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