Integers

ixia.rand_below

Link: Original section for secrets.randbelow

def rand_below(n: int) -> int

Returns a random int in the range $[0, n)$.

ixia.rand_bits

Link: Original section for random.getrandbits

def rand_bits(k: int) -> int

Returns a non-negative Python integer with k random bits.

ixia.rand_bool

def rand_bool(p: float = 0.5) -> bool

Returns a random bool with a probability p of being true (0.5 by default).

ixia.rand_int

Link: Original section for random.randint

def rand_int(a: int, b: int) -> int

Returns a random integer N in the range $[a, b]$. Equivalent to ixia.rand_range(a, b+1).

ixia.rand_ints

def rand_ints(a: int, b: int, *, k: int) -> list[int]

Returns a list of k random integrs in the range $[a, b]$.
Equivalent to [rand_int(a, b) for _ in range(k)].

ixia.rand_range

Link: Original section for random.randrange

def rand_range(start: int, stop: int | None = None, step: int = 1) -> int

Returns a randomly selected element from range(start, stop, step). This is equivalent to ixia.choice(range(start, stop, step)), but doesn't actually build a range object.

The positional argument pattern matches that of range(). Keyword arguments should not be used because the function may use them in unexpected ways.

ixia.universe_rand

def universe_rand() -> int

Generates a random number based on the universe.

Thanks to the work of the recent Nobel Prize laureates, it was possible to code this function that computes a random number by simulating universe dimensions phasing on gamma ray emission using the sum of the spins of the pair positron/electron in a normalized Higgs field. Surprisingly, Taylor series is involved in this beautiful mathematical operation. It may or may not always return $42$, we do not know.