nlcpy.where

nlcpy.where(condition, x=None, y=None)[ソース]

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.

`nonzero`

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

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.

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]])
```