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Computer Science Data Structures

Animora includes nine stateful data structures (animora.datastructures) designed for computer science education. Each structure pairs a pure Python data model (XModel) with an animation generation layer (X).

1. Supported Structures

  1. Array (ArrayListModel): Linear indexed sequence with animate_swap(), animate_highlight(), animate_set().
  2. Stack (StackModel): LIFO stack with animate_push(), animate_pop(), animate_peek().
  3. Queue (QueueModel): FIFO queue with animate_enqueue(), animate_dequeue(), animate_peek().
  4. LinkedList (LinkedListModel): Singly-linked list with pointer transitions (animate_insert_tail()).
  5. Heap (HeapModel): Binary min-heap with sift-up/sift-down animations.
  6. Tree (GenericTreeModel): N-ary tree positioned via Phase 4 TreeLayout.
  7. BST (BSTModel): Binary search tree with path-traced insertions and full 3-case deletion (leaf, 1-child, 2-children).
  8. Graph (GraphModel): Network positioned via GraphLayout with state primitives (animate_highlight_node(), animate_mark_visited(), animate_highlight_edge()).
  9. HashTable (HashTableChainingModel): Hash table using Separate Chaining collision handling.

2. Usage Example: Binary Search Tree

from animora.core import Scene
from animora.datastructures import BST


class BSTDemo(Scene):
    def construct(self) -> None:
        bst = BST([50, 30, 70, 20, 40])
        self.play(bst.animate_create())
        # Traverses [50, 30, 40] before placing 35:
        self.play(bst.animate_insert(35))
        # Search highlight:
        self.play(bst.animate_search(35))
        # Complete 2-children deletion with in-order successor:
        self.play(bst.animate_delete(30))