible cases, but there is a much shorter alternative through the initializer init?(rawValue:):
import Foundation
enum State: String, Decodable {
case active
case inactive
case undefined
init(from decoder: Decoder) throws {
let container = try decoder.singleValueContainer()
let decodedString = try container.decode(String.self)
self = State(rawValue: decodedString) ?? .undefined
}
}
let data = """
["active", "inactive", "foo"]
""".data(using: .utf8)!
let decoded = try! JSONDecoder().decode([State].self, from: data)
print(decoded) // [State.active, State.inactive, State.undefined]
Another lightweight dependency injection through default values for function parameters
Dependency injection boils down to a simple idea: when an object requires a dependency, it shouldn't create it by itself, but instead it should be given a function that does it for him.
Now the great thing with Swift is that, not only can a function take another function as a parameter, but that parameter can also be given a default value.
When you combine both those features, you can end up with a dependency injection pattern that is both lightweight on boilerplate, but also type safe.
import Foundation
protocol Service {
func call() -> String
}
class ProductionService: Service {
func call() -> String {
return "This is the production"
}
}
class MockService: Service {
func call() -> String {
return "This is a mock"
}
}
typealias Provider<T> = () -> T
class Controller {
let service: Service
init(serviceProvider: Provider<Service> = { return ProductionService() }) {
self.service = serviceProvider()
}
func work() {
print(service.call())
}
}
let productionController = Controller()
productionController.work() // prints "This is the production"
let mockedController = Controller(serviceProvider: { return MockService() })
mockedController.work() // prints "This is a mock"
Lightweight dependency injection through protocol-oriented programming
Singletons are pretty bad. They make your architecture rigid and tightly coupled, which then results in your code being hard to test and refactor. Instead of using singletons, your code should rely on dependency injection, which is a much more architecturally sound approach.
But singletons are so easy to use, and dependency injection requires us to do extra-work. So maybe, for simple situations, we could find an in-between solution?
One possible solution is to rely on one of Swift's most know features: protocol-oriented programming. Using a protocol, we declare and access our dependency. We then store it in a private singleton, and perform the injection through an extension of said protocol.
This way, our code will indeed be decoupled from its dependency, while at the same time keeping the boilerplate to a minimum.
import Foundation
protocol Formatting {
var formatter: NumberFormatter { get }
}
private let sharedFormatter: NumberFormatter = {
let sharedFormatter = NumberFormatter()
sharedFormatter.numberStyle = .currency
return sharedFormatter
}()
extension Formatting {
var formatter: NumberFormatter { return sharedFormatter }
}
class ViewModel: Formatting {
var displayableAmount: String?
func updateDisplay(to amount: Double) {
displayableAmount = formatter.string(for: amount)
}
}
let viewModel = ViewModel()
viewModel.updateDisplay(to: 42000.45)
viewModel.displayableAmount // "$42,000.45"
Getting rid of overabundant [weak self] and guard
Callbacks are a part of almost all iOS apps, and as frameworks such as RxSwift keep gaining in popularity, they become ever more present in our codebase.
Seasoned Swift developers are aware of the potential memory leaks that @escaping callbacks can produce, so they make real sure to always use [weak self], whenever they need to use self inside such a context. And when they need to have self be non-optional, they then add a guard statement along.
Consequently, this syntax of a [weak self] followed by a guard rapidly tends to appear everywhere in the codebase. The good thing is that, through a little protocol-oriented trick, it's actually possible to get rid of this tedious syntax, without loosing any of its benefits!
import Foundation
import PlaygroundSupport
PlaygroundPage.current.needsIndefiniteExecution = true
protocol Weakifiable: class { }
extension Weakifiable {
func weakify(_ code: @escaping (Self) -> Void) -> () -> Void {
return { [weak self] in
guard let self = self else { return }
code(self)
}
}
func weakify<T>(_ code: @escaping (T, Self) -> Void) -> (T) -> Void {
return { [weak self] arg in
guard let self = self else { return }
code(arg, self)
}
}
}
extension NSObject: Weakifiable { }
class Producer: NSObject {
deinit {
print("deinit Producer")
}
private var handler: (Int) -> Void = { _ in }
func register(handler: @escaping (Int) -> Void) {
self.handler = handler
DispatchQueue.main.asyncAfter(deadline: .now() + 1.0, execute: { self.handler(42) })
}
}
class Consumer: NSObject {
deinit {
print("deinit Consumer")
}
let producer = Producer()
func consume() {
producer.register(handler: weakify { result, strongSelf in
strongSelf.handle(result)
})
}
private func handle(_ result: Int) {
print("๐ \(result)")
}
}
var consumer: Consumer? = Consumer()
consumer?.consume()
DispatchQueue.main.asyncAfter(deadline: .now() + 2.0, execute: { consumer = nil })
// This code prints:
// ๐ 42
// deinit Consumer
// deinit Producer
Solving callback hell with function composition
Asynchronous functions are a big part of iOS APIs, and most developers are familiar with the challenge they pose when one needs to sequentially call several asynchronous APIs.
This often results in callbacks being nested into one another, a predicament often referred to as callback hell.
Many third-party frameworks are able to tackle this issue, for instance RxSwift or PromiseKit. Yet, for simple instances of the problem, there is no need to use such big guns, as it can actually be solved with simple function composition.
import Foundation
typealias CompletionHandler<Result> = (Result?, Error?) -> Void
infix operator ~>: MultiplicationPrecedence
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ second: @escaping (T, CompletionHandler<U>) -> Void) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ firstResult, error in
guard let firstResult = firstResult else { completion(nil, error); return }
second(firstResult, { (secondResult, error) in
completion(secondResult, error)
})
})
}
}
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ transform: @escaping (T) -> U) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ result, error in
guard let result = result else { completion(nil, error); return }
completion(transform(result), nil)
})
}
}
func service1(_ completionHandler: CompletionHandler<Int>) {
completionHandler(42, nil)
}
func service2(arg: String, _ completionHandler: CompletionHandler<String>) {
completionHandler("๐ \(arg)", nil)
}
let chainedServices = service1
~> { int in return String(int / 2) }
~> service2
chainedServices({ result, _ in
guard let result = result else { return }
print(result) // Prints: ๐ 21
})
Transform an asynchronous function into a synchronous one
Asynchronous functions are a great way to deal with future events without blocking a thread. Yet, there are times where we would like them to behave in exactly such a blocking way.
Think about writing unit tests and using mocked network calls. You will need to add complexity to your test in order to deal with asynchronous functions, whereas synchronous ones would be much easier to manage.
Thanks to Swift proficiency in the functional paradigm, it is possible to write a function whose job is to take an asynchronous function and transform it into a synchronous one.
import Foundation
func makeSynchrone<A, B>(_ asyncFunction: @escaping (A, (B) -> Void) -> Void) -> (A) -> B {
return { arg in
let lock = NSRecursiveLock()
var result: B? = nil
asyncFunction(arg) {
result = $0
lock.unlock()
}
lock.lock()
return result!
}
}
func myAsyncFunction(arg: Int, completionHandler: (String) -> Void) {
completionHandler("๐ \(arg)")
}
let syncFunction = makeSynchrone(myAsyncFunction)
print(syncFunction(42)) // prints ๐ 42
Using KeyPaths instead of closures
Closures are a great way to interact with generic APIs, for instance APIs that allow to manipulate data structures through the use of generic functions, such as filter() or sorted().
The annoying part is that closures tend to clutter your code with many instances of {, } and $0, which can quickly undermine its readably.
A nice alternative for a cleaner syntax is to use a KeyPath instead of a closure, along with an operator that will deal with transforming the provided KeyPath in a closure.
import Foundation
prefix operator ^
prefix func ^ <Element, Attribute>(_ keyPath: KeyPath<Element, Attribute>) -> (Element) -> Attribute {
return { element in element[keyPath: keyPath] }
}
struct MyData {
let int: Int
let string: String
}
let data = [MyData(int: 2, string: "Foo"), MyData(int: 4, string: "Bar")]
data.map(^\.int) // [2, 4]
data.map(^\.string) // ["Foo", "Bar"]
Bringing some type-safety to a userInfo Dictionary
Many iOS APIs still rely on a userInfo Dictionary to handle use-case specific data. This Dictionary usually stores untyped values, and is declared as follows: [String: Any] (or sometimes [AnyHashable: Any].
Retrieving data from such a structure will involve some conditional casting (via the as? operator), which is prone to both errors and repetitions. Yet, by introducing a custom subscript, it's possible to encapsulate all the tedious logic, and end-up with an easier and more robust API.
import Foundation
typealias TypedUserInfoKey<T> = (key: String, type: T.Type)
extension Dictionary where Key == String, Value == Any {
subscript<T>(_ typedKey: TypedUserInfoKey<T>) -> T? {
return self[typedKey.key] as? T
}
}
let userInfo: [String : Any] = ["Foo": 4, "Bar": "forty-two"]
let integerTypedKey = TypedUserInfoKey(key: "Foo", type: Int.self)
let intValue = userInfo[integerTypedKey] // returns 4
type(of: intValue) // returns Int?
let stringTypedKey = TypedUserInfoKey(key: "Bar", type: String.self)
let stringValue = userInfo[stringTypedKey] // returns "forty-two"
type(of: stringValue) // returns String?
Lightweight data-binding for an MVVM implementation
MVVM is a great pattern to separate business logic from presentation logic. The main challenge to make it work, is to define a mechanism for the presentation layer to be notified of model updates.
RxSwift is a perfect choice to solve such a problem. Yet, some developers don't feel confortable with leveraging a third-party library for such a central part of their architecture.
For those situation, it's possible to define a lightweight Variable type, that will make the MVVM pattern very easy to use!
import Foundation
class Variable<Value> {
var value: Value {
didSet {
onUpdate?(value)
}
}
var onUpdate: ((Value) -> Void)? {
didSet {
onUpdate?(value)
}
}
init(_ value: Value, _ onUpdate: ((Value) -> Void)? = nil) {
self.value = value
self.onUpdate = onUpdate
self.onUpdate?(value)
}
}
let variable: Variable<String?> = Variable(nil)
variable.onUpdate = { data in
if let data = data {
print(data)
}
}
variable.value = "Foo"
variable.value = "Bar"
// prints:
// Foo
// Bar
Using typealias to its fullest
The keyword typealias allows developers to give a new name to an already existing type. For instance, Swift defines Void as a typealias of (), the empty tuple.
But a less known feature of this mechanism is that it allows to assign concrete types for generic parameters, or to rename them. This can help make the semantics of generic types much clearer, when used in specific use cases.
import Foundation
enum Either<Left, Right> {
case left(Left)
case right(Right)
}
typealias Result<Value> = Either<Value, Error>
typealias IntOrString = Either<Int, String>
Writing an interruptible overload of forEach
Iterating through objects via the forEach(_:) method is a great alternative to the classic for loop, as it allows our code to be completely oblivious of the iteration logic. One limitation, however, is that forEach(_:) does not allow to stop the iteration midway.
Taking inspiration from the Objective-C implementation, we can write an overload that will allow the developer to stop the iteration, if needed.
import Foundation
extension Sequence {
func forEach(_ body: (Element, _ stop: inout Bool) throws -> Void) rethrows {
var stop = false
for element in self {
try body(element, &stop)
if stop {
return
}
}
}
}
["Foo", "Bar", "FooBar"].forEach { element, stop in
print(element)
stop = (element == "Bar")
}
// Prints:
// Foo
// Bar
Optimizing the use of reduce()
Functional programing is a great way to simplify a codebase. For instance, reduce is an alternative to the classic for loop, without most the boilerplate. Unfortunately, simplicity often comes at the price of performance.
Consider that you want to remove duplicate values from a Sequence. While reduce() is a perfectly fine way to express this computation, the performance will be sub optimal, because of all the unnecessary Array copying that will happen every time its closure gets called.
That's when reduce(into:_:) comes into play. This version of reduce leverages the capacities of copy-on-write type (such as Array or Dictionnary) in order to avoid unnecessary copying, which results in a great performance boost.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
let data = (1...1_000).map { _ in Int(arc4random_uniform(256)) }
// runs in 0.63s
time {
let noDuplicates: [Int] = data.reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
// runs in 0.15s
time {
let noDuplicates: [Int] = data.reduce(into: [], { if !$0.contains($1) { $0.append($1) } } )
}
Avoiding hardcoded reuse identifiers
UI components such as UITableView and UICollectionView rely on reuse identifiers in order to efficiently recycle the views they display. Often, those reuse identifiers take the form of a static hardcoded String, that will be used for every instance of their class.
Through protocol-oriented programing, it's possible to avoid those hardcoded values, and instead use the name of the type as a reuse identifier.
import Foundation
import UIKit
protocol Reusable {
static var reuseIdentifier: String { get }
}
extension Reusable {
static var reuseIdentifier: String {
return String(describing: self)
}
}
extension UITableViewCell: Reusable { }
extension UITableView {
func register<T: UITableViewCell>(_ class: T.Type) {
register(`class`, forCellReuseIdentifier: T.reuseIdentifier)
}
func dequeueReusableCell<T: UITableViewCell>(for indexPath: IndexPath) -> T {
return dequeueReusableCell(withIdentifier: T.reuseIdentifier, for: indexPath) as! T
}
}
class MyCell: UITableViewCell { }
let tableView = UITableView()
tableView.register(MyCell.self)
let myCell: MyCell = tableView.dequeueReusableCell(for: [0, 0])
Defining a union type
The C language has a construct called union, that allows a single variable to hold values from different types. While Swift does not provide such a construct, it provides enums with associated values, which allows us to define a type called Either that implements a union of two types.
import Foundation
enum Either<A, B> {
case left(A)
case right(B)
func either(ifLeft: ((A) -> Void)? = nil, ifRight: ((B) -> Void)? = nil) {
switch self {
case let .left(a):
ifLeft?(a)
case let .right(b):
ifRight?(b)
}
}
}
extension Bool { static func random() -> Bool { return arc4random_uniform(2) == 0 } }
var intOrString: Either<Int, String> = Bool.random() ? .left(2) : .right("Foo")
intOrString.either(ifLeft: { print($0 + 1) }, ifRight: { print($0 + "Bar") })
If you're interested by this kind of data structure, I strongly recommend that you learn more about Algebraic Data Types.
Asserting that classes have associated NIBs and vice-versa
Most of the time, when we create a .xib file, we give it the same name as its associated class. From that, if we later refactor our code and rename such a class, we run the risk of forgetting to rename the associated .xib.
While the error will often be easy to catch, if the .xib is used in a remote section of its app, it might go unnoticed for sometime. Fortunately it's possible to build custom test predicates that will assert that 1) for a given class, there exists a .nib with the same name in a given Bundle, 2) for all the .nib in a given Bundle, there exists a class with the same name.
import XCTest
public func XCTAssertClassHasNib(_ class: AnyClass, bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
let associatedNibURL = bundle.url(forResource: String(describing: `class`), withExtension: "nib")
XCTAssertNotNil(associatedNibURL, "Class \"\(`class`)\" has no associated nib file", file: file, line: line)
}
public func XCTAssertNibHaveClasses(_ bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
guard let bundleName = bundle.infoDictionary?["CFBundleName"] as? String,
let basePath = bundle.resourcePath,
let enumerator = FileManager.default.enumerator(at: URL(fileURLWithPath: basePath),
includingPropertiesForKeys: nil,
options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) else { return }
var nibFilesURLs = [URL]()
for case let fileURL as URL in enumerator {
if fileURL.pathExtension.uppercased() == "NIB" {
nibFilesURLs.append(fileURL)
}
}
nibFilesURLs.map { $0.lastPathComponent }
.compactMap { $0.split(separator: ".").first }
.map { String($0) }
.forEach {
let associatedClass: AnyClass? = bundle.classNamed("\(bundleName).\($0)")
XCTAssertNotNil(associatedClass, "File \"\($0).nib\" has no associated class", file: file, line: line)
}
}
XCTAssertClassHasNib(MyFirstTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertClassHasNib(MySecondTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertNibHaveClasses(Bundle(for: AppDelegate.self))
Many thanks Benjamin Lavialle for coming up with the idea behind the second test predicate.
Small footprint type-erasing with functions
Seasoned Swift developers know it: a protocol with associated type (PAT) "can only be used as a generic constraint because it has Self or associated type requirements". When we really need to use a PAT to type a variable, the goto workaround is to use a type-erased wrapper.
While this solution works perfectly, it requires a fair amount of boilerplate code. In instances where we are only interested in exposing one particular function of the PAT, a shorter approach using function types is possible.
import Foundation
import UIKit
protocol Configurable {
associatedtype Model
func configure(with model: Model)
}
typealias Configurator<Model> = (Model) -> ()
extension UILabel: Configurable {
func configure(with model: String) {
self.text = model
}
}
let label = UILabel()
let configurator: Configurator<String> = label.configure
configurator("Foo")
label.text // "Foo"
Performing animations sequentially
UIKit exposes a very powerful and simple API to perform view animations. However, this API can become a little bit quirky to use when we want to perform animations sequentially, because it involves nesting closure within one another, which produces notoriously hard to maintain code.
Nonetheless, it's possible to define a rather simple class, that will expose a really nicer API for this particular use case ๐
import Foundation
import UIKit
class AnimationSequence {
typealias Animations = () -> Void
private let current: Animations
private let duration: TimeInterval
private var next: AnimationSequence? = nil
init(animations: @escaping Animations, duration: TimeInterval) {
self.current = animations
self.duration = duration
}
@discardableResult func append(animations: @escaping Animations, duration: TimeInterval) -> AnimationSequence {
var lastAnimation = self
while let nextAnimation = lastAnimation.next {
lastAnimation = nextAnimation
}
lastAnimation.next = AnimationSequence(animations: animations, duration: duration)
return self
}
func run() {
UIView.animate(withDuration: duration, animations: current, completion: { finished in
if finished, let next = self.next {
next.run()
}
})
}
}
var firstView = UIView()
var secondView = UIView()
firstView.alpha = 0
secondView.alpha = 0
AnimationSequence(animations: { firstView.alpha = 1.0 }, duration: 1)
.append(animations: { secondView.alpha = 1.0 }, duration: 0.5)
.append(animations: { firstView.alpha = 0.0 }, duration: 2.0)
.run()
Debouncing a function call
Debouncing is a very useful tool when dealing with UI inputs. Consider a search bar, whose content is used to query an API. It wouldn't make sense to perform a request for every character the user is typing, because as soon as a new character is entered, the result of the previous request has become irrelevant.
Instead, our code will perform much better if we "debounce" the API call, meaning that we will wait until some delay has passed, without the input being modified, before actually performing the call.
import Foundation
func debounced(delay: TimeInterval, queue: DispatchQueue = .main, action: @escaping (() -> Void)) -> () -> Void {
var workItem: DispatchWorkItem?
return {
workItem?.cancel()
workItem = DispatchWorkItem(block: action)
queue.asyncAfter(deadline: .now() + delay, execute: workItem!)
}
}
let debouncedPrint = debounced(delay: 1.0) { print("Action performed!") }
debouncedPrint()
debouncedPrint()
debouncedPrint()
// After a 1 second delay, this gets
// printed only once to the console:
// Action performed!
Providing useful operators for Optional booleans
When we need to apply the standard boolean operators to Optional booleans, we often end up with a syntax unnecessarily crowded with unwrapping operations. By taking a cue from the world of three-valued logics, we can define a couple operators that make working with Bool? values much nicer.
import Foundation
func && (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (false, _), (_, false):
return false
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs && unwrapRhs
default:
return nil
}
}
func || (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (true, _), (_, true):
return true
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs || unwrapRhs
default:
return nil
}
}
false && nil // false
true && nil // nil
[true, nil, false].reduce(true, &&) // false
nil || true // true
nil || false // nil
[true, nil, false].reduce(false, ||) // true
Removing duplicate values from a Sequence
Transforming a Sequence in order to remove all the duplicate values it contains is a classic use case. To implement it, one could be tempted to transform the Sequence into a Set, then back to an Array. The downside with this approach is that it will not preserve the order of the sequence, which can definitely be a dealbreaker. Using reduce() it is possible to provide a concise implementation that preserves ordering:
import Foundation
extension Sequence where Element: Equatable {
func duplicatesRemoved() -> [Element] {
return reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
}
let data = [2, 5, 2, 3, 6, 5, 2]
data.duplicatesRemoved() // [2, 5, 3, 6]
Shorter syntax to deal with optional strings
Optional strings are very common in Swift code, for instance many objects from UIKit expose the text they display as a String?. Many times you will need to manipulate this data as an unwrapped String, with a default value set to the empty string for nil cases.
While the nil-coalescing operator (e.g. ??) is a perfectly fine way to a achieve this goal, defining a computed variable like orEmpty can help a lot in cleaning the syntax.
import Foundation
import UIKit
extension Optional where Wrapped == String {
var orEmpty: String {
switch self {
case .some(let value):
return value
case .none:
return ""
}
}
}
func doesNotWorkWithOptionalString(_ param: String) {
// do something with `param`
}
let label = UILabel()
label.text = "This is some text."
doesNotWorkWithOptionalString(label.text.orEmpty)
Encapsulating background computation and UI update
Every seasoned iOS developers knows it: objects from UIKit can only be accessed from the main thread. Any attempt to access them from a background thread is a guaranteed crash.
Still, running a costly computation on the background, and then using it to update the UI can be a common pattern.
In such cases you can rely on asyncUI to encapsulate all the boilerplate code.
import Foundation
import UIKit
func asyncUI<T>(_ computation: @autoclosure @escaping () -> T, qos: DispatchQoS.QoSClass = .userInitiated, _ completion: @escaping (T) -> Void) {
DispatchQueue.global(qos: qos).async {
let value = computation()
DispatchQueue.main.async {
completion(value)
}
}
}
let label = UILabel()
func costlyComputation() -> Int { return (0..<10_000).reduce(0, +) }
asyncUI(costlyComputation()) { value in
label.text = "\(value)"
}
Retrieving all the necessary data to build a debug view
A debug view, from which any controller of an app can be instantiated and pushed on the navigation stack, has the potential to bring some real value to a development process. A requirement to build such a view is to have a list of all the classes from a given Bundle that inherit from UIViewController. With the following extension, retrieving this list becomes a piece of cake ๐ฐ
import Foundation
import UIKit
import ObjectiveC
extension Bundle {
func viewControllerTypes() -> [UIViewController.Type] {
guard let bundlePath = self.executablePath else { return [] }
var size: UInt32 = 0
var rawClassNames: UnsafeMutablePointer<UnsafePointer<Int8>>!
var parsedClassNames = [String]()
rawClassNames = objc_copyClassNamesForImage(bundlePath, &size)
for index in 0..<size {
let className = rawClassNames[Int(index)]
if let name = NSString.init(utf8String:className) as String?,
NSClassFromString(name) is UIViewController.Type {
parsedClassNames.append(name)
}
}
return parsedClassNames
.sorted()
.compactMap { NSClassFromString($0) as? UIViewController.Type }
}
}
// Fetch all view controller types in UIKit
Bundle(for: UIViewController.self).viewControllerTypes()
I share the credit for this tip with Benoรฎt Caron.
Defining a function to map over dictionaries
Update As it turns out, map is actually a really bad name for this function, because it does not preserve composition of transformations, a property that is required to fit the definition of a real map function.
Surprisingly enough, the standard library doesn't define a map() function for dictionaries that allows to map both keys and values into a new Dictionary. Nevertheless, such a function can be helpful, for instance when converting data across different frameworks.
import Foundation
extension Dictionary {
func map<T: Hashable, U>(_ transform: (Key, Value) throws -> (T, U)) rethrows -> [T: U] {
var result: [T: U] = [:]
for (key, value) in self {
let (transformedKey, transformedValue) = try transform(key, value)
result[transformedKey] = transformedValue
}
return result
}
}
let data = [0: 5, 1: 6, 2: 7]
data.map { ("\($0)", $1 * $1) } // ["2": 49, "0": 25, "1": 36]
A shorter syntax to remove nil values
Swift provides the function compactMap(), that can be used to remove nil values from a Sequence of optionals when calling it with an argument that just returns its parameter (i.e. compactMap { $0 }). Still, for such use cases it would be nice to get rid of the trailing closure.
The implementation isn't as straightforward as your usual extension, but once it has been written, the call site definitely gets cleaner ๐
import Foundation
protocol OptionalConvertible {
associatedtype Wrapped
func asOptional() -> Wrapped?
}
extension Optional: OptionalConvertible {
func asOptional() -> Wrapped? {
return self
}
}
extension Sequence where Element: OptionalConvertible {
func compacted() -> [Element.Wrapped] {
return compactMap { $0.asOptional() }
}
}
let data = [nil, 1, 2, nil, 3, 5, nil, 8, nil]
data.compacted() // [1, 2, 3, 5, 8]
Dealing with expirable values
It might happen that your code has to deal with values that come with an expiration date. In a game, it could be a score multiplier that will only last for 30 seconds. Or it could be an authentication token for an API, with a 15 minutes lifespan. In both instances you can rely on the type Expirable to encapsulate the expiration logic.
import Foundation
struct Expirable<T> {
private var innerValue: T
private(set) var expirationDate: Date
var value: T? {
return hasExpired() ? nil : innerValue
}
init(value: T, expirationDate: Date) {
self.innerValue = value
self.expirationDate = expirationDate
}
init(value: T, duration: Double) {
self.innerValue = value
self.expirationDate = Date().addingTimeInterval(duration)
}
func hasExpired() -> Bool {
return expirationDate < Date()
}
}
let expirable = Expirable(value: 42, duration: 3)
sleep(2)
expirable.value // 42
sleep(2)
expirable.value // nil
I share the credit for this tip with Benoรฎt Caron.
Using parallelism to speed-up map()
Almost all Apple devices able to run Swift code are powered by a multi-core CPU, consequently making a good use of parallelism is a great way to improve code performance. map() is a perfect candidate for such an optimization, because it is almost trivial to define a parallel implementation.
import Foundation
extension Array {
func parallelMap<T>(_ transform: (Element) -> T) -> [T] {
let res = UnsafeMutablePointer<T>.allocate(capacity: count)
DispatchQueue.concurrentPerform(iterations: count) { i in
res[i] = transform(self[i])
}
let finalResult = Array<T>(UnsafeBufferPointer(start: res, count: count))
res.deallocate(capacity: count)
return finalResult
}
}
let array = (0..<1_000).map { $0 }
func work(_ n: Int) -> Int {
return (0..<n).reduce(0, +)
}
array.parallelMap { work($0) }
๐จ Make sure to only use parallelMap() when the transform function actually performs some costly computations. Otherwise performances will be systematically slower than using map(), because of the multithreading overhead.
Measuring execution time with minimum boilerplate
During development of a feature that performs some heavy computations, it can be helpful to measure just how much time a chunk of code takes to run. The time() function is a nice tool for this purpose, because of how simple it is to add and then to remove when it is no longer needed.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
time {
(0...10_000).map { $0 * $0 }
}
// time: 0.183973908424377
Running two pieces of code in parallel
Concurrency is definitely one of those topics were the right encapsulation bears the potential to make your life so much easier. For instance, with this piece of code you can easily launch two computations in parallel, and have the results returned in a tuple.
import Foundation
func parallel<T, U>(_ left: @autoclosure () -> T, _ right: @autoclosure () -> U) -> (T, U) {
var leftRes: T?
var rightRes: U?
DispatchQueue.concurrentPerform(iterations: 2, execute: { id in
if id == 0 {
leftRes = left()
} else {
rightRes = right()
}
})
return (leftRes!, rightRes!)
}
let values = (1...100_000).map { $0 }
let results = parallel(values.map { $0 * $0 }, values.reduce(0, +))
Making good use of #file, #line and #function
Swift exposes three special variables #file, #line and #function, that are respectively set to the name of the current file, line and function. Those variables become very useful when writing custom logging functions or test predicates.
import Foundation
func log(_ message: String, _ file: String = #file, _ line: Int = #line, _ function: String = #function) {
print("[\(file):\(line)] \(function) - \(message)")
}
func foo() {
log("Hello world!")
}
foo() // [MyPlayground.playground:8] foo() - Hello world!
Comparing Optionals through Conditional Conformance
Swift 4.1 has introduced a new feature called Conditional Conformance, which allows a type to implement a protocol only when its generic type also does.
With this addition it becomes easy to let Optional implement Comparable only when Wrapped also implements Comparable:
import Foundation
extension Optional: Comparable where Wrapped: Comparable {
public static func < (lhs: Optional, rhs: Optional) -> Bool {
switch (lhs, rhs) {
case let (lhs?, rhs?):
return lhs < rhs
case (nil, _?):
return true // anything is greater than nil
case (_?, nil):
return false // nil in smaller than anything
case (nil, nil):
return true // nil is not smaller than itself
}
}
}
let data: [Int?] = [8, 4, 3, nil, 12, 4, 2, nil, -5]
data.sorted() // [nil, nil, Optional(-5), Optional(2), Optional(3), Optional(4), Optional(4), Optional(8), Optional(12)]
Safely subscripting a Collection
Any attempt to access an Array beyond its bounds will result in a crash. While it's possible to write conditions such as if index < array.count { array[index] } in order to prevent such crashes, this approach will rapidly become cumbersome.
A great thing is that this condition can be encapsulated in a custom subscript that will work on any Collection:
import Foundation
extension Collection {
subscript (safe index: Index) -> Element? {
return indices.contains(index) ? self[index] : nil
}
}
let data = [1, 3, 4]
data[safe: 1] // Optional(3)
data[safe: 10] // nil
Easier String slicing using ranges
Subscripting a string with a range can be very cumbersome in Swift 4. Let's face it, no one wants to write lines like someString[index(startIndex, offsetBy: 0)..<index(startIndex, offsetBy: 10)] on a regular basis.
Luckily, with the addition of one clever extension, strings can be sliced as easily as arrays ๐
import Foundation
extension String {
public subscript(value: CountableClosedRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: CountableRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeUpTo<Int>) -> Substring {
get {
return self[..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeThrough<Int>) -> Substring {
get {
return self[...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeFrom<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...]
}
}
}
let data = "This is a string!"
data[..<4] // "This"
data[5..<9] // "is a"
data[10...] // "string!"
Concise syntax for sorting using a KeyPath
By using a KeyPath along with a generic type, a very clean and concise syntax for sorting data can be implemented:
import Foundation
extension Sequence {
func sorted<T: Comparable>(by attribute: KeyPath<Element, T>) -> [Element] {
return sorted(by: { $0[keyPath: attribute] < $1[keyPath: attribute] })
}
}
let data = ["Some", "words", "of", "different", "lengths"]
data.sorted(by: \.count) // ["of", "Some", "words", "lengths", "different"]
If you like this syntax, make sure to checkout KeyPathKit!
Manufacturing cache-efficient versions of pure functions
By capturing a local variable in a returned closure, it is possible to manufacture cache-efficient versions of pure functions. Be careful though, this trick only works with non-recursive function!
import Foundation
func cached<In: Hashable, Out>(_ f: @escaping (In) -> Out) -> (In) -> Out {
var cache = [In: Out]()
return { (input: In) -> Out in
if let cachedValue = cache[input] {
return cachedValue
} else {
let result = f(input)
cache[input] = result
return result
}
}
}
let cachedCos = cached { (x: Double) in cos(x) }
cachedCos(.pi * 2) // value of cos for 2ฯ is now cached
Simplifying complex conditions with pattern matching
When distinguishing between complex boolean conditions, using a switch statement along with pattern matching can be more readable than the classic series of if {} else if {}.
import Foundation
let expr1: Bool
let expr2: Bool
let expr3: Bool
if expr1 && !expr3 {
functionA()
} else if !expr2 && expr3 {
functionB()
} else if expr1 && !expr2 && expr3 {
functionC()
}
switch (expr1, expr2, expr3) {
case (true, _, false):
functionA()
case (_, false, true):
functionB()
case (true, false, true):
functionC()
default:
break
}
Easily generating arrays of data
Using map() on a range makes it easy to generate an array of data.
import Foundation
func randomInt() -> Int { return Int(arc4random()) }
let randomArray = (1...10).map { _ in randomInt() }
Using @autoclosure for cleaner call sites
Using @autoclosure enables the compiler to automatically wrap an argument within a closure, thus allowing for a very clean syntax at call sites.
import UIKit
extension UIView {
class func animate(withDuration duration: TimeInterval, _ animations: @escaping @autoclosure () -> Void) {
UIView.animate(withDuration: duration, animations: animations)
}
}
let view = UIView()
UIView.animate(withDuration: 0.3, view.backgroundColor = .orange)
Observing new and old value with RxSwift
When working with RxSwift, it's very easy to observe both the current and previous value of an observable sequence by simply introducing a shift using skip().
import RxSwift
let values = Observable.of(4, 8, 15, 16, 23, 42)
let newAndOld = Observable.zip(values, values.skip(1)) { (previous: $0, current: $1) }
.subscribe(onNext: { pair in
print("current: \(pair.current) - previous: \(pair.previous)")
})
//current: 8 - previous: 4
//current: 15 - previous: 8
//current: 16 - previous: 15
//current: 23 - previous: 16
//current: 42 - previous: 23
Implicit initialization from literal values
Using protocols such as ExpressibleByStringLiteral it is possible to provide an init that will be automatically when a literal value is provided, allowing for nice and short syntax. This can be very helpful when writing mock or test data.
import Foundation
extension URL: ExpressibleByStringLiteral {
public init(stringLiteral value: String) {
self.init(string: value)!
}
}
let url: URL = "http://www.google.fr"
NSURLConnection.canHandle(URLRequest(url: "http://www.google.fr"))
Achieving systematic validation of data
Through some clever use of Swift private visibility it is possible to define a container that holds any untrusted value (such as a user input) from which the only way to retrieve the value is by making it successfully pass a validation test.
import Foundation
struct Untrusted<T> {
private(set) var value: T
}
protocol Validator {
associatedtype T
static func validation(value: T) -> Bool
}
extension Validator {
static func validate(untrusted: Untrusted<T>) -> T? {
if self.validation(value: untrusted.value) {
return untrusted.value
} else {
return nil
}
}
}
struct FrenchPhoneNumberValidator: Validator {
static func validation(value: String) -> Bool {
return (value.count) == 10 && CharacterSet(charactersIn: value).isSubset(of: CharacterSet.decimalDigits)
}
}
let validInput = Untrusted(value: "0122334455")
let invalidInput = Untrusted(value: "0123")
FrenchPhoneNumberValidator.validate(untrusted: validInput) // returns "0122334455"
FrenchPhoneNumberValidator.validate(untrusted: invalidInput) // returns nil
Implementing the builder pattern with keypaths
With the addition of keypaths in Swift 4, it is now possible to easily implement the builder pattern, that allows the developer to clearly separate the code that initializes a value from the code that uses it, without the burden of defining a factory method.
import UIKit
protocol With {}
extension With where Self: AnyObject {
@discardableResult
func with<T>(_ property: ReferenceWritableKeyPath<Self, T>, setTo value: T) -> Self {
self[keyPath: property] = value
return self
}
}
extension UIView: With {}
let view = UIView()
let label = UILabel()
.with(\.textColor, setTo: .red)
.with(\.text, setTo: "Foo")
.with(\.textAlignment, setTo: .right)
.with(\.layer.cornerRadius, setTo: 5)
view.addSubview(label)
๐จ The Swift compiler does not perform OS availability checks on properties referenced by keypaths. Any attempt to use a KeyPath for an unavailable property will result in a runtime crash.
I share the credit for this tip with Marion Curtil.
Storing functions rather than values
When a type stores values for the sole purpose of parametrizing its functions, itโs then possible to not store the values but directly the function, with no discernable difference at the call site.
import Foundation
struct MaxValidator {
let max: Int
let strictComparison: Bool
func isValid(_ value: Int) -> Bool {
return self.strictComparison ? value < self.max : value <= self.max
}
}
struct MaxValidator2 {
var isValid: (_ value: Int) -> Bool
init(max: Int, strictComparison: Bool) {
self.isValid = strictComparison ? { $0 < max } : { $0 <= max }
}
}
MaxValidator(max: 5, strictComparison: true).isValid(5) // false
MaxValidator2(max: 5, strictComparison: false).isValid(5) // true
Defining operators on function types
Functions are first-class citizen types in Swift, so it is perfectly legal to define operators for them.
import Foundation
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
func ||(_ lhs: @escaping (Int) -> Bool, _ rhs: @escaping (Int) -> Bool) -> (Int) -> Bool {
return { value in
return lhs(value) || rhs(value)
}
}
(firstRange || secondRange)(2) // true
(firstRange || secondRange)(4) // false
(firstRange || secondRange)(6) // true
Typealiases for functions
Typealiases are great to express function signatures in a more comprehensive manner, which then enables us to easily define functions that operate on them, resulting in a nice way to write and use some powerful API.
import Foundation
typealias RangeSet = (Int) -> Bool
func union(_ left: @escaping RangeSet, _ right: @escaping RangeSet) -> RangeSet {
return { left($0) || right($0) }
}
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
let unionRange = union(firstRange, secondRange)
unionRange(2) // true
unionRange(4) // false
Encapsulating state within a function
By returning a closure that captures a local variable, it's possible to encapsulate a mutable state within a function.
import Foundation
func counterFactory() -> () -> Int {
var counter = 0
return {
counter += 1
return counter
}
}
let counter = counterFactory()
counter() // returns 1
counter() // returns 2
Generating all cases for an Enum
โ ๏ธ Since Swift 4.2,
allCasescan now be synthesized at compile-time by simply conforming to the protocolCaseIterable. The implementation below should no longer be used in production code.
Through some clever leveraging of how enums are stored in memory, it is possible to generate an array that contains all the possible cases of an enum. This can prove particularly useful when writing unit tests that consume random data.
import Foundation
enum MyEnum { case first; case second; case third; case fourth }
protocol EnumCollection: Hashable {
static var allCases: [Self] { get }
}
extension EnumCollection {
public static var allCases: [Self] {
var i = 0
return Array(AnyIterator {
let next = withUnsafePointer(to: &i) {
$0.withMemoryRebound(to: Self.self, capacity: 1) { $0.pointee }
}
if next.hashValue != i { return nil }
i += 1
return next
})
}
}
extension MyEnum: EnumCollection { }
MyEnum.allCases // [.first, .second, .third, .fourth]
Using map on optional values
The if-let syntax is a great way to deal with optional values in a safe manner, but at times it can prove to be just a little bit to cumbersome. In such cases, using the Optional.map() function is a nice way to achieve a shorter code while retaining safeness and readability.
import UIKit
let date: Date? = Date() // or could be nil, doesn't matter
let formatter = DateFormatter()
let label = UILabel()
if let safeDate = date {
label.text = formatter.string(from: safeDate)
}
label.text = date.map { return formatter.string(from: $0) }
label.text = date.map(formatter.string(from:)) // even shorter, tough less readable
๐ฃ NEW ๐ฃ Swift Tips are now available on YouTube ๐
Summary
- #57 Property Wrappers as Debugging Tools
- #56 Localization through
Stringinterpolation - #55 Implementing pseudo-inheritance between
structs - #54 Composing
NSAttributedStringthrough a Function Builder - #53 Using
switchandifas expressions - #52 Avoiding double negatives within
guardstatements - #51 Defining a custom
initwithout loosing the compiler-generated one - #50 Implementing a namespace through an empty
enum - #49 Using
Neverto represent impossible code paths - #48 Providing a default value to a
Decodableenum - #47 Another lightweight dependency injection through default values for function parameters
- #46 Lightweight dependency injection through protocol-oriented programming
- #45 Getting rid of overabundant
[weak self]andguard - #44 Solving callback hell with function composition
- #43 Transform an asynchronous function into a synchronous one
- #42 Using KeyPaths instead of closures
- #41 Bringing some type-safety to a
userInfoDictionary - #40 Lightweight data-binding for an MVVM implementation
- #39 Using
typealiasto its fullest - #38 Writing an interruptible overload of
forEach - #37 Optimizing the use of
reduce() - #36 Avoiding hardcoded reuse identifiers
- #35 Defining a union type
- #34 Asserting that classes have associated NIBs and vice-versa
- #33 Small footprint type-erasing with functions
- #32 Performing animations sequentially
- #31 Debouncing a function call
- #30 Providing useful operators for
Optionalbooleans - #29 Removing duplicate values from a
Sequence - #28 Shorter syntax to deal with optional strings
- #27 Encapsulating background computation and UI update
- #26 Retrieving all the necessary data to build a debug view
- #25 Defining a function to map over dictionaries
- #24 A shorter syntax to remove
nilvalues - #23 Dealing with expirable values
- #22 Using parallelism to speed-up
map() - #21 Measuring execution time with minimum boilerplate
- #20 Running two pieces of code in parallel
- #19 Making good use of #file, #line and #function
- #18 Comparing Optionals through Conditional Conformance
- #17 Safely subscripting a Collection
- #16 Easier String slicing using ranges
- #15 Concise syntax for sorting using a KeyPath
- #14 Manufacturing cache-efficient versions of pure functions
- #13 Simplifying complex condition with pattern matching
- #12 Easily generating arrays of data
- #11 Using @autoclosure for cleaner call sites
- #10 Observing new and old value with RxSwift
- #09 Implicit initialization from literal values
- #08 Achieving systematic validation of data
- #07 Implementing the builder pattern with keypaths
- #06 Storing functions rather than values
- #05 Defining operators on function types
- #04 Typealiases for functions
- #03 Encapsulating state within a function
- #02 Generating all cases for an Enum
- #01 Using map on optional values
Tips
Download Details:
Author: vincent-pradeilles
Source code: https://github.com/vincent-pradeilles/swift-tips
License: MIT license
#swift
1642405260
Build an Android application with Kivy Python framework
If youโre a Python developer thinking about getting started with mobile development, then the Kivy framework is your best bet. With Kivy, you can develop platform-independent applications that compile for iOS, Android, Windows, macOS, and Linux. In this article, weโll cover Android specifically because it is the most used.
Weโll build a simple random number generator app that you can install on your phone and test when you are done. To follow along with this article, you should be familiar with Python. Letโs get started!
Getting started with Kivy
First, youโll need a new directory for your app. Make sure you have Python installed on your machine and open a new Python file. Youโll need to install the Kivy module from your terminal using either of the commands below. To avoid any package conflicts, be sure youโre installing Kivy in a virtual environment:
pip install kivy
//
pip3 install kivy Once you have installed Kivy, you should see a success message from your terminal that looks like the screenshots below:
Kivy installation
Successful Kivy installation
Next, navigate into your project folder. In the main.py file, weโll need to import the Kivy module and specify which version we want. You can use Kivy v2.0.0, but if you have a smartphone that is older than Android 8.0, I recommend using Kivy v1.9.0. You can mess around with the different versions during the build to see the differences in features and performance.
Add the version number right after the import kivy line as follows:
kivy.require('1.9.0')Now, weโll create a class that will basically define our app; Iโll name mine RandomNumber. This class will inherit the app class from Kivy. Therefore, you need to import the app by adding from kivy.app import App:
class RandomNumber(App): In the RandomNumber class, youโll need to add a function called build, which takes a self parameter. To actually return the UI, weโll use the build function. For now, I have it returned as a simple label. To do so, youโll need to import Label using the line from kivy.uix.label import Label:
import kivy
from kivy.app import App
from kivy.uix.label import Label
class RandomNumber(App):
def build(self):
return Label(text="Random Number Generator")Now, our app skeleton is complete! Before moving forward, you should create an instance of the RandomNumber class and run it in your terminal or IDE to see the interface:
import kivy from kivy.app import App from kivy.uix.label import Label class RandomNumber(App): def build(self): return Label(text="Random Number Generator") randomApp = RandomNumber() randomApp.run()
When you run the class instance with the text Random Number Generator, you should see a simple interface or window that looks like the screenshot below:
Simple interface after running the code
You wonโt be able to run the text on Android until youโve finished building the whole thing.
Outsourcing the interface
Next, weโll need a way to outsource the interface. First, weโll create a Kivy file in our directory that will house most of our design work. Youโll want to name this file the same name as your class using lowercase letters and a .kv extension. Kivy will automatically associate the class name and the file name, but it may not work on Android if they are exactly the same.
Inside that .kv file, you need to specify the layout for your app, including elements like the label, buttons, forms, etc. To keep this demonstration simple, Iโll add a label for the title Random Number, a label that will serve as a placeholder for the random number that is generated _, and a Generate button that calls the generate function.
My .kv file looks like the code below, but you can mess around with the different values to fit your requirements:
<boxLayout>:
orientation: "vertical"
Label:
text: "Random Number"
font_size: 30
color: 0, 0.62, 0.96
Label:
text: "_"
font_size: 30
Button:
text: "Generate"
font_size: 15 In the main.py file, you no longer need the Label import statement because the Kivy file takes care of your UI. However, you do need to import boxlayout, which you will use in the Kivy file.
In your main file, you need to add the import statement and edit your main.py file to read return BoxLayout() in the build method:
from kivy.uix.boxlayout import BoxLayoutIf you run the command above, you should see a simple interface that has the random number title, the _ place holder, and the clickable generate button:
Random Number app rendered
Notice that you didnโt have to import anything for the Kivy file to work. Basically, when you run the app, it returns boxlayout by looking for a file inside the Kivy file with the same name as your class. Keep in mind, this is a simple interface, and you can make your app as robust as you want. Be sure to check out the Kv language documentation.
Generate the random number function
Now that our app is almost done, weโll need a simple function to generate random numbers when a user clicks the generate button, then render that random number into the app interface. To do so, weโll need to change a few things in our files.
First, weโll import the module that weโll use to generate a random number with import random. Then, weโll create a function or method that calls the generated number. For this demonstration, Iโll use a range between 0 and 2000. Generating the random number is simple with the random.randint(0, 2000) command. Weโll add this into our code in a moment.
Next, weโll create another class that will be our own version of the box layout. Our class will have to inherit the box layout class, which houses the method to generate random numbers and render them on the interface:
class MyRoot(BoxLayout):
def __init__(self):
super(MyRoot, self).__init__()Within that class, weโll create the generate method, which will not only generate random numbers but also manipulate the label that controls what is displayed as the random number in the Kivy file.
To accommodate this method, weโll first need to make changes to the .kv file . Since the MyRoot class has inherited the box layout, you can make MyRoot the top level element in your .kv file:
<MyRoot>:
BoxLayout:
orientation: "vertical"
Label:
text: "Random Number"
font_size: 30
color: 0, 0.62, 0.96
Label:
text: "_"
font_size: 30
Button:
text: "Generate"
font_size: 15Notice that you are still keeping all your UI specifications indented in the Box Layout. After this, you need to add an ID to the label that will hold the generated numbers, making it easy to manipulate when the generate function is called. You need to specify the relationship between the ID in this file and another in the main code at the top, just before the BoxLayout line:
<MyRoot>:
random_label: random_label
BoxLayout:
orientation: "vertical"
Label:
text: "Random Number"
font_size: 30
color: 0, 0.62, 0.96
Label:
id: random_label
text: "_"
font_size: 30
Button:
text: "Generate"
font_size: 15The random_label: random_label line basically means that the label with the ID random_label will be mapped to random_label in the main.py file, meaning that any action that manipulates random_label will be mapped on the label with the specified name.
We can now create the method to generate the random number in the main file:
def generate_number(self):
self.random_label.text = str(random.randint(0, 2000))
# notice how the class method manipulates the text attributre of the random label by a# ssigning it a new random number generate by the 'random.randint(0, 2000)' funcion. S# ince this the random number generated is an integer, typecasting is required to make # it a string otherwise you will get a typeError in your terminal when you run it.The MyRoot class should look like the code below:
class MyRoot(BoxLayout):
def __init__(self):
super(MyRoot, self).__init__()
def generate_number(self):
self.random_label.text = str(random.randint(0, 2000))Congratulations! Youโre now done with the main file of the app. The only thing left to do is make sure that you call this function when the generate button is clicked. You need only add the line on_press: root.generate_number() to the button selection part of your .kv file:
<MyRoot>:
random_label: random_label
BoxLayout:
orientation: "vertical"
Label:
text: "Random Number"
font_size: 30
color: 0, 0.62, 0.96
Label:
id: random_label
text: "_"
font_size: 30
Button:
text: "Generate"
font_size: 15
on_press: root.generate_number()Now, you can run the app.
Compiling our app on Android
Before compiling our app on Android, I have some bad news for Windows users. Youโll need Linux or macOS to compile your Android application. However, you donโt need to have a separate Linux distribution, instead, you can use a virtual machine.
To compile and generate a full Android .apk application, weโll use a tool called Buildozer. Letโs install Buildozer through our terminal using one of the commands below:
pip3 install buildozer
//
pip install buildozerNow, weโll install some of Buildozerโs required dependencies. I am on Linux Ergo, so Iโll use Linux-specific commands. You should execute these commands one by one:
sudo apt update
sudo apt install -y git zip unzip openjdk-13-jdk python3-pip autoconf libtool pkg-config zlib1g-dev libncurses5-dev libncursesw5-dev libtinfo5 cmake libffi-dev libssl-dev
pip3 install --upgrade Cython==0.29.19 virtualenv
# add the following line at the end of your ~/.bashrc file
export PATH=$PATH:~/.local/bin/After executing the specific commands, run buildozer init. You should see an output similar to the screenshot below:
Buildozer successful initialization
The command above creates a Buildozer .spec file, which you can use to make specifications to your app, including the name of the app, the icon, etc. The .spec file should look like the code block below:
[app]
# (str) Title of your application
title = My Application
# (str) Package name
package.name = myapp
# (str) Package domain (needed for android/ios packaging)
package.domain = org.test
# (str) Source code where the main.py live
source.dir = .
# (list) Source files to include (let empty to include all the files)
source.include_exts = py,png,jpg,kv,atlas
# (list) List of inclusions using pattern matching
#source.include_patterns = assets/*,images/*.png
# (list) Source files to exclude (let empty to not exclude anything)
#source.exclude_exts = spec
# (list) List of directory to exclude (let empty to not exclude anything)
#source.exclude_dirs = tests, bin
# (list) List of exclusions using pattern matching
#source.exclude_patterns = license,images/*/*.jpg
# (str) Application versioning (method 1)
version = 0.1
# (str) Application versioning (method 2)
# version.regex = __version__ = \['"\](.*)['"]
# version.filename = %(source.dir)s/main.py
# (list) Application requirements
# comma separated e.g. requirements = sqlite3,kivy
requirements = python3,kivy
# (str) Custom source folders for requirements
# Sets custom source for any requirements with recipes
# requirements.source.kivy = ../../kivy
# (list) Garden requirements
#garden_requirements =
# (str) Presplash of the application
#presplash.filename = %(source.dir)s/data/presplash.png
# (str) Icon of the application
#icon.filename = %(source.dir)s/data/icon.png
# (str) Supported orientation (one of landscape, sensorLandscape, portrait or all)
orientation = portrait
# (list) List of service to declare
#services = NAME:ENTRYPOINT_TO_PY,NAME2:ENTRYPOINT2_TO_PY
#
# OSX Specific
#
#
# author = ยฉ Copyright Info
# change the major version of python used by the app
osx.python_version = 3
# Kivy version to use
osx.kivy_version = 1.9.1
#
# Android specific
#
# (bool) Indicate if the application should be fullscreen or not
fullscreen = 0
# (string) Presplash background color (for new android toolchain)
# Supported formats are: #RRGGBB #AARRGGBB or one of the following names:
# red, blue, green, black, white, gray, cyan, magenta, yellow, lightgray,
# darkgray, grey, lightgrey, darkgrey, aqua, fuchsia, lime, maroon, navy,
# olive, purple, silver, teal.
#android.presplash_color = #FFFFFF
# (list) Permissions
#android.permissions = INTERNET
# (int) Target Android API, should be as high as possible.
#android.api = 27
# (int) Minimum API your APK will support.
#android.minapi = 21
# (int) Android SDK version to use
#android.sdk = 20
# (str) Android NDK version to use
#android.ndk = 19b
# (int) Android NDK API to use. This is the minimum API your app will support, it should usually match android.minapi.
#android.ndk_api = 21
# (bool) Use --private data storage (True) or --dir public storage (False)
#android.private_storage = True
# (str) Android NDK directory (if empty, it will be automatically downloaded.)
#android.ndk_path =
# (str) Android SDK directory (if empty, it will be automatically downloaded.)
#android.sdk_path =
# (str) ANT directory (if empty, it will be automatically downloaded.)
#android.ant_path =
# (bool) If True, then skip trying to update the Android sdk
# This can be useful to avoid excess Internet downloads or save time
# when an update is due and you just want to test/build your package
# android.skip_update = False
# (bool) If True, then automatically accept SDK license
# agreements. This is intended for automation only. If set to False,
# the default, you will be shown the license when first running
# buildozer.
# android.accept_sdk_license = False
# (str) Android entry point, default is ok for Kivy-based app
#android.entrypoint = org.renpy.android.PythonActivity
# (str) Android app theme, default is ok for Kivy-based app
# android.apptheme = "@android:style/Theme.NoTitleBar"
# (list) Pattern to whitelist for the whole project
#android.whitelist =
# (str) Path to a custom whitelist file
#android.whitelist_src =
# (str) Path to a custom blacklist file
#android.blacklist_src =
# (list) List of Java .jar files to add to the libs so that pyjnius can access
# their classes. Don't add jars that you do not need, since extra jars can slow
# down the build process. Allows wildcards matching, for example:
# OUYA-ODK/libs/*.jar
#android.add_jars = foo.jar,bar.jar,path/to/more/*.jar
# (list) List of Java files to add to the android project (can be java or a
# directory containing the files)
#android.add_src =
# (list) Android AAR archives to add (currently works only with sdl2_gradle
# bootstrap)
#android.add_aars =
# (list) Gradle dependencies to add (currently works only with sdl2_gradle
# bootstrap)
#android.gradle_dependencies =
# (list) add java compile options
# this can for example be necessary when importing certain java libraries using the 'android.gradle_dependencies' option
# see https://developer.android.com/studio/write/java8-support for further information
# android.add_compile_options = "sourceCompatibility = 1.8", "targetCompatibility = 1.8"
# (list) Gradle repositories to add {can be necessary for some android.gradle_dependencies}
# please enclose in double quotes
# e.g. android.gradle_repositories = "maven { url 'https://kotlin.bintray.com/ktor' }"
#android.add_gradle_repositories =
# (list) packaging options to add
# see https://google.github.io/android-gradle-dsl/current/com.android.build.gradle.internal.dsl.PackagingOptions.html
# can be necessary to solve conflicts in gradle_dependencies
# please enclose in double quotes
# e.g. android.add_packaging_options = "exclude 'META-INF/common.kotlin_module'", "exclude 'META-INF/*.kotlin_module'"
#android.add_gradle_repositories =
# (list) Java classes to add as activities to the manifest.
#android.add_activities = com.example.ExampleActivity
# (str) OUYA Console category. Should be one of GAME or APP
# If you leave this blank, OUYA support will not be enabled
#android.ouya.category = GAME
# (str) Filename of OUYA Console icon. It must be a 732x412 png image.
#android.ouya.icon.filename = %(source.dir)s/data/ouya_icon.png
# (str) XML file to include as an intent filters in <activity> tag
#android.manifest.intent_filters =
# (str) launchMode to set for the main activity
#android.manifest.launch_mode = standard
# (list) Android additional libraries to copy into libs/armeabi
#android.add_libs_armeabi = libs/android/*.so
#android.add_libs_armeabi_v7a = libs/android-v7/*.so
#android.add_libs_arm64_v8a = libs/android-v8/*.so
#android.add_libs_x86 = libs/android-x86/*.so
#android.add_libs_mips = libs/android-mips/*.so
# (bool) Indicate whether the screen should stay on
# Don't forget to add the WAKE_LOCK permission if you set this to True
#android.wakelock = False
# (list) Android application meta-data to set (key=value format)
#android.meta_data =
# (list) Android library project to add (will be added in the
# project.properties automatically.)
#android.library_references =
# (list) Android shared libraries which will be added to AndroidManifest.xml using <uses-library> tag
#android.uses_library =
# (str) Android logcat filters to use
#android.logcat_filters = *:S python:D
# (bool) Copy library instead of making a libpymodules.so
#android.copy_libs = 1
# (str) The Android arch to build for, choices: armeabi-v7a, arm64-v8a, x86, x86_64
android.arch = armeabi-v7a
# (int) overrides automatic versionCode computation (used in build.gradle)
# this is not the same as app version and should only be edited if you know what you're doing
# android.numeric_version = 1
#
# Python for android (p4a) specific
#
# (str) python-for-android fork to use, defaults to upstream (kivy)
#p4a.fork = kivy
# (str) python-for-android branch to use, defaults to master
#p4a.branch = master
# (str) python-for-android git clone directory (if empty, it will be automatically cloned from github)
#p4a.source_dir =
# (str) The directory in which python-for-android should look for your own build recipes (if any)
#p4a.local_recipes =
# (str) Filename to the hook for p4a
#p4a.hook =
# (str) Bootstrap to use for android builds
# p4a.bootstrap = sdl2
# (int) port number to specify an explicit --port= p4a argument (eg for bootstrap flask)
#p4a.port =
#
# iOS specific
#
# (str) Path to a custom kivy-ios folder
#ios.kivy_ios_dir = ../kivy-ios
# Alternately, specify the URL and branch of a git checkout:
ios.kivy_ios_url = https://github.com/kivy/kivy-ios
ios.kivy_ios_branch = master
# Another platform dependency: ios-deploy
# Uncomment to use a custom checkout
#ios.ios_deploy_dir = ../ios_deploy
# Or specify URL and branch
ios.ios_deploy_url = https://github.com/phonegap/ios-deploy
ios.ios_deploy_branch = 1.7.0
# (str) Name of the certificate to use for signing the debug version
# Get a list of available identities: buildozer ios list_identities
#ios.codesign.debug = "iPhone Developer: <lastname> <firstname> (<hexstring>)"
# (str) Name of the certificate to use for signing the release version
#ios.codesign.release = %(ios.codesign.debug)s
[buildozer]
# (int) Log level (0 = error only, 1 = info, 2 = debug (with command output))
log_level = 2
# (int) Display warning if buildozer is run as root (0 = False, 1 = True)
warn_on_root = 1
# (str) Path to build artifact storage, absolute or relative to spec file
# build_dir = ./.buildozer
# (str) Path to build output (i.e. .apk, .ipa) storage
# bin_dir = ./bin
# -----------------------------------------------------------------------------
# List as sections
#
# You can define all the "list" as [section:key].
# Each line will be considered as a option to the list.
# Let's take [app] / source.exclude_patterns.
# Instead of doing:
#
#[app]
#source.exclude_patterns = license,data/audio/*.wav,data/images/original/*
#
# This can be translated into:
#
#[app:source.exclude_patterns]
#license
#data/audio/*.wav
#data/images/original/*
#
# -----------------------------------------------------------------------------
# Profiles
#
# You can extend section / key with a profile
# For example, you want to deploy a demo version of your application without
# HD content. You could first change the title to add "(demo)" in the name
# and extend the excluded directories to remove the HD content.
#
#[app@demo]
#title = My Application (demo)
#
#[app:source.exclude_patterns@demo]
#images/hd/*
#
# Then, invoke the command line with the "demo" profile:
#
#buildozer --profile demo android debugIf you want to specify things like the icon, requirements, loading screen, etc., you should edit this file. After making all the desired edits to your application, run buildozer -v android debug from your app directory to build and compile your application. This may take a while, especially if you have a slow machine.
After the process is done, your terminal should have some logs, one confirming that the build was successful:
Android successful build
You should also have an APK version of your app in your bin directory. This is the application executable that you will install and run on your phone:
Android .apk in the bin directory
Conclusion
Congratulations! If you have followed this tutorial step by step, you should have a simple random number generator app on your phone. Play around with it and tweak some values, then rebuild. Running the rebuild will not take as much time as the first build.
As you can see, building a mobile application with Python is fairly straightforward, as long as you are familiar with the framework or module you are working with. Regardless, the logic is executed the same way.
Get familiar with the Kivy module and itโs widgets. You can never know everything all at once. You only need to find a project and get your feet wet as early as possible. Happy coding.
Link: https://blog.logrocket.com/build-android-application-kivy-python-framework/
1599633600
GitHub Arctic Code Vault: Overview
Are you an Arctic Code Vault Contributor or have seen someone posting about it and donโt know what it is. So letโs take a look at what is an Arctic Code Vault Contributor and who are the ones who gets this batch.
GitHub, the worldโs largest open-source platform for software and programs has safely locked the data of huge value and magnitude in a coal mine in Longyearbyenโs Norwegian town in the Arctic region.
Back in November 2019, GitHub Arctic Code Vault was first announced.
The GitHub Arctic Code Vault is a data repository preserved in the Arctic
World Archive (AWA), a very-long-term archival facility 250 meters deep in the permafrost of an Arctic mountain. The archive is located in a decommissioned coal mine in the Svalbard archipelago, closer to the North Pole than the Arctic Circle.
Last year, GitHub said that it plans to capture a snapshot of every active
public repository on 02/02/2020 and preserve that data in the Arctic
Code Vault.
The project began on February 2, when the firm took a snapshot of all of
GitHubโs active public repositories to store them in the vault. They initially intended to travel to Norway and personally escort the worldโs open-source technology to the Arctic but their plans were derailed by the global pandemic. Then, they had to wait until 8 Julyfor the Arctic Data Vault data to be deposited.
GitHub announced that the code was successfully deposited in the Arctic Code Vault on July 8, 2020. Over the past several months, GitHub worked
with its archive partners Piql to write the 21TB of GitHub repository data to 186 reels of piqlFilm (digital photosensitive archival film).
GitHubโs strategic software director, Julia Metcalf, has written a blog post
on the companyโs website notifying the completion of GitHubโs Archive Program on July 8th. Discussing the objective of the Archive Program, Metcalf wrote โOur mission is to preserve open-source software for future generations by storing your code in an archive built to last a thousand years.โ
The Arctic Code Vault is only a small part of the wider GitHub Archive
Program, however, which sees the company partner with the Long Now
Foundation, Internet Archive, Software Heritage Foundation, Microsoft
Research and others.
How the cold storage will last 1,000 years?
Svalbard has been regulated by the international Svalbard Treaty as a demilitarized zone. Home to the worldโs northernmost town, it is one of the most remote and geopolitically stable human habitations on Earth.
The AWA is a joint initiative between Norwegian state-owned mining company Store Norske Spitsbergen Kulkompani (SNSK) and very-long-term digital preservation provider Piql AS. AWA is devoted to archival storage in perpetuity. The film reels will be stored in a steel-walled container inside a sealed chamber within a decommissioned coal mine on the remote archipelago of Svalbard. The AWA already preserves historical and cultural data from Italy, Brazil, Norway, the Vatican, and many others.
Whatโs in the 02/02/2020 snapshot?
The 02/02/2020 snapshot archived in the GitHub Arctic Code Vault will
sweep up every active public GitHub repository, in addition to significant dormant repos.
The snapshot will include every repo with any commits between the announcement at GitHub Universe on November 13th and 02/02/2020,
every repo with at least 1 star and any commits from the year before the snapshot (02/03/2019 โ 02/02/2020), and every repo with at least 250 stars.
The snapshot will consist of the HEAD of the default branch of each repository, minus any binaries larger than 100KB in sizeโdepending on available space, repos with more stars may retain binaries. Each repository will be packaged as a single TAR file. For greater data density and integrity, most of the data will be stored QR-encoded and compressed. A human-readable index and guide will itemize the location of each repository and explain how to recover the data.
The company further shared that every reel of the archive includes a copy
of the โGuide to the GitHub Code Vaultโ in five languages, written with input from GitHubโs community and available at the Archive Programโs own GitHub repository.
#github #open-source #coding #open-source-contribution #contributing-to-open-source #github-arctic-code-vault #arctic-code-vault #arctic-code-vault-contributor
1646698200
Face Recognition with OpenCV and Python
Introduction
What is face recognition? Or what is recognition? When you look at an apple fruit, your mind immediately tells you that this is an apple fruit. This process, your mind telling you that this is an apple fruit is recognition in simple words. So what is face recognition then? I am sure you have guessed it right. When you look at your friend walking down the street or a picture of him, you recognize that he is your friend Paulo. Interestingly when you look at your friend or a picture of him you look at his face first before looking at anything else. Ever wondered why you do that? This is so that you can recognize him by looking at his face. Well, this is you doing face recognition.
But the real question is how does face recognition works? It is quite simple and intuitive. Take a real life example, when you meet someone first time in your life you don't recognize him, right? While he talks or shakes hands with you, you look at his face, eyes, nose, mouth, color and overall look. This is your mind learning or training for the face recognition of that person by gathering face data. Then he tells you that his name is Paulo. At this point your mind knows that the face data it just learned belongs to Paulo. Now your mind is trained and ready to do face recognition on Paulo's face. Next time when you will see Paulo or his face in a picture you will immediately recognize him. This is how face recognition work. The more you will meet Paulo, the more data your mind will collect about Paulo and especially his face and the better you will become at recognizing him.
Now the next question is how to code face recognition with OpenCV, after all this is the only reason why you are reading this article, right? OK then. You might say that our mind can do these things easily but to actually code them into a computer is difficult? Don't worry, it is not. Thanks to OpenCV, coding face recognition is as easier as it feels. The coding steps for face recognition are same as we discussed it in real life example above.
- Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
- Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
- Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.
OpenCV comes equipped with built in face recognizer, all you have to do is feed it the face data. It's that simple and this how it will look once we are done coding it.
OpenCV Face Recognizers
OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can use any of them by just changing a single line of code. Below are the names of those face recognizers and their OpenCV calls.
- EigenFaces Face Recognizer Recognizer -
cv2.face.createEigenFaceRecognizer() - FisherFaces Face Recognizer Recognizer -
cv2.face.createFisherFaceRecognizer() - Local Binary Patterns Histograms (LBPH) Face Recognizer -
cv2.face.createLBPHFaceRecognizer()
We have got three face recognizers but do you know which one to use and when? Or which one is better? I guess not. So why not go through a brief summary of each, what you say? I am assuming you said yes :) So let's dive into the theory of each.
EigenFaces Face Recognizer
This algorithm considers the fact that not all parts of a face are equally important and equally useful. When you look at some one you recognize him/her by his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other. So you are actually focusing on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and same is the case from nose to mouth. When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other. This is exactly how EigenFaces face recognizer works.
EigenFaces face recognizer looks at all the training images of all the persons as a whole and try to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components. This way it not only extracts the important components from the training data but also saves memory by discarding the less important components. These important components it extracts are called principal components. Below is an image showing the principal components extracted from a list of faces.
Principal Components 
source
You can see that principal components actually represent faces and these faces are called eigen faces and hence the name of the algorithm.
So this is how EigenFaces face recognizer trains itself (by extracting principal components). Remember, it also keeps a record of which principal component belongs to which person. One thing to note in above image is that Eigenfaces algorithm also considers illumination as an important component.
Later during recognition, when you feed a new image to the algorithm, it repeats the same process on that image as well. It extracts the principal component from that new image and compares that component with the list of components it stored during training and finds the component with the best match and returns the person label associated with that best match component.
Easy peasy, right? Next one is easier than this one.
FisherFaces Face Recognizer
This algorithm is an improved version of EigenFaces face recognizer. Eigenfaces face recognizer looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are not focusing on the features that discriminate one person from the other but the features that represent all the persons in the training data as a whole.
This approach has drawbacks, for example, images with sharp changes (like light changes which is not a useful feature at all) may dominate the rest of the images and you may end up with features that are from external source like light and are not useful for discrimination at all. In the end, your principal components will represent light changes and not the actual face features.
Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, it extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.
Below is an image of features extracted using Fisherfaces algorithm.
Fisher Faces 
source
You can see that features extracted actually represent faces and these faces are called fisher faces and hence the name of the algorithm.
One thing to note here is that even in Fisherfaces algorithm if multiple persons have images with sharp changes due to external sources like light they will dominate over other features and affect recognition accuracy.
Getting bored with this theory? Don't worry, only one face recognizer is left and then we will dive deep into the coding part.
Local Binary Patterns Histograms (LBPH) Face Recognizer
I wrote a detailed explaination on Local Binary Patterns Histograms in my previous article on face detection using local binary patterns histograms. So here I will just give a brief overview of how it works.
We know that Eigenfaces and Fisherfaces are both affected by light and in real life we can't guarantee perfect light conditions. LBPH face recognizer is an improvement to overcome this drawback.
Idea is to not look at the image as a whole instead find the local features of an image. LBPH alogrithm try to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.
Take a 3x3 window and move it one image, at each move (each local part of an image), compare the pixel at the center with its neighbor pixels. The neighbors with intensity value less than or equal to center pixel are denoted by 1 and others by 0. Then you read these 0/1 values under 3x3 window in a clockwise order and you will have a binary pattern like 11100011 and this pattern is local to some area of the image. You do this on whole image and you will have a list of local binary patterns.
LBP Labeling 
Now you get why this algorithm has Local Binary Patterns in its name? Because you get a list of local binary patterns. Now you may be wondering, what about the histogram part of the LBPH? Well after you get a list of local binary patterns, you convert each binary pattern into a decimal number (as shown in above image) and then you make a histogram of all of those values. A sample histogram looks like this.
Sample Histogram 
I guess this answers the question about histogram part. So in the end you will have one histogram for each face image in the training data set. That means if there were 100 images in training data set then LBPH will extract 100 histograms after training and store them for later recognition. Remember, algorithm also keeps track of which histogram belongs to which person.
Later during recognition, when you will feed a new image to the recognizer for recognition it will generate a histogram for that new image, compare that histogram with the histograms it already has, find the best match histogram and return the person label associated with that best match histogram.
Below is a list of faces and their respective local binary patterns images. You can see that the LBP images are not affected by changes in light conditions.
LBP Faces 
source
The theory part is over and now comes the coding part! Ready to dive into coding? Let's get into it then.
Coding Face Recognition with OpenCV
The Face Recognition process in this tutorial is divided into three steps.
- Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
- Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
- Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.
[There should be a visualization diagram for above steps here]
To detect faces, I will use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its Python coding.
Import Required Modules
Before starting the actual coding we need to import the required modules for coding. So let's import them first.
- cv2: is OpenCV module for Python which we will use for face detection and face recognition.
- os: We will use this Python module to read our training directories and file names.
- numpy: We will use this module to convert Python lists to numpy arrays as OpenCV face recognizers accept numpy arrays.
#import OpenCV module
import cv2
#import os module for reading training data directories and paths
import os
#import numpy to convert python lists to numpy arrays as
#it is needed by OpenCV face recognizers
import numpy as np
#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline
Training Data
The more images used in training the better. Normally a lot of images are used for training a face recognizer so that it can learn different looks of the same person, for example with glasses, without glasses, laughing, sad, happy, crying, with beard, without beard etc. To keep our tutorial simple we are going to use only 12 images for each person.
So our training data consists of total 2 persons with 12 images of each person. All training data is inside training-data folder. training-data folder contains one folder for each person and each folder is named with format sLabel (e.g. s1, s2) where label is actually the integer label assigned to that person. For example folder named s1 means that this folder contains images for person 1. The directory structure tree for training data is as follows:
training-data
|-------------- s1
| |-- 1.jpg
| |-- ...
| |-- 12.jpg
|-------------- s2
| |-- 1.jpg
| |-- ...
| |-- 12.jpg
The test-data folder contains images that we will use to test our face recognizer after it has been successfully trained.
As OpenCV face recognizer accepts labels as integers so we need to define a mapping between integer labels and persons actual names so below I am defining a mapping of persons integer labels and their respective names.
Note: As we have not assigned label 0 to any person so the mapping for label 0 is empty.
#there is no label 0 in our training data so subject name for index/label 0 is empty
subjects = ["", "Tom Cruise", "Shahrukh Khan"]
Prepare training data
You may be wondering why data preparation, right? Well, OpenCV face recognizer accepts data in a specific format. It accepts two vectors, one vector is of faces of all the persons and the second vector is of integer labels for each face so that when processing a face the face recognizer knows which person that particular face belongs too.
For example, if we had 2 persons and 2 images for each person.
PERSON-1 PERSON-2
img1 img1
img2 img2
Then the prepare data step will produce following face and label vectors.
FACES LABELS
person1_img1_face 1
person1_img2_face 1
person2_img1_face 2
person2_img2_face 2
Preparing data step can be further divided into following sub-steps.
- Read all the folder names of subjects/persons provided in training data folder. So for example, in this tutorial we have folder names:
s1, s2. - For each subject, extract label number. Do you remember that our folders have a special naming convention? Folder names follow the format
sLabelwhereLabelis an integer representing the label we have assigned to that subject. So for example, folder names1means that the subject has label 1, s2 means subject label is 2 and so on. The label extracted in this step is assigned to each face detected in the next step. - Read all the images of the subject, detect face from each image.
- Add each face to faces vector with corresponding subject label (extracted in above step) added to labels vector.
[There should be a visualization for above steps here]
Did you read my last article on face detection? No? Then you better do so right now because to detect faces, I am going to use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its coding. Below is the same code.
#function to detect face using OpenCV
def detect_face(img):
#convert the test image to gray image as opencv face detector expects gray images
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#load OpenCV face detector, I am using LBP which is fast
#there is also a more accurate but slow Haar classifier
face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')
#let's detect multiscale (some images may be closer to camera than others) images
#result is a list of faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
#if no faces are detected then return original img
if (len(faces) == 0):
return None, None
#under the assumption that there will be only one face,
#extract the face area
(x, y, w, h) = faces[0]
#return only the face part of the image
return gray[y:y+w, x:x+h], faces[0]
I am using OpenCV's LBP face detector. On line 4, I convert the image to grayscale because most operations in OpenCV are performed in gray scale, then on line 8 I load LBP face detector using cv2.CascadeClassifier class. After that on line 12 I use cv2.CascadeClassifier class' detectMultiScale method to detect all the faces in the image. on line 20, from detected faces I only pick the first face because in one image there will be only one face (under the assumption that there will be only one prominent face). As faces returned by detectMultiScale method are actually rectangles (x, y, width, height) and not actual faces images so we have to extract face image area from the main image. So on line 23 I extract face area from gray image and return both the face image area and face rectangle.
Now you have got a face detector and you know the 4 steps to prepare the data, so are you ready to code the prepare data step? Yes? So let's do it.
#this function will read all persons' training images, detect face from each image
#and will return two lists of exactly same size, one list
# of faces and another list of labels for each face
def prepare_training_data(data_folder_path):
#------STEP-1--------
#get the directories (one directory for each subject) in data folder
dirs = os.listdir(data_folder_path)
#list to hold all subject faces
faces = []
#list to hold labels for all subjects
labels = []
#let's go through each directory and read images within it
for dir_name in dirs:
#our subject directories start with letter 's' so
#ignore any non-relevant directories if any
if not dir_name.startswith("s"):
continue;
#------STEP-2--------
#extract label number of subject from dir_name
#format of dir name = slabel
#, so removing letter 's' from dir_name will give us label
label = int(dir_name.replace("s", ""))
#build path of directory containin images for current subject subject
#sample subject_dir_path = "training-data/s1"
subject_dir_path = data_folder_path + "/" + dir_name
#get the images names that are inside the given subject directory
subject_images_names = os.listdir(subject_dir_path)
#------STEP-3--------
#go through each image name, read image,
#detect face and add face to list of faces
for image_name in subject_images_names:
#ignore system files like .DS_Store
if image_name.startswith("."):
continue;
#build image path
#sample image path = training-data/s1/1.pgm
image_path = subject_dir_path + "/" + image_name
#read image
image = cv2.imread(image_path)
#display an image window to show the image
cv2.imshow("Training on image...", image)
cv2.waitKey(100)
#detect face
face, rect = detect_face(image)
#------STEP-4--------
#for the purpose of this tutorial
#we will ignore faces that are not detected
if face is not None:
#add face to list of faces
faces.append(face)
#add label for this face
labels.append(label)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
return faces, labels
I have defined a function that takes the path, where training subjects' folders are stored, as parameter. This function follows the same 4 prepare data substeps mentioned above.
(step-1) On line 8 I am using os.listdir method to read names of all folders stored on path passed to function as parameter. On line 10-13 I am defining labels and faces vectors.
(step-2) After that I traverse through all subjects' folder names and from each subject's folder name on line 27 I am extracting the label information. As folder names follow the sLabel naming convention so removing the letter s from folder name will give us the label assigned to that subject.
(step-3) On line 34, I read all the images names of of the current subject being traversed and on line 39-66 I traverse those images one by one. On line 53-54 I am using OpenCV's imshow(window_title, image) along with OpenCV's waitKey(interval) method to display the current image being traveresed. The waitKey(interval) method pauses the code flow for the given interval (milliseconds), I am using it with 100ms interval so that we can view the image window for 100ms. On line 57, I detect face from the current image being traversed.
(step-4) On line 62-66, I add the detected face and label to their respective vectors.
But a function can't do anything unless we call it on some data that it has to prepare, right? Don't worry, I have got data of two beautiful and famous celebrities. I am sure you will recognize them!
Let's call this function on images of these beautiful celebrities to prepare data for training of our Face Recognizer. Below is a simple code to do that.
#let's first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print("Preparing data...")
faces, labels = prepare_training_data("training-data")
print("Data prepared")
#print total faces and labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
Preparing data...
Data prepared
Total faces: 23
Total labels: 23
This was probably the boring part, right? Don't worry, the fun stuff is coming up next. It's time to train our own face recognizer so that once trained it can recognize new faces of the persons it was trained on. Read? Ok then let's train our face recognizer.
Train Face Recognizer
As we know, OpenCV comes equipped with three face recognizers.
- EigenFace Recognizer: This can be created with
cv2.face.createEigenFaceRecognizer() - FisherFace Recognizer: This can be created with
cv2.face.createFisherFaceRecognizer() - Local Binary Patterns Histogram (LBPH): This can be created with
cv2.face.LBPHFisherFaceRecognizer()
I am going to use LBPH face recognizer but you can use any face recognizer of your choice. No matter which of the OpenCV's face recognizer you use the code will remain the same. You just have to change one line, the face recognizer initialization line given below.
#create our LBPH face recognizer
face_recognizer = cv2.face.createLBPHFaceRecognizer()
#or use EigenFaceRecognizer by replacing above line with
#face_recognizer = cv2.face.createEigenFaceRecognizer()
#or use FisherFaceRecognizer by replacing above line with
#face_recognizer = cv2.face.createFisherFaceRecognizer()
Now that we have initialized our face recognizer and we also have prepared our training data, it's time to train the face recognizer. We will do that by calling the train(faces-vector, labels-vector) method of face recognizer.
#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))
Did you notice that instead of passing labels vector directly to face recognizer I am first converting it to numpy array? This is because OpenCV expects labels vector to be a numpy array.
Still not satisfied? Want to see some action? Next step is the real action, I promise!
Prediction
Now comes my favorite part, the prediction part. This is where we actually get to see if our algorithm is actually recognizing our trained subjects's faces or not. We will take two test images of our celeberities, detect faces from each of them and then pass those faces to our trained face recognizer to see if it recognizes them.
Below are some utility functions that we will use for drawing bounding box (rectangle) around face and putting celeberity name near the face bounding box.
#function to draw rectangle on image
#according to given (x, y) coordinates and
#given width and heigh
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
#function to draw text on give image starting from
#passed (x, y) coordinates.
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
First function draw_rectangle draws a rectangle on image based on passed rectangle coordinates. It uses OpenCV's built in function cv2.rectangle(img, topLeftPoint, bottomRightPoint, rgbColor, lineWidth) to draw rectangle. We will use it to draw a rectangle around the face detected in test image.
Second function draw_text uses OpenCV's built in function cv2.putText(img, text, startPoint, font, fontSize, rgbColor, lineWidth) to draw text on image.
Now that we have the drawing functions, we just need to call the face recognizer's predict(face) method to test our face recognizer on test images. Following function does the prediction for us.
#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the
#subject
def predict(test_img):
#make a copy of the image as we don't want to chang original image
img = test_img.copy()
#detect face from the image
face, rect = detect_face(img)
#predict the image using our face recognizer
label= face_recognizer.predict(face)
#get name of respective label returned by face recognizer
label_text = subjects[label]
#draw a rectangle around face detected
draw_rectangle(img, rect)
#draw name of predicted person
draw_text(img, label_text, rect[0], rect[1]-5)
return img
- line-6 read the test image
- line-7 detect face from test image
- line-11 recognize the face by calling face recognizer's
predict(face)method. This method will return a lable - line-12 get the name associated with the label
- line-16 draw rectangle around the detected face
- line-18 draw name of predicted subject above face rectangle
Now that we have the prediction function well defined, next step is to actually call this function on our test images and display those test images to see if our face recognizer correctly recognized them. So let's do it. This is what we have been waiting for.
print("Predicting images...")
#load test images
test_img1 = cv2.imread("test-data/test1.jpg")
test_img2 = cv2.imread("test-data/test2.jpg")
#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")
#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))
#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))
#display both images
cv2.imshow("Tom cruise test", predicted_img1)
cv2.imshow("Shahrukh Khan test", predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
Predicting images...
Prediction complete
wohooo! Is'nt it beautiful? Indeed, it is!
End Notes
Face Recognition is a fascinating idea to work on and OpenCV has made it extremely simple and easy for us to code it. It just takes a few lines of code to have a fully working face recognition application and we can switch between all three face recognizers with a single line of code change. It's that simple.
Although EigenFaces, FisherFaces and LBPH face recognizers are good but there are even better ways to perform face recognition like using Histogram of Oriented Gradients (HOGs) and Neural Networks. So the more advanced face recognition algorithms are now a days implemented using a combination of OpenCV and Machine learning. I have plans to write some articles on those more advanced methods as well, so stay tuned!
Download Details:
Author: informramiz
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License
1673365703
Swift-tips: A Collection Useful Tips for The Swift Language
SwiftTips
The following is a collection of tips I find to be useful when working with the Swift language. More content is available on my Twitter account!
๐ฃ NEW ๐ฃ Swift Tips are now available on YouTube ๐
Tips
Property Wrappers as Debugging Tools
Property Wrappers allow developers to wrap properties with specific behaviors, that will be seamlessly triggered whenever the properties are accessed.
While their primary use case is to implement business logic within our apps, it's also possible to use Property Wrappers as debugging tools!
For example, we could build a wrapper called @History, that would be added to a property while debugging and would keep track of all the values set to this property.
import Foundation
@propertyWrapper
struct History<Value> {
private var value: Value
private(set) var history: [Value] = []
init(wrappedValue: Value) {
self.value = wrappedValue
}
var wrappedValue: Value {
get { value }
set {
history.append(value)
value = newValue
}
}
var projectedValue: Self {
return self
}
}
// We can then decorate our business code
// with the `@History` wrapper
struct User {
@History var name: String = ""
}
var user = User()
// All the existing call sites will still
// compile, without the need for any change
user.name = "John"
user.name = "Jane"
// But now we can also access an history of
// all the previous values!
user.$name.history // ["", "John"]
Localization through String interpolation
Swift 5 gave us the possibility to define our own custom String interpolation methods.
This feature can be used to power many use cases, but there is one that is guaranteed to make sense in most projects: localizing user-facing strings.
import Foundation
extension String.StringInterpolation {
mutating func appendInterpolation(localized key: String, _ args: CVarArg...) {
let localized = String(format: NSLocalizedString(key, comment: ""), arguments: args)
appendLiteral(localized)
}
}
/*
Let's assume that this is the content of our Localizable.strings:
"welcome.screen.greetings" = "Hello %@!";
*/
let userName = "John"
print("\(localized: "welcome.screen.greetings", userName)") // Hello John!
Implementing pseudo-inheritance between structs
If youโve always wanted to use some kind of inheritance mechanism for your structs, Swift 5.1 is going to make you very happy!
Using the new KeyPath-based dynamic member lookup, you can implement some pseudo-inheritance, where a type inherits the API of another one ๐
(However, be careful, Iโm definitely not advocating inheritance as a go-to solution ๐)
import Foundation
protocol Inherits {
associatedtype SuperType
var `super`: SuperType { get }
}
extension Inherits {
subscript<T>(dynamicMember keyPath: KeyPath<SuperType, T>) -> T {
return self.`super`[keyPath: keyPath]
}
}
struct Person {
let name: String
}
@dynamicMemberLookup
struct User: Inherits {
let `super`: Person
let login: String
let password: String
}
let user = User(super: Person(name: "John Appleseed"), login: "Johnny", password: "1234")
user.name // "John Appleseed"
user.login // "Johnny"
Composing NSAttributedString through a Function Builder
Swift 5.1 introduced Function Builders: a great tool for building custom DSL syntaxes, like SwiftUI. However, one doesn't need to be building a full-fledged DSL in order to leverage them.
For example, it's possible to write a simple Function Builder, whose job will be to compose together individual instances of NSAttributedString through a nicer syntax than the standard API.
import UIKit
@_functionBuilder
class NSAttributedStringBuilder {
static func buildBlock(_ components: NSAttributedString...) -> NSAttributedString {
let result = NSMutableAttributedString(string: "")
return components.reduce(into: result) { (result, current) in result.append(current) }
}
}
extension NSAttributedString {
class func composing(@NSAttributedStringBuilder _ parts: () -> NSAttributedString) -> NSAttributedString {
return parts()
}
}
let result = NSAttributedString.composing {
NSAttributedString(string: "Hello",
attributes: [.font: UIFont.systemFont(ofSize: 24),
.foregroundColor: UIColor.red])
NSAttributedString(string: " world!",
attributes: [.font: UIFont.systemFont(ofSize: 20),
.foregroundColor: UIColor.orange])
}
Using switch and if as expressions
Contrary to other languages, like Kotlin, Swift does not allow switch and if to be used as expressions. Meaning that the following code is not valid Swift:
let constant = if condition {
someValue
} else {
someOtherValue
}
A common solution to this problem is to wrap the if or switch statement within a closure, that will then be immediately called. While this approach does manage to achieve the desired goal, it makes for a rather poor syntax.
To avoid the ugly trailing () and improve on the readability, you can define a resultOf function, that will serve the exact same purpose, in a more elegant way.
import Foundation
func resultOf<T>(_ code: () -> T) -> T {
return code()
}
let randomInt = Int.random(in: 0...3)
let spelledOut: String = resultOf {
switch randomInt {
case 0:
return "Zero"
case 1:
return "One"
case 2:
return "Two"
case 3:
return "Three"
default:
return "Out of range"
}
}
print(spelledOut)
Avoiding double negatives within guard statements
A guard statement is a very convenient way for the developer to assert that a condition is met, in order for the execution of the program to keep going.
However, since the body of a guard statement is meant to be executed when the condition evaluates to false, the use of the negation (!) operator within the condition of a guard statement can make the code hard to read, as it becomes a double negative.
A nice trick to avoid such double negatives is to encapsulate the use of the ! operator within a new property or function, whose name does not include a negative.
import Foundation
extension Collection {
var hasElements: Bool {
return !isEmpty
}
}
let array = Bool.random() ? [1, 2, 3] : []
guard array.hasElements else { fatalError("array was empty") }
print(array)
Defining a custom init without loosing the compiler-generated one
It's common knowledge for Swift developers that, when you define a struct, the compiler is going to automatically generate a memberwise init for you. That is, unless you also define an init of your own. Because then, the compiler won't generate any memberwise init.
Yet, there are many instances where we might enjoy the opportunity to get both. As it turns out, this goal is quite easy to achieve: you just need to define your own init in an extension rather than inside the type definition itself.
import Foundation
struct Point {
let x: Int
let y: Int
}
extension Point {
init() {
x = 0
y = 0
}
}
let usingDefaultInit = Point(x: 4, y: 3)
let usingCustomInit = Point()
Implementing a namespace through an empty enum
Swift does not really have an out-of-the-box support of namespaces. One could argue that a Swift module can be seen as a namespace, but creating a dedicated Framework for this sole purpose can legitimately be regarded as overkill.
Some developers have taken the habit to use a struct which only contains static fields to implement a namespace. While this does the job, it requires us to remember to implement an empty private init(), because it wouldn't make sense for such a struct to be instantiated.
It's actually possible to take this approach one step further, by replacing the struct with an enum. While it might seem weird to have an enum with no case, it's actually a very idiomatic way to declare a type that cannot be instantiated.
import Foundation
enum NumberFormatterProvider {
static var currencyFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .currency
formatter.roundingIncrement = 0.01
return formatter
}
static var decimalFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .decimal
formatter.decimalSeparator = ","
return formatter
}
}
NumberFormatterProvider() // โ impossible to instantiate by mistake
NumberFormatterProvider.currencyFormatter.string(from: 2.456) // $2.46
NumberFormatterProvider.decimalFormatter.string(from: 2.456) // 2,456
Using Never to represent impossible code paths
Never is quite a peculiar type in the Swift Standard Library: it is defined as an empty enum enum Never { }.
While this might seem odd at first glance, it actually yields a very interesting property: it makes it a type that cannot be constructed (i.e. it possesses no instances).
This way, Never can be used as a generic parameter to let the compiler know that a particular feature will not be used.
import Foundation
enum Result<Value, Error> {
case success(value: Value)
case failure(error: Error)
}
func willAlwaysSucceed(_ completion: @escaping ((Result<String, Never>) -> Void)) {
completion(.success(value: "Call was successful"))
}
willAlwaysSucceed( { result in
switch result {
case .success(let value):
print(value)
// the compiler knows that the `failure` case cannot happen
// so it doesn't require us to handle it.
}
})
Providing a default value to a Decodable enum
Swift's Codable framework does a great job at seamlessly decoding entities from a JSON stream. However, when we integrate web-services, we are sometimes left to deal with JSONs that require behaviors that Codable does not provide out-of-the-box.
For instance, we might have a string-based or integer-based enum, and be required to set it to a default value when the data found in the JSON does not match any of its cases.
We might be tempted to implement this via an extensive switch statement over all the possible cases, but there is a much shorter alternative through the initializer init?(rawValue:):
import Foundation
enum State: String, Decodable {
case active
case inactive
case undefined
init(from decoder: Decoder) throws {
let container = try decoder.singleValueContainer()
let decodedString = try container.decode(String.self)
self = State(rawValue: decodedString) ?? .undefined
}
}
let data = """
["active", "inactive", "foo"]
""".data(using: .utf8)!
let decoded = try! JSONDecoder().decode([State].self, from: data)
print(decoded) // [State.active, State.inactive, State.undefined]
Another lightweight dependency injection through default values for function parameters
Dependency injection boils down to a simple idea: when an object requires a dependency, it shouldn't create it by itself, but instead it should be given a function that does it for him.
Now the great thing with Swift is that, not only can a function take another function as a parameter, but that parameter can also be given a default value.
When you combine both those features, you can end up with a dependency injection pattern that is both lightweight on boilerplate, but also type safe.
import Foundation
protocol Service {
func call() -> String
}
class ProductionService: Service {
func call() -> String {
return "This is the production"
}
}
class MockService: Service {
func call() -> String {
return "This is a mock"
}
}
typealias Provider<T> = () -> T
class Controller {
let service: Service
init(serviceProvider: Provider<Service> = { return ProductionService() }) {
self.service = serviceProvider()
}
func work() {
print(service.call())
}
}
let productionController = Controller()
productionController.work() // prints "This is the production"
let mockedController = Controller(serviceProvider: { return MockService() })
mockedController.work() // prints "This is a mock"
Lightweight dependency injection through protocol-oriented programming
Singletons are pretty bad. They make your architecture rigid and tightly coupled, which then results in your code being hard to test and refactor. Instead of using singletons, your code should rely on dependency injection, which is a much more architecturally sound approach.
But singletons are so easy to use, and dependency injection requires us to do extra-work. So maybe, for simple situations, we could find an in-between solution?
One possible solution is to rely on one of Swift's most know features: protocol-oriented programming. Using a protocol, we declare and access our dependency. We then store it in a private singleton, and perform the injection through an extension of said protocol.
This way, our code will indeed be decoupled from its dependency, while at the same time keeping the boilerplate to a minimum.
import Foundation
protocol Formatting {
var formatter: NumberFormatter { get }
}
private let sharedFormatter: NumberFormatter = {
let sharedFormatter = NumberFormatter()
sharedFormatter.numberStyle = .currency
return sharedFormatter
}()
extension Formatting {
var formatter: NumberFormatter { return sharedFormatter }
}
class ViewModel: Formatting {
var displayableAmount: String?
func updateDisplay(to amount: Double) {
displayableAmount = formatter.string(for: amount)
}
}
let viewModel = ViewModel()
viewModel.updateDisplay(to: 42000.45)
viewModel.displayableAmount // "$42,000.45"
Getting rid of overabundant [weak self] and guard
Callbacks are a part of almost all iOS apps, and as frameworks such as RxSwift keep gaining in popularity, they become ever more present in our codebase.
Seasoned Swift developers are aware of the potential memory leaks that @escaping callbacks can produce, so they make real sure to always use [weak self], whenever they need to use self inside such a context. And when they need to have self be non-optional, they then add a guard statement along.
Consequently, this syntax of a [weak self] followed by a guard rapidly tends to appear everywhere in the codebase. The good thing is that, through a little protocol-oriented trick, it's actually possible to get rid of this tedious syntax, without loosing any of its benefits!
import Foundation
import PlaygroundSupport
PlaygroundPage.current.needsIndefiniteExecution = true
protocol Weakifiable: class { }
extension Weakifiable {
func weakify(_ code: @escaping (Self) -> Void) -> () -> Void {
return { [weak self] in
guard let self = self else { return }
code(self)
}
}
func weakify<T>(_ code: @escaping (T, Self) -> Void) -> (T) -> Void {
return { [weak self] arg in
guard let self = self else { return }
code(arg, self)
}
}
}
extension NSObject: Weakifiable { }
class Producer: NSObject {
deinit {
print("deinit Producer")
}
private var handler: (Int) -> Void = { _ in }
func register(handler: @escaping (Int) -> Void) {
self.handler = handler
DispatchQueue.main.asyncAfter(deadline: .now() + 1.0, execute: { self.handler(42) })
}
}
class Consumer: NSObject {
deinit {
print("deinit Consumer")
}
let producer = Producer()
func consume() {
producer.register(handler: weakify { result, strongSelf in
strongSelf.handle(result)
})
}
private func handle(_ result: Int) {
print("๐ \(result)")
}
}
var consumer: Consumer? = Consumer()
consumer?.consume()
DispatchQueue.main.asyncAfter(deadline: .now() + 2.0, execute: { consumer = nil })
// This code prints:
// ๐ 42
// deinit Consumer
// deinit Producer
Solving callback hell with function composition
Asynchronous functions are a big part of iOS APIs, and most developers are familiar with the challenge they pose when one needs to sequentially call several asynchronous APIs.
This often results in callbacks being nested into one another, a predicament often referred to as callback hell.
Many third-party frameworks are able to tackle this issue, for instance RxSwift or PromiseKit. Yet, for simple instances of the problem, there is no need to use such big guns, as it can actually be solved with simple function composition.
import Foundation
typealias CompletionHandler<Result> = (Result?, Error?) -> Void
infix operator ~>: MultiplicationPrecedence
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ second: @escaping (T, CompletionHandler<U>) -> Void) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ firstResult, error in
guard let firstResult = firstResult else { completion(nil, error); return }
second(firstResult, { (secondResult, error) in
completion(secondResult, error)
})
})
}
}
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ transform: @escaping (T) -> U) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ result, error in
guard let result = result else { completion(nil, error); return }
completion(transform(result), nil)
})
}
}
func service1(_ completionHandler: CompletionHandler<Int>) {
completionHandler(42, nil)
}
func service2(arg: String, _ completionHandler: CompletionHandler<String>) {
completionHandler("๐ \(arg)", nil)
}
let chainedServices = service1
~> { int in return String(int / 2) }
~> service2
chainedServices({ result, _ in
guard let result = result else { return }
print(result) // Prints: ๐ 21
})
Transform an asynchronous function into a synchronous one
Asynchronous functions are a great way to deal with future events without blocking a thread. Yet, there are times where we would like them to behave in exactly such a blocking way.
Think about writing unit tests and using mocked network calls. You will need to add complexity to your test in order to deal with asynchronous functions, whereas synchronous ones would be much easier to manage.
Thanks to Swift proficiency in the functional paradigm, it is possible to write a function whose job is to take an asynchronous function and transform it into a synchronous one.
import Foundation
func makeSynchrone<A, B>(_ asyncFunction: @escaping (A, (B) -> Void) -> Void) -> (A) -> B {
return { arg in
let lock = NSRecursiveLock()
var result: B? = nil
asyncFunction(arg) {
result = $0
lock.unlock()
}
lock.lock()
return result!
}
}
func myAsyncFunction(arg: Int, completionHandler: (String) -> Void) {
completionHandler("๐ \(arg)")
}
let syncFunction = makeSynchrone(myAsyncFunction)
print(syncFunction(42)) // prints ๐ 42
Using KeyPaths instead of closures
Closures are a great way to interact with generic APIs, for instance APIs that allow to manipulate data structures through the use of generic functions, such as filter() or sorted().
The annoying part is that closures tend to clutter your code with many instances of {, } and $0, which can quickly undermine its readably.
A nice alternative for a cleaner syntax is to use a KeyPath instead of a closure, along with an operator that will deal with transforming the provided KeyPath in a closure.
import Foundation
prefix operator ^
prefix func ^ <Element, Attribute>(_ keyPath: KeyPath<Element, Attribute>) -> (Element) -> Attribute {
return { element in element[keyPath: keyPath] }
}
struct MyData {
let int: Int
let string: String
}
let data = [MyData(int: 2, string: "Foo"), MyData(int: 4, string: "Bar")]
data.map(^\.int) // [2, 4]
data.map(^\.string) // ["Foo", "Bar"]
Bringing some type-safety to a userInfo Dictionary
Many iOS APIs still rely on a userInfo Dictionary to handle use-case specific data. This Dictionary usually stores untyped values, and is declared as follows: [String: Any] (or sometimes [AnyHashable: Any].
Retrieving data from such a structure will involve some conditional casting (via the as? operator), which is prone to both errors and repetitions. Yet, by introducing a custom subscript, it's possible to encapsulate all the tedious logic, and end-up with an easier and more robust API.
import Foundation
typealias TypedUserInfoKey<T> = (key: String, type: T.Type)
extension Dictionary where Key == String, Value == Any {
subscript<T>(_ typedKey: TypedUserInfoKey<T>) -> T? {
return self[typedKey.key] as? T
}
}
let userInfo: [String : Any] = ["Foo": 4, "Bar": "forty-two"]
let integerTypedKey = TypedUserInfoKey(key: "Foo", type: Int.self)
let intValue = userInfo[integerTypedKey] // returns 4
type(of: intValue) // returns Int?
let stringTypedKey = TypedUserInfoKey(key: "Bar", type: String.self)
let stringValue = userInfo[stringTypedKey] // returns "forty-two"
type(of: stringValue) // returns String?
Lightweight data-binding for an MVVM implementation
MVVM is a great pattern to separate business logic from presentation logic. The main challenge to make it work, is to define a mechanism for the presentation layer to be notified of model updates.
RxSwift is a perfect choice to solve such a problem. Yet, some developers don't feel confortable with leveraging a third-party library for such a central part of their architecture.
For those situation, it's possible to define a lightweight Variable type, that will make the MVVM pattern very easy to use!
import Foundation
class Variable<Value> {
var value: Value {
didSet {
onUpdate?(value)
}
}
var onUpdate: ((Value) -> Void)? {
didSet {
onUpdate?(value)
}
}
init(_ value: Value, _ onUpdate: ((Value) -> Void)? = nil) {
self.value = value
self.onUpdate = onUpdate
self.onUpdate?(value)
}
}
let variable: Variable<String?> = Variable(nil)
variable.onUpdate = { data in
if let data = data {
print(data)
}
}
variable.value = "Foo"
variable.value = "Bar"
// prints:
// Foo
// Bar
Using typealias to its fullest
The keyword typealias allows developers to give a new name to an already existing type. For instance, Swift defines Void as a typealias of (), the empty tuple.
But a less known feature of this mechanism is that it allows to assign concrete types for generic parameters, or to rename them. This can help make the semantics of generic types much clearer, when used in specific use cases.
import Foundation
enum Either<Left, Right> {
case left(Left)
case right(Right)
}
typealias Result<Value> = Either<Value, Error>
typealias IntOrString = Either<Int, String>
Writing an interruptible overload of forEach
Iterating through objects via the forEach(_:) method is a great alternative to the classic for loop, as it allows our code to be completely oblivious of the iteration logic. One limitation, however, is that forEach(_:) does not allow to stop the iteration midway.
Taking inspiration from the Objective-C implementation, we can write an overload that will allow the developer to stop the iteration, if needed.
import Foundation
extension Sequence {
func forEach(_ body: (Element, _ stop: inout Bool) throws -> Void) rethrows {
var stop = false
for element in self {
try body(element, &stop)
if stop {
return
}
}
}
}
["Foo", "Bar", "FooBar"].forEach { element, stop in
print(element)
stop = (element == "Bar")
}
// Prints:
// Foo
// Bar
Optimizing the use of reduce()
Functional programing is a great way to simplify a codebase. For instance, reduce is an alternative to the classic for loop, without most the boilerplate. Unfortunately, simplicity often comes at the price of performance.
Consider that you want to remove duplicate values from a Sequence. While reduce() is a perfectly fine way to express this computation, the performance will be sub optimal, because of all the unnecessary Array copying that will happen every time its closure gets called.
That's when reduce(into:_:) comes into play. This version of reduce leverages the capacities of copy-on-write type (such as Array or Dictionnary) in order to avoid unnecessary copying, which results in a great performance boost.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
let data = (1...1_000).map { _ in Int(arc4random_uniform(256)) }
// runs in 0.63s
time {
let noDuplicates: [Int] = data.reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
// runs in 0.15s
time {
let noDuplicates: [Int] = data.reduce(into: [], { if !$0.contains($1) { $0.append($1) } } )
}
Avoiding hardcoded reuse identifiers
UI components such as UITableView and UICollectionView rely on reuse identifiers in order to efficiently recycle the views they display. Often, those reuse identifiers take the form of a static hardcoded String, that will be used for every instance of their class.
Through protocol-oriented programing, it's possible to avoid those hardcoded values, and instead use the name of the type as a reuse identifier.
import Foundation
import UIKit
protocol Reusable {
static var reuseIdentifier: String { get }
}
extension Reusable {
static var reuseIdentifier: String {
return String(describing: self)
}
}
extension UITableViewCell: Reusable { }
extension UITableView {
func register<T: UITableViewCell>(_ class: T.Type) {
register(`class`, forCellReuseIdentifier: T.reuseIdentifier)
}
func dequeueReusableCell<T: UITableViewCell>(for indexPath: IndexPath) -> T {
return dequeueReusableCell(withIdentifier: T.reuseIdentifier, for: indexPath) as! T
}
}
class MyCell: UITableViewCell { }
let tableView = UITableView()
tableView.register(MyCell.self)
let myCell: MyCell = tableView.dequeueReusableCell(for: [0, 0])
Defining a union type
The C language has a construct called union, that allows a single variable to hold values from different types. While Swift does not provide such a construct, it provides enums with associated values, which allows us to define a type called Either that implements a union of two types.
import Foundation
enum Either<A, B> {
case left(A)
case right(B)
func either(ifLeft: ((A) -> Void)? = nil, ifRight: ((B) -> Void)? = nil) {
switch self {
case let .left(a):
ifLeft?(a)
case let .right(b):
ifRight?(b)
}
}
}
extension Bool { static func random() -> Bool { return arc4random_uniform(2) == 0 } }
var intOrString: Either<Int, String> = Bool.random() ? .left(2) : .right("Foo")
intOrString.either(ifLeft: { print($0 + 1) }, ifRight: { print($0 + "Bar") })
If you're interested by this kind of data structure, I strongly recommend that you learn more about Algebraic Data Types.
Asserting that classes have associated NIBs and vice-versa
Most of the time, when we create a .xib file, we give it the same name as its associated class. From that, if we later refactor our code and rename such a class, we run the risk of forgetting to rename the associated .xib.
While the error will often be easy to catch, if the .xib is used in a remote section of its app, it might go unnoticed for sometime. Fortunately it's possible to build custom test predicates that will assert that 1) for a given class, there exists a .nib with the same name in a given Bundle, 2) for all the .nib in a given Bundle, there exists a class with the same name.
import XCTest
public func XCTAssertClassHasNib(_ class: AnyClass, bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
let associatedNibURL = bundle.url(forResource: String(describing: `class`), withExtension: "nib")
XCTAssertNotNil(associatedNibURL, "Class \"\(`class`)\" has no associated nib file", file: file, line: line)
}
public func XCTAssertNibHaveClasses(_ bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
guard let bundleName = bundle.infoDictionary?["CFBundleName"] as? String,
let basePath = bundle.resourcePath,
let enumerator = FileManager.default.enumerator(at: URL(fileURLWithPath: basePath),
includingPropertiesForKeys: nil,
options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) else { return }
var nibFilesURLs = [URL]()
for case let fileURL as URL in enumerator {
if fileURL.pathExtension.uppercased() == "NIB" {
nibFilesURLs.append(fileURL)
}
}
nibFilesURLs.map { $0.lastPathComponent }
.compactMap { $0.split(separator: ".").first }
.map { String($0) }
.forEach {
let associatedClass: AnyClass? = bundle.classNamed("\(bundleName).\($0)")
XCTAssertNotNil(associatedClass, "File \"\($0).nib\" has no associated class", file: file, line: line)
}
}
XCTAssertClassHasNib(MyFirstTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertClassHasNib(MySecondTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertNibHaveClasses(Bundle(for: AppDelegate.self))
Many thanks Benjamin Lavialle for coming up with the idea behind the second test predicate.
Small footprint type-erasing with functions
Seasoned Swift developers know it: a protocol with associated type (PAT) "can only be used as a generic constraint because it has Self or associated type requirements". When we really need to use a PAT to type a variable, the goto workaround is to use a type-erased wrapper.
While this solution works perfectly, it requires a fair amount of boilerplate code. In instances where we are only interested in exposing one particular function of the PAT, a shorter approach using function types is possible.
import Foundation
import UIKit
protocol Configurable {
associatedtype Model
func configure(with model: Model)
}
typealias Configurator<Model> = (Model) -> ()
extension UILabel: Configurable {
func configure(with model: String) {
self.text = model
}
}
let label = UILabel()
let configurator: Configurator<String> = label.configure
configurator("Foo")
label.text // "Foo"
Performing animations sequentially
UIKit exposes a very powerful and simple API to perform view animations. However, this API can become a little bit quirky to use when we want to perform animations sequentially, because it involves nesting closure within one another, which produces notoriously hard to maintain code.
Nonetheless, it's possible to define a rather simple class, that will expose a really nicer API for this particular use case ๐
import Foundation
import UIKit
class AnimationSequence {
typealias Animations = () -> Void
private let current: Animations
private let duration: TimeInterval
private var next: AnimationSequence? = nil
init(animations: @escaping Animations, duration: TimeInterval) {
self.current = animations
self.duration = duration
}
@discardableResult func append(animations: @escaping Animations, duration: TimeInterval) -> AnimationSequence {
var lastAnimation = self
while let nextAnimation = lastAnimation.next {
lastAnimation = nextAnimation
}
lastAnimation.next = AnimationSequence(animations: animations, duration: duration)
return self
}
func run() {
UIView.animate(withDuration: duration, animations: current, completion: { finished in
if finished, let next = self.next {
next.run()
}
})
}
}
var firstView = UIView()
var secondView = UIView()
firstView.alpha = 0
secondView.alpha = 0
AnimationSequence(animations: { firstView.alpha = 1.0 }, duration: 1)
.append(animations: { secondView.alpha = 1.0 }, duration: 0.5)
.append(animations: { firstView.alpha = 0.0 }, duration: 2.0)
.run()
Debouncing a function call
Debouncing is a very useful tool when dealing with UI inputs. Consider a search bar, whose content is used to query an API. It wouldn't make sense to perform a request for every character the user is typing, because as soon as a new character is entered, the result of the previous request has become irrelevant.
Instead, our code will perform much better if we "debounce" the API call, meaning that we will wait until some delay has passed, without the input being modified, before actually performing the call.
import Foundation
func debounced(delay: TimeInterval, queue: DispatchQueue = .main, action: @escaping (() -> Void)) -> () -> Void {
var workItem: DispatchWorkItem?
return {
workItem?.cancel()
workItem = DispatchWorkItem(block: action)
queue.asyncAfter(deadline: .now() + delay, execute: workItem!)
}
}
let debouncedPrint = debounced(delay: 1.0) { print("Action performed!") }
debouncedPrint()
debouncedPrint()
debouncedPrint()
// After a 1 second delay, this gets
// printed only once to the console:
// Action performed!
Providing useful operators for Optional booleans
When we need to apply the standard boolean operators to Optional booleans, we often end up with a syntax unnecessarily crowded with unwrapping operations. By taking a cue from the world of three-valued logics, we can define a couple operators that make working with Bool? values much nicer.
import Foundation
func && (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (false, _), (_, false):
return false
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs && unwrapRhs
default:
return nil
}
}
func || (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (true, _), (_, true):
return true
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs || unwrapRhs
default:
return nil
}
}
false && nil // false
true && nil // nil
[true, nil, false].reduce(true, &&) // false
nil || true // true
nil || false // nil
[true, nil, false].reduce(false, ||) // true
Removing duplicate values from a Sequence
Transforming a Sequence in order to remove all the duplicate values it contains is a classic use case. To implement it, one could be tempted to transform the Sequence into a Set, then back to an Array. The downside with this approach is that it will not preserve the order of the sequence, which can definitely be a dealbreaker. Using reduce() it is possible to provide a concise implementation that preserves ordering:
import Foundation
extension Sequence where Element: Equatable {
func duplicatesRemoved() -> [Element] {
return reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
}
let data = [2, 5, 2, 3, 6, 5, 2]
data.duplicatesRemoved() // [2, 5, 3, 6]
Shorter syntax to deal with optional strings
Optional strings are very common in Swift code, for instance many objects from UIKit expose the text they display as a String?. Many times you will need to manipulate this data as an unwrapped String, with a default value set to the empty string for nil cases.
While the nil-coalescing operator (e.g. ??) is a perfectly fine way to a achieve this goal, defining a computed variable like orEmpty can help a lot in cleaning the syntax.
import Foundation
import UIKit
extension Optional where Wrapped == String {
var orEmpty: String {
switch self {
case .some(let value):
return value
case .none:
return ""
}
}
}
func doesNotWorkWithOptionalString(_ param: String) {
// do something with `param`
}
let label = UILabel()
label.text = "This is some text."
doesNotWorkWithOptionalString(label.text.orEmpty)
Encapsulating background computation and UI update
Every seasoned iOS developers knows it: objects from UIKit can only be accessed from the main thread. Any attempt to access them from a background thread is a guaranteed crash.
Still, running a costly computation on the background, and then using it to update the UI can be a common pattern.
In such cases you can rely on asyncUI to encapsulate all the boilerplate code.
import Foundation
import UIKit
func asyncUI<T>(_ computation: @autoclosure @escaping () -> T, qos: DispatchQoS.QoSClass = .userInitiated, _ completion: @escaping (T) -> Void) {
DispatchQueue.global(qos: qos).async {
let value = computation()
DispatchQueue.main.async {
completion(value)
}
}
}
let label = UILabel()
func costlyComputation() -> Int { return (0..<10_000).reduce(0, +) }
asyncUI(costlyComputation()) { value in
label.text = "\(value)"
}
Retrieving all the necessary data to build a debug view
A debug view, from which any controller of an app can be instantiated and pushed on the navigation stack, has the potential to bring some real value to a development process. A requirement to build such a view is to have a list of all the classes from a given Bundle that inherit from UIViewController. With the following extension, retrieving this list becomes a piece of cake ๐ฐ
import Foundation
import UIKit
import ObjectiveC
extension Bundle {
func viewControllerTypes() -> [UIViewController.Type] {
guard let bundlePath = self.executablePath else { return [] }
var size: UInt32 = 0
var rawClassNames: UnsafeMutablePointer<UnsafePointer<Int8>>!
var parsedClassNames = [String]()
rawClassNames = objc_copyClassNamesForImage(bundlePath, &size)
for index in 0..<size {
let className = rawClassNames[Int(index)]
if let name = NSString.init(utf8String:className) as String?,
NSClassFromString(name) is UIViewController.Type {
parsedClassNames.append(name)
}
}
return parsedClassNames
.sorted()
.compactMap { NSClassFromString($0) as? UIViewController.Type }
}
}
// Fetch all view controller types in UIKit
Bundle(for: UIViewController.self).viewControllerTypes()
I share the credit for this tip with Benoรฎt Caron.
Defining a function to map over dictionaries
Update As it turns out, map is actually a really bad name for this function, because it does not preserve composition of transformations, a property that is required to fit the definition of a real map function.
Surprisingly enough, the standard library doesn't define a map() function for dictionaries that allows to map both keys and values into a new Dictionary. Nevertheless, such a function can be helpful, for instance when converting data across different frameworks.
import Foundation
extension Dictionary {
func map<T: Hashable, U>(_ transform: (Key, Value) throws -> (T, U)) rethrows -> [T: U] {
var result: [T: U] = [:]
for (key, value) in self {
let (transformedKey, transformedValue) = try transform(key, value)
result[transformedKey] = transformedValue
}
return result
}
}
let data = [0: 5, 1: 6, 2: 7]
data.map { ("\($0)", $1 * $1) } // ["2": 49, "0": 25, "1": 36]
A shorter syntax to remove nil values
Swift provides the function compactMap(), that can be used to remove nil values from a Sequence of optionals when calling it with an argument that just returns its parameter (i.e. compactMap { $0 }). Still, for such use cases it would be nice to get rid of the trailing closure.
The implementation isn't as straightforward as your usual extension, but once it has been written, the call site definitely gets cleaner ๐
import Foundation
protocol OptionalConvertible {
associatedtype Wrapped
func asOptional() -> Wrapped?
}
extension Optional: OptionalConvertible {
func asOptional() -> Wrapped? {
return self
}
}
extension Sequence where Element: OptionalConvertible {
func compacted() -> [Element.Wrapped] {
return compactMap { $0.asOptional() }
}
}
let data = [nil, 1, 2, nil, 3, 5, nil, 8, nil]
data.compacted() // [1, 2, 3, 5, 8]
Dealing with expirable values
It might happen that your code has to deal with values that come with an expiration date. In a game, it could be a score multiplier that will only last for 30 seconds. Or it could be an authentication token for an API, with a 15 minutes lifespan. In both instances you can rely on the type Expirable to encapsulate the expiration logic.
import Foundation
struct Expirable<T> {
private var innerValue: T
private(set) var expirationDate: Date
var value: T? {
return hasExpired() ? nil : innerValue
}
init(value: T, expirationDate: Date) {
self.innerValue = value
self.expirationDate = expirationDate
}
init(value: T, duration: Double) {
self.innerValue = value
self.expirationDate = Date().addingTimeInterval(duration)
}
func hasExpired() -> Bool {
return expirationDate < Date()
}
}
let expirable = Expirable(value: 42, duration: 3)
sleep(2)
expirable.value // 42
sleep(2)
expirable.value // nil
I share the credit for this tip with Benoรฎt Caron.
Using parallelism to speed-up map()
Almost all Apple devices able to run Swift code are powered by a multi-core CPU, consequently making a good use of parallelism is a great way to improve code performance. map() is a perfect candidate for such an optimization, because it is almost trivial to define a parallel implementation.
import Foundation
extension Array {
func parallelMap<T>(_ transform: (Element) -> T) -> [T] {
let res = UnsafeMutablePointer<T>.allocate(capacity: count)
DispatchQueue.concurrentPerform(iterations: count) { i in
res[i] = transform(self[i])
}
let finalResult = Array<T>(UnsafeBufferPointer(start: res, count: count))
res.deallocate(capacity: count)
return finalResult
}
}
let array = (0..<1_000).map { $0 }
func work(_ n: Int) -> Int {
return (0..<n).reduce(0, +)
}
array.parallelMap { work($0) }
๐จ Make sure to only use parallelMap() when the transform function actually performs some costly computations. Otherwise performances will be systematically slower than using map(), because of the multithreading overhead.
Measuring execution time with minimum boilerplate
During development of a feature that performs some heavy computations, it can be helpful to measure just how much time a chunk of code takes to run. The time() function is a nice tool for this purpose, because of how simple it is to add and then to remove when it is no longer needed.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
time {
(0...10_000).map { $0 * $0 }
}
// time: 0.183973908424377
Running two pieces of code in parallel
Concurrency is definitely one of those topics were the right encapsulation bears the potential to make your life so much easier. For instance, with this piece of code you can easily launch two computations in parallel, and have the results returned in a tuple.
import Foundation
func parallel<T, U>(_ left: @autoclosure () -> T, _ right: @autoclosure () -> U) -> (T, U) {
var leftRes: T?
var rightRes: U?
DispatchQueue.concurrentPerform(iterations: 2, execute: { id in
if id == 0 {
leftRes = left()
} else {
rightRes = right()
}
})
return (leftRes!, rightRes!)
}
let values = (1...100_000).map { $0 }
let results = parallel(values.map { $0 * $0 }, values.reduce(0, +))
Making good use of #file, #line and #function
Swift exposes three special variables #file, #line and #function, that are respectively set to the name of the current file, line and function. Those variables become very useful when writing custom logging functions or test predicates.
import Foundation
func log(_ message: String, _ file: String = #file, _ line: Int = #line, _ function: String = #function) {
print("[\(file):\(line)] \(function) - \(message)")
}
func foo() {
log("Hello world!")
}
foo() // [MyPlayground.playground:8] foo() - Hello world!
Comparing Optionals through Conditional Conformance
Swift 4.1 has introduced a new feature called Conditional Conformance, which allows a type to implement a protocol only when its generic type also does.
With this addition it becomes easy to let Optional implement Comparable only when Wrapped also implements Comparable:
import Foundation
extension Optional: Comparable where Wrapped: Comparable {
public static func < (lhs: Optional, rhs: Optional) -> Bool {
switch (lhs, rhs) {
case let (lhs?, rhs?):
return lhs < rhs
case (nil, _?):
return true // anything is greater than nil
case (_?, nil):
return false // nil in smaller than anything
case (nil, nil):
return true // nil is not smaller than itself
}
}
}
let data: [Int?] = [8, 4, 3, nil, 12, 4, 2, nil, -5]
data.sorted() // [nil, nil, Optional(-5), Optional(2), Optional(3), Optional(4), Optional(4), Optional(8), Optional(12)]
Safely subscripting a Collection
Any attempt to access an Array beyond its bounds will result in a crash. While it's possible to write conditions such as if index < array.count { array[index] } in order to prevent such crashes, this approach will rapidly become cumbersome.
A great thing is that this condition can be encapsulated in a custom subscript that will work on any Collection:
import Foundation
extension Collection {
subscript (safe index: Index) -> Element? {
return indices.contains(index) ? self[index] : nil
}
}
let data = [1, 3, 4]
data[safe: 1] // Optional(3)
data[safe: 10] // nil
Easier String slicing using ranges
Subscripting a string with a range can be very cumbersome in Swift 4. Let's face it, no one wants to write lines like someString[index(startIndex, offsetBy: 0)..<index(startIndex, offsetBy: 10)] on a regular basis.
Luckily, with the addition of one clever extension, strings can be sliced as easily as arrays ๐
import Foundation
extension String {
public subscript(value: CountableClosedRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: CountableRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeUpTo<Int>) -> Substring {
get {
return self[..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeThrough<Int>) -> Substring {
get {
return self[...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeFrom<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...]
}
}
}
let data = "This is a string!"
data[..<4] // "This"
data[5..<9] // "is a"
data[10...] // "string!"
Concise syntax for sorting using a KeyPath
By using a KeyPath along with a generic type, a very clean and concise syntax for sorting data can be implemented:
import Foundation
extension Sequence {
func sorted<T: Comparable>(by attribute: KeyPath<Element, T>) -> [Element] {
return sorted(by: { $0[keyPath: attribute] < $1[keyPath: attribute] })
}
}
let data = ["Some", "words", "of", "different", "lengths"]
data.sorted(by: \.count) // ["of", "Some", "words", "lengths", "different"]
If you like this syntax, make sure to checkout KeyPathKit!
Manufacturing cache-efficient versions of pure functions
By capturing a local variable in a returned closure, it is possible to manufacture cache-efficient versions of pure functions. Be careful though, this trick only works with non-recursive function!
import Foundation
func cached<In: Hashable, Out>(_ f: @escaping (In) -> Out) -> (In) -> Out {
var cache = [In: Out]()
return { (input: In) -> Out in
if let cachedValue = cache[input] {
return cachedValue
} else {
let result = f(input)
cache[input] = result
return result
}
}
}
let cachedCos = cached { (x: Double) in cos(x) }
cachedCos(.pi * 2) // value of cos for 2ฯ is now cached
Simplifying complex conditions with pattern matching
When distinguishing between complex boolean conditions, using a switch statement along with pattern matching can be more readable than the classic series of if {} else if {}.
import Foundation
let expr1: Bool
let expr2: Bool
let expr3: Bool
if expr1 && !expr3 {
functionA()
} else if !expr2 && expr3 {
functionB()
} else if expr1 && !expr2 && expr3 {
functionC()
}
switch (expr1, expr2, expr3) {
case (true, _, false):
functionA()
case (_, false, true):
functionB()
case (true, false, true):
functionC()
default:
break
}
Easily generating arrays of data
Using map() on a range makes it easy to generate an array of data.
import Foundation
func randomInt() -> Int { return Int(arc4random()) }
let randomArray = (1...10).map { _ in randomInt() }
Using @autoclosure for cleaner call sites
Using @autoclosure enables the compiler to automatically wrap an argument within a closure, thus allowing for a very clean syntax at call sites.
import UIKit
extension UIView {
class func animate(withDuration duration: TimeInterval, _ animations: @escaping @autoclosure () -> Void) {
UIView.animate(withDuration: duration, animations: animations)
}
}
let view = UIView()
UIView.animate(withDuration: 0.3, view.backgroundColor = .orange)
Observing new and old value with RxSwift
When working with RxSwift, it's very easy to observe both the current and previous value of an observable sequence by simply introducing a shift using skip().
import RxSwift
let values = Observable.of(4, 8, 15, 16, 23, 42)
let newAndOld = Observable.zip(values, values.skip(1)) { (previous: $0, current: $1) }
.subscribe(onNext: { pair in
print("current: \(pair.current) - previous: \(pair.previous)")
})
//current: 8 - previous: 4
//current: 15 - previous: 8
//current: 16 - previous: 15
//current: 23 - previous: 16
//current: 42 - previous: 23
Implicit initialization from literal values
Using protocols such as ExpressibleByStringLiteral it is possible to provide an init that will be automatically when a literal value is provided, allowing for nice and short syntax. This can be very helpful when writing mock or test data.
import Foundation
extension URL: ExpressibleByStringLiteral {
public init(stringLiteral value: String) {
self.init(string: value)!
}
}
let url: URL = "http://www.google.fr"
NSURLConnection.canHandle(URLRequest(url: "http://www.google.fr"))
Achieving systematic validation of data
Through some clever use of Swift private visibility it is possible to define a container that holds any untrusted value (such as a user input) from which the only way to retrieve the value is by making it successfully pass a validation test.
import Foundation
struct Untrusted<T> {
private(set) var value: T
}
protocol Validator {
associatedtype T
static func validation(value: T) -> Bool
}
extension Validator {
static func validate(untrusted: Untrusted<T>) -> T? {
if self.validation(value: untrusted.value) {
return untrusted.value
} else {
return nil
}
}
}
struct FrenchPhoneNumberValidator: Validator {
static func validation(value: String) -> Bool {
return (value.count) == 10 && CharacterSet(charactersIn: value).isSubset(of: CharacterSet.decimalDigits)
}
}
let validInput = Untrusted(value: "0122334455")
let invalidInput = Untrusted(value: "0123")
FrenchPhoneNumberValidator.validate(untrusted: validInput) // returns "0122334455"
FrenchPhoneNumberValidator.validate(untrusted: invalidInput) // returns nil
Implementing the builder pattern with keypaths
With the addition of keypaths in Swift 4, it is now possible to easily implement the builder pattern, that allows the developer to clearly separate the code that initializes a value from the code that uses it, without the burden of defining a factory method.
import UIKit
protocol With {}
extension With where Self: AnyObject {
@discardableResult
func with<T>(_ property: ReferenceWritableKeyPath<Self, T>, setTo value: T) -> Self {
self[keyPath: property] = value
return self
}
}
extension UIView: With {}
let view = UIView()
let label = UILabel()
.with(\.textColor, setTo: .red)
.with(\.text, setTo: "Foo")
.with(\.textAlignment, setTo: .right)
.with(\.layer.cornerRadius, setTo: 5)
view.addSubview(label)
๐จ The Swift compiler does not perform OS availability checks on properties referenced by keypaths. Any attempt to use a KeyPath for an unavailable property will result in a runtime crash.
I share the credit for this tip with Marion Curtil.
Storing functions rather than values
When a type stores values for the sole purpose of parametrizing its functions, itโs then possible to not store the values but directly the function, with no discernable difference at the call site.
import Foundation
struct MaxValidator {
let max: Int
let strictComparison: Bool
func isValid(_ value: Int) -> Bool {
return self.strictComparison ? value < self.max : value <= self.max
}
}
struct MaxValidator2 {
var isValid: (_ value: Int) -> Bool
init(max: Int, strictComparison: Bool) {
self.isValid = strictComparison ? { $0 < max } : { $0 <= max }
}
}
MaxValidator(max: 5, strictComparison: true).isValid(5) // false
MaxValidator2(max: 5, strictComparison: false).isValid(5) // true
Defining operators on function types
Functions are first-class citizen types in Swift, so it is perfectly legal to define operators for them.
import Foundation
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
func ||(_ lhs: @escaping (Int) -> Bool, _ rhs: @escaping (Int) -> Bool) -> (Int) -> Bool {
return { value in
return lhs(value) || rhs(value)
}
}
(firstRange || secondRange)(2) // true
(firstRange || secondRange)(4) // false
(firstRange || secondRange)(6) // true
Typealiases for functions
Typealiases are great to express function signatures in a more comprehensive manner, which then enables us to easily define functions that operate on them, resulting in a nice way to write and use some powerful API.
import Foundation
typealias RangeSet = (Int) -> Bool
func union(_ left: @escaping RangeSet, _ right: @escaping RangeSet) -> RangeSet {
return { left($0) || right($0) }
}
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
let unionRange = union(firstRange, secondRange)
unionRange(2) // true
unionRange(4) // false
Encapsulating state within a function
By returning a closure that captures a local variable, it's possible to encapsulate a mutable state within a function.
import Foundation
func counterFactory() -> () -> Int {
var counter = 0
return {
counter += 1
return counter
}
}
let counter = counterFactory()
counter() // returns 1
counter() // returns 2
Generating all cases for an Enum
โ ๏ธ Since Swift 4.2,
allCasescan now be synthesized at compile-time by simply conforming to the protocolCaseIterable. The implementation below should no longer be used in production code.
Through some clever leveraging of how enums are stored in memory, it is possible to generate an array that contains all the possible cases of an enum. This can prove particularly useful when writing unit tests that consume random data.
import Foundation
enum MyEnum { case first; case second; case third; case fourth }
protocol EnumCollection: Hashable {
static var allCases: [Self] { get }
}
extension EnumCollection {
public static var allCases: [Self] {
var i = 0
return Array(AnyIterator {
let next = withUnsafePointer(to: &i) {
$0.withMemoryRebound(to: Self.self, capacity: 1) { $0.pointee }
}
if next.hashValue != i { return nil }
i += 1
return next
})
}
}
extension MyEnum: EnumCollection { }
MyEnum.allCases // [.first, .second, .third, .fourth]
Using map on optional values
The if-let syntax is a great way to deal with optional values in a safe manner, but at times it can prove to be just a little bit to cumbersome. In such cases, using the Optional.map() function is a nice way to achieve a shorter code while retaining safeness and readability.
import UIKit
let date: Date? = Date() // or could be nil, doesn't matter
let formatter = DateFormatter()
let label = UILabel()
if let safeDate = date {
label.text = formatter.string(from: safeDate)
}
label.text = date.map { return formatter.string(from: $0) }
label.text = date.map(formatter.string(from:)) // even shorter, tough less readable
Download Details:
Author: Vincent-pradeilles
Source Code: https://github.com/vincent-pradeilles/swift-tips
License: MIT license