nlcpy.where

nlcpy.where(condition, x=None, y=None)[source]

Returns elements chosen from x or y depending on condition.

Parameters

conditionarray_like, bool: Where True, yield x, otherwise yield y.
x, yarray_like: Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns

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

See also

nonzero: Returns the indices of the elements that are non-zero.

Note

When only condition is provided, this function is a shorthand for nlcpy.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.

Note

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

>>> import nlcpy as vp
>>> a = vp.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> vp.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:

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