Randomizing background generation

webpack/js/mlab.js

@@ -21,13 +21,17 @@ function paintTree() {
  const TRUNK_COL = '#444' //'#7fc0c2'
  const RAY_COL = '#fff' //'#fff'
  const ROOT_COL = '#444' //'#222'
- const LBLS_PER_FRAME = 1
 
  const graph = $('#graph')
  const W = graph.width()
  const H = graph.height()
  const N_X = Math.max(10, Math.floor(W / GRID_UNIT)) - 1
  const N_Y = Math.max(10, Math.floor(H / GRID_UNIT)) - 1
+
+ console.log(`Grid graph: ${N_X}×${N_Y}`)
+
+ const LBLS_PER_FRAME = N_X * N_Y / 250
+
  const embed = pos => [(W - N_X * GRID_UNIT) / 2 + pos[0] * GRID_UNIT, (H - N_Y * GRID_UNIT) / 2 + pos[1] * GRID_UNIT]
 
  let edges = {}
@@ -39,7 +43,22 @@ function paintTree() {
  const grid = new GridGraph(N_X, N_Y)
 
  function node_feasibility(node) {
- return 1.;
+ let pos = grid.getNodeXY(node)
+ pos[0] /= N_X
+ pos[1] /= N_Y
+ const center = [0.5, 0.3]
+ const radius = 0.2
+ const falloff = 0.3
+ const exp = 0.8
+ const rand_fraction = 0.3
+ // Actual math:
+ const dx = center[0] - pos[0]
+ const dy = center[1] - pos[1]
+ const l2 = Math.sqrt(dx * dx + dy * dy)
+ const unit_l2 = Math.max(Math.min((l2 - radius) / falloff, 1.), 0.)
+ const l2_pow_law = Math.pow(unit_l2, exp)
+ const rand_lerp = l2_pow_law * (1. - rand_fraction) + rand_fraction * Math.random()
+ return rand_lerp
  }
 
  // Generate nodes and arcs
@@ -62,18 +81,15 @@ function paintTree() {
 
  grid.directions.forEach(dir => dir_l2_len[dir2idx(dir)] = Math.sqrt(dir[0] * dir[0] + dir[1] * dir[1]))
 
- const RANDOM_EDGE_COST_WEIGHT = 0.5
- const NODE_WEIGHT = 10.
-
  function edge_cost(dijkstra, node, edge) {
  const dir = grid.getEdgeDirection(edge)
  // base l2 cost
- let cost = dir_l2_len[dir2idx(dir)]
+ const l2 = dir_l2_len[dir2idx(dir)]
+ const node_weight = 5.
+ const rand_fraction = 0.6
  // Add a random component
- cost += RANDOM_EDGE_COST_WEIGHT * Math.random()
- // Node component. This is really not symmetric, but ok
- cost /= NODE_WEIGHT * node_feasibility(node)
- return cost
+ const artificial_cost = (1. + (Math.random() - 1.) * rand_fraction) * node_weight * (1. - node_feasibility(node))
+ return l2 + artificial_cost
  }
 
  let algo = new Dijkstra(grid, edge_cost, {