We are simulating a grid of octopi, i.e. of cellular automata. Each is represented by a natural number, and a step of the simulation goes like this:
The energy level of each cell (the number representing it) increases by 1.
If any cell’s energy level is greater than 9, then the energy level of each adjacent (including diagonally adjacent) cell increases by 1. Chain reactions may trigger at this point, but each cell can “flash” at most once each step.
The energy level of each cell that “flashed” becomes 0.
Once again, we have a grid-like type at the center of our puzzle. We require the following operations:
neighbors : Grid ℕ → (ℕ × ℕ) → [((ℕ × ℕ) × ℕ)] inc_all : Grid ℕ → Grid ℕ
As usual, we also assume Grid is a functor.
neighbors (i, j) gives a list of the neighbors of the cell at (i, j) paired with their energy levels. inc_all increments every cell in the grid; thus, it describes the first part of a step of the simulation. [Note: I didn’t actually check what information we were supposed to be collecting until after writing all of this. We are looking for the total number of flashes that occur, so some changes to the following are needed to return this information.]
step : Grid ℕ → Grid ℕ step = inc_all ; ... ?
Handling flashing cells and their effects (indeed) is where things get trickier. A flash is a nonlinear phenomenon, so we cannot model it compositionally. Each flashing cell can, in principle, transform the entire grid.
resolve_flashes : Grid ℕ → Grid ℕ reset_flashed : Grid ℕ → Grid ℕ step = inc_all ; resolve_flashes ; reset_flashed
Further complicating matters (though saving us from a truly off-the-rails simulation) is the fact that each cell can flash at most once per step. We therefore need to reference the set of cells that have flashed during the current step.
An important fact for working out resolve_flashes is that the set of cells that will flash during a given step shrinks by only one cell when that cell’s flash is resolved. A single flash may push many other cells into the set of flashers, but it can’t pull any out.
In other words, the set of flashing cells behaves like a stack.
To simplify things, we will consider the propagation of a flash as a computation using the parametric State type. Each computation produces a list of “new” flashing cells.
resolve_flashes : Grid ℕ → Grid ℕ resolve_flashes g = π₁ (execState (resolve (flashers g)) (g, ∅)) flashers : Grid ℕ → [(ℕ × ℕ)] flashers = keys ∘ filter (> 9) resolve : [(ℕ × ℕ)] → State ((Grid ℕ) × Set (ℕ × ℕ)) ⊤
⊤ here is the unit type, which has a single member,
() of type
(), not the greatest notation ever, IMO.)
Let’s try to figure out resolve. If the list cs of flashing cells is empty, then resolve is simply the identity transformer:
resolve  = pure tt
Otherwise, at least one flash occurs, so the state must be
transformed. Using monadic
resolve (c ∷ cs) = do new ← resolve1 c resolve (push_adj new cs) push_adj : Eq α ⇒ [α] → [α] → [α] push_adj xs ys = (filter (λ x → not (x ∈ ys)) xs) ⧺ ys
resolve1 (i, j) flashes the cell at (i, j), updates the state, and returns a list of the neighbors of (i, j) which have become flashers. We require that the address passed to resolve1 is never that of a cell that has already flashed during this step.
resolve1 : (ℕ × ℕ) → State ((Grid ℕ) × Set (ℕ × ℕ)) [(ℕ × ℕ)] resolve1 c = state (λ (g, s) → let g' = flash g c ns = neighbors g' c cs = list π₁ (filter ((> 9) ∘ π₂) ns) s′ = adjoin c s in (filter (not (∈ s′)) cs, (g', s′)))
state is a library function which embeds a function σ → (α × σ) (transforming a state σ and returning a value α) into the state monad; it gives us a pretty compact way to write resolve1. The extensive transformation of the list of neighbors gives us the list of new flashers; first, we filter for cells with energy above 9, then we remove those in the “already-flashed” set.
flash c increments the neighbors of the cell at c:
flash : Grid ℕ → (ℕ × ℕ) → Grid ℕ flash g c = map_with_key (λ d n → if in_square c d ∧ c ≠ d then n + 1 else n) g
map_with_key : ((ℕ × ℕ) → α → β) → Grid α → Grid β is a variant of functorial application in the Grid type that passes the address as well as the element to the applied function.
The function in_square : (ℕ × ℕ) → (ℕ × ℕ) determines whether the second address is within the 3 × 3 square surrounding and including the first.
We now have a definition for step, with which we can write a function that runs the simulation for a given number of steps:
steps : ℕ → Grid → Grid steps n = iterateN step n
where iterateN f n x = f (f … (f x)) (n repeated applications of f).
This is a clean-enough process, but I’d like to be a bit more formal. We didn’t really come up with any equations describing this simulation; instead, the above was more a matter of picking a way to model it that “seemed good”, then elaborating it.
Executable Haskell solution
Haskellized short test input
This is much the same as Part 1, except we will now count the number of steps it takes to reach a certain state; namely, a state where all the cells flash at once. Since a flash resets a cell, to zero, we can express this by the predicate
all_flashed : Grid ℕ → Bool all_flashed = all (== 0)
where all p is true iff every cell of a grid satisfies p.
We can express the step-counting with:
steps_until : (Grid → Bool) → Grid → ℕ steps_until p g = length (takeWhile (¬ p) (iterate step g))
where iterate f x produces the infinite list of repeated applications of f: [x, f x, f (f x), …].
solution : Grid → ℕ solution = steps_until all_flashed
Executable Haskell solution
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