// Scala Lecture 1

//=================

// - Value assignments (val vs var)

// - How to define functions? (What is returned?)

// - If-Conditions

// - For-Comprehensions (guards, with/without yield)

// - String-Interpolations

//

//

// Value assignments

// (their names should be lower case)

//====================================

val x = 42

val y = 3 + 4 

val z = x / y

println(z)

// (you cannot reassign values: z = 9 will give an error)

// Collections

//=============

List(1,2,3,1)

Set(1,2,3,1)

// picking an element in a list

val lst = List(1, 2, 3, 1)

lst(0)

lst(2)

// head and tail

lst.head

lst.tail

// some alterative syntax for lists

Nil     // empty list

1 :: 2 :: 3 :: Nil

List(1, 2, 3) ::: List(4, 5, 6)

// also

List(1, 2, 3) ++ List(3, 6, 5)

Set(1, 2, 3) ++ Set(3, 6, 5)

// ranges

1 to 10

(1 to 10).toList

(1 to 10).toList.toString

(1 until 10).toList

// Equality in Scala is structural

//=================================

val b = "Dave"

if (a == b) println("Equal") else println("Unequal")

Set(1,2,3) == Set(3,1,2)

List(1,2,3) == List(3,1,2)

// this applies to "concrete" values...pretty much 

// everything; but for example you cannot compare 

// functions (later), and also not arrays

Array(1) == Array(1)

// Printing/Strings

//==================

println("test")

val tst = "This is a " ++ "test" 

print(tst) 

println(tst) 

val lst = List(1,2,3,1)

println(lst.toString)

println(lst.mkString)

println(lst.mkString(","))

// some methods take more than one argument

println(lst.mkString("{", ",", "}"))

// (in this case .mkString can take no, one, 

// or three arguments...this has to do with

// default arguments)

// Conversion methods

//====================

List(1,2,3,1).toString

List(1,2,3,1).toSet

"hello".toList

"hello".toSet

1.toDouble

1   // an Int

1L  // a Long

1F  // a Float

1D  // a Double

// useful methods for lists

//=========================

List(1,2,3,4).length

List(1,2,3,4).reverse

List(1,2,3,4).max

List(1,2,3,4).min

List(1,2,3,4).sum

List(1,2,3,4).take(2).sum

List(1,2,3,4).drop(2).sum

List(1,2,3,4,3).indexOf(3)

"1,2,3,4,5".split(",").mkString("\n")

"1,2,3,4,5".split(",").toList

"1,2,3,4,5".split(",3,").mkString("\n")

// Types (see slide)

//===================

/* Scala is a strongly typed language

 * base types

    Int, Long, BigInt, Float, Double

    String, Char

    Boolean...

 * compound types 

    List[Int]

    Set[Double]

    Pairs: (Int, String)        

    List[(BigInt, String)]

    Option[Int]

 * user-defined types (later)

*/

// you can make the type of a value explicit

val name = "bob"

val name : String = "bob"

// type errors

math.sqrt("64")

// produces

//

// Type Mismatch Error:

//   Found   : ("64" : String)

//   Required: Double

//

// Pairs/Tuples

//==============

val p = (1, "one")

p._1

p._2

val t = (4,1,2,3)

t._4

List(("one", 1), ("two", 2), ("three", 3))

// Function Definitions

//======================

def incr(x: Int) : Int = x + 1

def double(x: Int) : Int = x + x

def square(x: Int) : Int = x * x

def str(x: Int) : String = x.toString

incr(3)

double(4)

square(6)

str(3)

// The general scheme for a function: you have to give a 

// type to each argument and a return type of the function

//

//  def fname(arg1: ty1, arg2: ty2,..., argn: tyn): rty = {

//     ....

//  }

// If-Conditionals

//=================

// - Scala used to not have a then-keyword

// - !!both if-else branches need to be present!!

def fact(n: Int) : Int = 

  if (n == 0) 1 else n * fact(n - 1)

// Scala 3 introduced if-then-else - maybe people 

// desperately needed it 

def fact(n: Int) : Int = 

  if n == 0 then 1 else n * fact(n - 1)

fact(5)

fact(150)

/* boolean operators

   ==     equals

   !=     not equals

   !      not

   && ||  and, or

*/

def fib(n: Int) : Int = {

  if (n == 0) 1 else

    if (n == 1) 1 else fib(n - 1) + fib(n - 2)

}

fib(9)

//gcd - Euclid's algorithm

def gcd(a: Int, b: Int) : Int = {

  if (b == 0) a 

  else  gcd(b, a % b)

}

gcd(48, 18)

def power(x: Int, n: Int) : Int =

  if (n == 0) 1 else x * power(x, n - 1) 

power(5, 5)

// BTW: no returns!!

// "last" line (expression) in a function determines the 

// result

def average(xs: List[Int]) : Int = {

  if (xs.length == 0) 0 

  else xs.sum / xs.length

}

average(List(3,4,5))

average(List())

average(Nil)

// For-Comprehensions (not For-Loops)

//====================================

val lst = (1 to 10).toList

for (n <- lst) yield n * n 

for (n <- lst) yield { 

  square(n) + double(n)

}

for (n <- (1 to 10).toList; 

// you can assign the result of a for-comprehension

// to a value

val mult_table = 

  for (n <- (1 to 10).toList; 

       m <- (1 to 10).toList) yield n * m

// for-comprehensions also work for other

// collections

for (n <- Set(10,12,4,5,7,8,10)) yield n * n

for (n <- (1 to 10)) yield {

  n * n  

}

// with if-predicates / filters

for (n <- (1 to 3).toList; 

     m <- (1 to 3).toList;

     if (n + m) % 2 == 0) yield (n, m)

// with patterns

val lst = List((1, 4), (2, 3), (3, 2), (4, 1))

for (p <- lst) yield p._1 + p._2 

// general pattern of for-yield 

// (yield can be several lines)

for (pat <- ...) yield {

  // potentially complicated

  // calculation of a result

}

// For without yield

//===================

// with only a side-effect (no list is produced),

// has no "yield"

// BTW: a roundabout way of printing out a list, say

val lst = ('a' to 'm').toList

for (i <- (0 until lst.length)) println(lst(i))

// ...why not just the following? Why making your life 

// so complicated?

for (c <- lst) println(c)

// Functions producing multiple outputs

//======================================

def get_ascii(c: Char) : (Char, Int) = 

  (c, c.toInt)

get_ascii('a')

// .maxBy, sortBy with pairs

def get_length(s: String) : (String, Int) = 

  (s, s.length) 

val lst = List("zero", "one", "two", "three", "four", "ten")

val strs = for (s <- lst) yield get_length(s)

strs.sortBy(_._2)

strs.sortBy(_._1)

strs.maxBy(_._2)

strs.maxBy(_._1)

// Aside: concurrency 

// scala-cli --extra-jars scala-parallel-collections_3-1.0.4.jar 

for (n <- (1 to 10)) println(n)

import scala.collection.parallel.CollectionConverters._

for (n <- (1 to 10).par) println(n)

// for measuring time

def time_needed[T](n: Int, code: => T) = {

  val start = System.nanoTime()

  for (i <- (0 to n)) code

  val end = System.nanoTime()

  (end - start) / 1.0e9

}

val list = (1L to 10_000_000L).toList

time_needed(10, for (n <- list) yield n + 42)

time_needed(10, for (n <- list.par) yield n + 42)

// ...but par does not make everything faster

list.sum

list.par.sum

time_needed(10, list.sum)

time_needed(10, list.par.sum)

// Mutable vs Immutable

//======================

//

// Remember:

// - no vars, no ++i, no +=

// - no mutable data-structures (no Arrays, no ListBuffers)

// But what the heck....lets try to count to 1 Mio in parallel