add beehave

addons/beehave/nodes/composites/randomized_composite.gd

@@ -0,0 +1,176 @@
+@tool
+class_name RandomizedComposite extends Composite
+
+const WEIGHTS_PREFIX = "Weights/"
+
+## Sets a predicable seed
+@export var random_seed: int = 0:
+	set(rs):
+		random_seed = rs
+		if random_seed != 0:
+			seed(random_seed)
+		else:
+			randomize()
+
+## Wether to use weights for every child or not.
+@export var use_weights: bool:
+	set(value):
+		use_weights = value
+		if use_weights:
+			_update_weights(get_children())
+			_connect_children_changing_signals()
+		notify_property_list_changed()
+
+var _weights: Dictionary
+var _exiting_tree: bool
+
+
+func _ready():
+	_connect_children_changing_signals()
+
+
+func _connect_children_changing_signals():
+	if not child_entered_tree.is_connected(_on_child_entered_tree):
+		child_entered_tree.connect(_on_child_entered_tree)
+
+	if not child_exiting_tree.is_connected(_on_child_exiting_tree):
+		child_exiting_tree.connect(_on_child_exiting_tree)
+
+
+func get_shuffled_children() -> Array[Node]:
+	var children_bag: Array[Node] = get_children().duplicate()
+	if use_weights:
+		var weights: Array[int]
+		weights.assign(children_bag.map(func(child): return _weights[child.name]))
+		children_bag.assign(_weighted_shuffle(children_bag, weights))
+	else:
+		children_bag.shuffle()
+	return children_bag
+
+
+## Returns a shuffled version of a given array using the supplied array of weights.
+## Think of weights as the chance of a given item being the first in the array.
+func _weighted_shuffle(items: Array, weights: Array[int]) -> Array:
+	if len(items) != len(weights):
+		push_error(
+			(
+				"items and weights size mismatch: expected %d weights, got %d instead."
+				% [len(items), len(weights)]
+			)
+		)
+		return items
+
+	# This method is based on the weighted random sampling algorithm
+	# by Efraimidis, Spirakis; 2005. This runs in O(n log(n)).
+
+	# For each index, it will calculate random_value^(1/weight).
+	var chance_calc = func(i): return [i, randf() ** (1.0 / weights[i])]
+	var random_distribuition = range(len(items)).map(chance_calc)
+
+	# Now we just have to order by the calculated value, descending.
+	random_distribuition.sort_custom(func(a, b): return a[1] > b[1])
+
+	return random_distribuition.map(func(dist): return items[dist[0]])
+
+
+func _get_property_list():
+	var properties = []
+
+	if use_weights:
+		for key in _weights.keys():
+			properties.append(
+				{
+					"name": WEIGHTS_PREFIX + key,
+					"type": TYPE_INT,
+					"usage": PROPERTY_USAGE_STORAGE | PROPERTY_USAGE_EDITOR,
+					"hint": PROPERTY_HINT_RANGE,
+					"hint_string": "1,100"
+				}
+			)
+
+	return properties
+
+
+func _set(property: StringName, value: Variant) -> bool:
+	if property.begins_with(WEIGHTS_PREFIX):
+		var weight_name = property.trim_prefix(WEIGHTS_PREFIX)
+		_weights[weight_name] = value
+		return true
+
+	return false
+
+
+func _get(property: StringName):
+	if property.begins_with(WEIGHTS_PREFIX):
+		var weight_name = property.trim_prefix(WEIGHTS_PREFIX)
+		return _weights[weight_name]
+
+	return null
+
+
+func _update_weights(children: Array[Node]) -> void:
+	var new_weights = {}
+	for c in children:
+		if _weights.has(c.name):
+			new_weights[c.name] = _weights[c.name]
+		else:
+			new_weights[c.name] = 1
+	_weights = new_weights
+	notify_property_list_changed()
+
+
+func _exit_tree() -> void:
+	_exiting_tree = true
+
+
+func _enter_tree() -> void:
+	_exiting_tree = false
+
+
+func _on_child_entered_tree(node: Node):
+	_update_weights(get_children())
+
+	var renamed_callable = _on_child_renamed.bind(node.name, node)
+	if not node.renamed.is_connected(renamed_callable):
+		node.renamed.connect(renamed_callable)
+
+	if not node.tree_exited.is_connected(_on_child_tree_exited):
+		node.tree_exited.connect(_on_child_tree_exited.bind(node))
+
+
+func _on_child_exiting_tree(node: Node):
+	var renamed_callable = _on_child_renamed.bind(node.name, node)
+	if node.renamed.is_connected(renamed_callable):
+		node.renamed.disconnect(renamed_callable)
+
+
+func _on_child_tree_exited(node: Node) -> void:
+	# don't erase the individual child if the whole tree is exiting together
+	if not _exiting_tree:
+		var children = get_children()
+		children.erase(node)
+		_update_weights(children)
+
+	if node.tree_exited.is_connected(_on_child_tree_exited):
+		node.tree_exited.disconnect(_on_child_tree_exited)
+
+
+func _on_child_renamed(old_name: String, renamed_child: Node):
+	if old_name == renamed_child.name:
+		return  # No need to update the weights.
+
+	# Disconnect signal with old name...
+	renamed_child.renamed.disconnect(_on_child_renamed.bind(old_name, renamed_child))
+	# ...and connect with the new name.
+	renamed_child.renamed.connect(_on_child_renamed.bind(renamed_child.name, renamed_child))
+
+	var original_weight = _weights[old_name]
+	_weights.erase(old_name)
+	_weights[renamed_child.name] = original_weight
+	notify_property_list_changed()
+
+
+func get_class_name() -> Array[StringName]:
+	var classes := super()
+	classes.push_back(&"RandomizedComposite")
+	return classes