Uncle Chop’s Rocket Shop includes two minigames with repeatable combinatorial puzzles. These small scripts model the underlying rules and generate candidate solutions.
The Pancake Mixer presents you with a required set of pancake types and a stack of a given size. You need to construct a valid arrangement from the available ingredients. The catch: certain combinations are invalid, and the stack size constrains how many you can use.
The script generates all valid configurations up front, given the required pancakes and stack size as input. It's essentially a constrained permutation problem — enumerate permutations, filter against validity rules, present the options.
def generate_stacks(required, size):
all_combos = itertools.permutations(required, size)
return [stack for stack in all_combos if is_valid(stack)]
def is_valid(stack):
# Adjacent pancakes can't be the same type
return all(stack[i] != stack[i+1] for i in range(len(stack)-1))The output lists every valid stack configuration, which you can then pick from based on what ingredients the game gives you.
The Security Cracker is a different kind of puzzle. It presents a sequence of dice and a captcha pattern. You need to invert certain dice values and match the captcha sequence to unlock the panel.
The script takes the current dice values and the target sequence as input and computes the exact inversions needed. Dice inversion in this context means flipping a die to its opposite face — on a standard die, opposite faces sum to 7, so the inverse of 3 is 4, the inverse of 1 is 6, and so on.
def invert_die(value):
return 7 - value
def solve_cracker(current_dice, target_sequence):
steps = []
for i, target in enumerate(target_sequence):
current = current_dice[i]
if current != target:
steps.append(f"Die {i+1}: invert {current} → {invert_die(current)}")
return stepsThe project is a small exercise in translating game rules into constraints and automating a repeated decision process.
Beyond the puzzles, the project demonstrates a reusable pattern: generate candidate states, validate them against constraints, and present the valid results. Python’s itertools is well suited to this kind of task.