ORDERED_PAIR_INDEXING

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Returns the value of the OrderedPair at the requested index. Params: default : OrderedPair The input OrderedPair to index. index : int The index of the OrderedPair to return. x_axis : bool Index x axis? If not y is indexed. Returns: out : Scalar The scalar index of the input OrderedPair.

Python Code

from flojoy import flojoy, OrderedPair, Scalar


@flojoy
def ORDERED_PAIR_INDEXING(
    default: OrderedPair,
    index: int = 0,
    x_axis: bool = False,
) -> Scalar:
    """Returns the value of the OrderedPair at the requested index.

    Parameters
    ----------
    default : OrderedPair
        The input OrderedPair to index.
    index : int
        The index of the OrderedPair to return.
    x_axis : bool
        Index x axis? If not y is indexed.

    Returns
    -------
    Scalar
        The scalar index of the input OrderedPair.
    """

    assert (
        len(default.x) > index
    ), "The index parameter must be less than the length of the OrderedPair."
    assert index >= 0, "The index parameter must be greater than zero."

    if x_axis:
        c = default.x[index]
    else:
        c = default.y[index]

    return Scalar(c=c)

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This app uses ordered pair transformation techniques.

First the necessary blocks were added:

• LINSPACE
• SINE
• ORDERED_PAIR_LENGTH
• ORDERED_PAIR_INDEXING
• ORDERED_PAIR_DELETE
• 2x BIG_NUMBER
• TABLE

LINSPACE and SINE created an ordered pair data type (x and y axis pairs). ORDERED_PAIR_LENGTH extracts the length of an ordered pair (this length is viewed with BIG_NUMBER here). ORDERED_PAIR_INDEXING extracts a single value from either the x or y axes (this value is viewed with BIG_NUMBER here). ORDERED_PAIR_DELETE deletes single indexes from both the x or y axes and outputs a new ordered pair (the new ordered pair is viewed with BIG_NUMBER here).

The blocks were connected as shown and the app was run.