A Python smoke test for story-beat order in an automatically edited recap

A recap editor can maintain all necessary shots while disregarding the cause-and-effect order. A simple check against a review export can identify these instances prior to a person viewing the initial cut. In a hypothetical mystery, the crucial moment must occur before the detective attempts to open the door. We also wish to determine if any essential beat is missing. This illustration employs synthetic markers in milliseconds on the edited output timeline and Python’s built-in library.

The required sequence is defined as:

find_notebook, key_mark, try_door

The exported markers' timestamps are stored in a dictionary:

find_notebook: 14000, try_door: 42000, key_mark: 46000

The code then identifies any missing or out-of-order beats using list comprehensions. The expected output is:

Missing: []

Out of order: [(key_mark, try_door)]

To resolve any discrepancies, the sequence on the timeline should be corrected, the export updated, and the check performed again using timestamps from the revised export. It is important to note that while marker order can spot a disrupted sequence, it does not indicate whether a clue is displayed long enough for understanding.

If the narrative intentionally delays revealing the reason, the intended sequence must be explicitly stated in required_order at the beginning. The cover image provides a visual representation of the proposed workflow. This article, including its synthetic example, was created using artificial intelligence.