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foapy.intervals_distribution

intervals_distribution(tuple_result)

Compute the frequency distribution of interval values.

Given the output of :func:intervals_tuple (or any 1-D array of strictly positive integers), counts how many times each interval value occurs. The result is a 1-D array of length max(tuple_result) where result[i] is the count of interval value i + 1.

Parameters:

Name Type Description Default

tuple_result

array_like

1-D array of strictly positive integers, typically produced by :func:intervals_tuple.

 required

Returns:

Type Description
ndarray

1-D integer array of length max(tuple_result) (or empty for empty input). result[i] equals the number of elements in tuple_result with value i + 1.

Examples:

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import numpy as np
from foapy.core import intervals_distribution

print(intervals_distribution(np.array([1, 2, 2, 4, 2])))
# [1 3 0 1]

print(intervals_distribution(np.array([])))
# []
Source code in .tox/docs-deploy/lib/python3.11/site-packages/foapy/core/_intervals_distribution.py 
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def intervals_distribution(tuple_result) -> ndarray:
    """
    Compute the frequency distribution of interval values.

    Given the output of :func:`intervals_tuple` (or any 1-D array of strictly
    positive integers), counts how many times each interval value occurs.
    The result is a 1-D array of length ``max(tuple_result)`` where
    ``result[i]`` is the count of interval value ``i + 1``.

    Parameters
    ----------
    tuple_result : array_like
        1-D array of strictly positive integers, typically produced by
        :func:`intervals_tuple`.

    Returns
    -------
    ndarray
        1-D integer array of length ``max(tuple_result)`` (or empty for
        empty input).  ``result[i]`` equals the number of elements in
        *tuple_result* with value ``i + 1``.

    Examples
    --------

    ``` py linenums="1"
    import numpy as np
    from foapy.core import intervals_distribution

    print(intervals_distribution(np.array([1, 2, 2, 4, 2])))
    # [1 3 0 1]

    print(intervals_distribution(np.array([])))
    # []
    ```
    """
    ar = np.asanyarray(tuple_result, dtype=np.intp)

    if ar.size == 0:
        return np.array([], dtype=np.intp)

    return np.bincount(ar - 1).astype(np.intp)