# where function¶

(Shortest import: from brian2 import where)

brian2.units.unitsafefunctions.where(condition[, x, y])[source]

Return elements chosen from x or y depending on condition.

Note

When only condition is provided, this function is a shorthand for np.asarray(condition).nonzero(). Using nonzero() directly should be preferred, as it behaves correctly for subclasses. The rest of this documentation covers only the case where all three arguments are provided.

Parameters

condition : array_like, bool

Where True, yield x, otherwise yield y.

x, y : array_like

Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns

out : ndarray

An array with elements from x where condition is True, and elements from y elsewhere.

choose()

nonzero()

The function that is called when x and y are omitted

Notes

If all the arrays are 1-D, where() is equivalent to:

[xv if c else yv
for c, xv, yv in zip(condition, x, y)]


Examples

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])


This can be used on multidimensional arrays too:

>>> np.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])


The shapes of x, y, and the condition are broadcast together:

>>> x, y = np.ogrid[:3, :4]
>>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
array([[10,  0,  0,  0],
[10, 11,  1,  1],
[10, 11, 12,  2]])

>>> a = np.array([[0, 1, 2],
...               [0, 2, 4],
...               [0, 3, 6]])
>>> np.where(a < 4, a, -1)  # -1 is broadcast
array([[ 0,  1,  2],
[ 0,  2, -1],
[ 0,  3, -1]])