// Cloned by Jack O'Brien on 12 Nov 2020 from World "A star (clone by Jack O'Brien)" by Jack O'Brien
// Please leave this clone trail here.
// Cloned by Jack O'Brien on 5 Nov 2020 from World "A star" by "Coding Train" project
// Please leave this clone trail here.
// Port of
// https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning/tree/master/week1-graphs/05_astar
// Daniel Shiffman
// Nature of Code: Intelligence and Learning
// https://github.com/shiffman/NOC-S17-2-Intelligence-Learning
// Part 1: https://youtu.be/aKYlikFAV4k
// Part 2: https://youtu.be/EaZxUCWAjb0
// Part 3: https://youtu.be/jwRT4PCT6RU
// is diagonal move allowed
const diagonal = true ;
// canvas size
const cw = 900;
const ch = 600;
// How many columns and rows
// different each time
var rando = AB.randomIntAtoB ( 1, 5 );
var cols = 30;
var rows = 20;
// how many walls to make, from 0 to 1
// different each time
// const wallAmount = AB.randomFloatAtoB ( 0, 0.2 );
// const waterAmount = AB.randomFloatAtoB ( 0.4, 0.8 );
const backcolor = 'white';
const wallcolor = 'black';
const pathcolor = 'darkred';
const opencolor = 'lightgreen';
const closedcolor = 'lightpink';
const watercolor = 'blue';
// 2D array
var grid = new Array(cols);
// Open and closed set
var openSet = [];
var closedSet = [];
// Start and end
var start;
var end;
// Width and height of each cell of grid
var w, h;
// The road taken
var path = [];
//=== heuristic ===========================
// this must be always optimistic - real time will be this or longer
function heuristic(a, b)
{
if ( diagonal ) return ( dist(a.i, a.j, b.i, b.j) );
// 2D distance
// dist is a P5 function
else return ( abs(a.i - b.i) + abs(a.j - b.j) );
// else not diagonal, can only go across and down
// so this is optimistic
// not this is not optimistic if we can do diagonal move
}
// Function to delete element from the array
function removeFromArray(arr, elt)
{
// Could use indexOf here instead to be more efficient
for (var i = arr.length - 1; i >= 0; i--)
if (arr[i] == elt)
arr.splice(i, 1);
}
// Daniel Shiffman
// Nature of Code: Intelligence and Learning
// https://github.com/shiffman/NOC-S17-2-Intelligence-Learning
// Part 1: https://youtu.be/aKYlikFAV4k
// Part 2: https://youtu.be/EaZxUCWAjb0
// Part 3: https://youtu.be/jwRT4PCT6RU
// An object to describe a spot in the grid
function Spot(i, j)
{
// Location
this.i = i;
this.j = j;
// f, g, and h values for A*
this.f = 0;
this.g = 0;
this.h = 0;
// Neighbors
this.neighbors = [];
// Where did I come from?
this.previous = undefined;
// Display me
this.show = function(col)
{
fill(backcolor);
stroke(51);
rect(this.i * w, this.j * h, w, h);
};
// Figure out who my neighbors are
this.addNeighbors = function(grid)
{
var i = this.i;
var j = this.j;
if (i < cols - 1) this.neighbors.push(grid[i + 1][j]);
if (i > 0) this.neighbors.push(grid[i - 1][j]);
if (j < rows - 1) this.neighbors.push(grid[i][j + 1]);
if (j > 0) this.neighbors.push(grid[i][j - 1]);
if (diagonal)
// diagonals are also neighbours:
{
if (i > 0 && j > 0) this.neighbors.push(grid[i - 1][j - 1]);
if (i < cols - 1 && j > 0) this.neighbors.push(grid[i + 1][j - 1]);
if (i > 0 && j < rows - 1) this.neighbors.push(grid[i - 1][j + 1]);
if (i < cols - 1 && j < rows - 1) this.neighbors.push(grid[i + 1][j + 1]);
}
}
}
function setup()
{
// slower frame rate to see how it is working
// frameRate (2);
createCanvas(cw, ch);
// Grid cell size
w = width / cols;
h = height / rows;
// Making a 2D array
for (var i = 0; i < cols; i++)
grid[i] = new Array(rows);
for (var i = 0; i < cols; i++)
for (var j = 0; j < rows; j++)
grid[i][j] = new Spot(i, j);
// All the neighbors
for (var i = 0; i < cols; i++)
for (var j = 0; j < rows; j++)
grid[i][j].addNeighbors(grid);
// Start and end
start = grid[AB.randomIntAtoB ( 0, cols - 1)][AB.randomIntAtoB ( 0, rows - 1)];
end = grid[AB.randomIntAtoB ( 0, cols - 1)][AB.randomIntAtoB ( 0, rows - 1)];
start.wall = false;
end.wall = false;
start.water = false;
end.water = false;
// openSet starts with beginning only
openSet.push(start);
console.log('start search');
}
function draw()
// the search goes on over many timesteps
// each timestep, check one more square and draw current partial solution
{
// Draw current state of everything
background(backcolor);
for (var i = 0; i < cols; i++)
for (var j = 0; j < rows; j++)
grid[i][j].show();
// for (var i = 0; i < closedSet.length; i++)
// closedSet[i].show( closedcolor );
// for (var i = 0; i < openSet.length; i++)
// openSet[i].show( opencolor );
// Find the path by working backwards
noFill();
stroke(pathcolor);
strokeWeight(w / 8);
beginShape();
for (var i = 0; i < 10; i++)
vertex ( (i * w) + w / 2, (i * h) + h / 2 );
endShape();
if (diagonal)
{
// path as continuous line
noFill();
stroke(pathcolor);
strokeWeight(w / 8);
beginShape();
for (var i = 0; i < path.length; i++)
vertex ( (path[i].i * w) + w / 2, (path[i].j * h) + h / 2 );
endShape();
}
else
{
// path as solid blocks
for (var i = 0; i < path.length; i++)
path[i].show(pathcolor);
}
}