POPULATE

Generate an OrderedPair of random numbers, depending on the distribution selected and the input data. Inputs ------ default : OrderedPair|Vector Input to use as the x-axis for the random samples. Params: distribution : select the distribution over the random samples lower_bound : float the lower bound of the output interval upper_bound : float the upper bound of the output interval normal_mean : float the mean or "center" of the normal distribution normal_standard_deviation : float the spread or "width" of the normal distribution poisson_events : float the expected number of events occurring in a fixed time-interval when distribution is poisson Returns: out : OrderedPair x: provided from input data y: the random samples

Python Code

import random
from typing import Literal

import numpy as np
from flojoy import OrderedPair, Vector, display, flojoy


@flojoy
def POPULATE(
    default: OrderedPair | Vector,
    distribution: Literal["normal", "uniform", "poisson"] = "normal",
    lower_bound: float = 0,
    upper_bound: float = 1,
    normal_mean: float = 0,
    normal_standard_deviation: float = 1,
    poisson_events: float = 1,
) -> OrderedPair:
    """Generate an OrderedPair of random numbers, depending on the distribution selected and the input data.

    Inputs
    ------
    default : OrderedPair|Vector
        Input to use as the x-axis for the random samples.

    Parameters
    ----------
    distribution : select
        the distribution over the random samples
    lower_bound : float
        the lower bound of the output interval
    upper_bound : float
        the upper bound of the output interval
    normal_mean : float
        the mean or "center" of the normal distribution
    normal_standard_deviation : float
        the spread or "width" of the normal distribution
    poisson_events : float
        the expected number of events occurring in a fixed time-interval when distribution is poisson

    Returns
    -------
    OrderedPair
        x: provided from input data
        y: the random samples
    """

    if upper_bound < lower_bound:
        upper_bound, lower_bound = lower_bound, upper_bound

    seed = random.randint(1, 10000)
    my_generator = np.random.default_rng(seed)

    match default:
        case OrderedPair():
            size = len(default.x)
            x = default.x
        case Vector():
            size = len(default.v)
            x = default.v

    match distribution:
        case "uniform":
            y = my_generator.uniform(low=lower_bound, high=upper_bound, size=size)
        case "normal":
            y = my_generator.normal(
                loc=normal_mean, scale=normal_standard_deviation, size=size
            )
        case "poisson":
            y = my_generator.poisson(lam=poisson_events, size=size)

    return OrderedPair(x=x, y=y)


@display
def OVERLOAD(lower_bound, upper_bound, distribution="uniform") -> None:
    return None


@display
def OVERLOAD(  # noqa: F811
    normal_mean, normal_standard_deviation, distribution="normal"
) -> None:
    return None


@display
def OVERLOAD(poisson_events, distribution="poisson") -> None:  # noqa: F811
    return None

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Example

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In this example, LINSPACE is used to generate a list of 1000 samples, it is then passed into two POPULATE nodes, which randomizes the values within the list with a normal (or Gaussian) distribution and a Poisson distribution.

The distribution is then plotted with HISTOGRAM and as expected of a Gaussian distribution, the output of the HISTOGRAM node converges towards a bell curve. The Poisson distribution results in more of a step function.

The POPULATE node requires an input Vector or OrderedPair to function.