digitize

class brainunit.math.digitize(x, bins, right=False, **kwargs)

Return the indices of the bins to which each value in input array belongs.

right	order of bins	returned index i satisfies
False	increasing	bins[i-1] <= x < bins[i]
True	increasing	bins[i-1] < x <= bins[i]
False	decreasing	bins[i-1] > x >= bins[i]
True	decreasing	bins[i-1] >= x > bins[i]

If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate.

Parameters:

• x (Array | ndarray | bool | number | bool | int | float | complex | saiunit.Quantity) – Input array to be binned. Prior to NumPy 1.10.0, this array had to be 1-dimensional, but can now have any shape.

• bins (Array | ndarray | bool | number | bool | int | float | complex | saiunit.Quantity) – Array of bins. It has to be 1-dimensional and monotonic.

• right (bool) – Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval does not include the right edge. The left bin end is open in this case, i.e., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins.

Returns:

indices – Output array of bin indices, same shape as x.

Return type:

Array

Examples

>>> import saiunit as u
>>> import jax.numpy as jnp
>>> x = jnp.array([0.5, 1.5, 2.5])
>>> bins = jnp.array([0.0, 1.0, 2.0, 3.0])
>>> u.math.digitize(x, bins)
Array([1, 2, 3], dtype=int32)