# xorbits.numpy.choose#

xorbits.numpy.choose(a, choices, out=None, mode='raise')[source]#

Construct an array from an index array and a list of arrays to choose from.

First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks):

`np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])`.

But this omits some subtleties. Here is a fully general summary:

Given an “index” array (a) of integers and a sequence of `n` arrays (choices), a and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these Ba and Bchoices[i], i = 0,…,n-1 we have that, necessarily, `Ba.shape == Bchoices[i].shape` for each `i`. Then, a new array with shape `Ba.shape` is created as follows:

• if `mode='raise'` (the default), then, first of all, each element of `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that `i` (in that range) is the value at the `(j0, j1, ..., jm)` position in `Ba` - then the value at the same position in the new array is the value in `Bchoices[i]` at that same position;

• if `mode='wrap'`, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;

• if `mode='clip'`, values in a (and thus `Ba`) may be any (signed) integer; negative integers are mapped to 0; values greater than `n-1` are mapped to `n-1`; and then the new array is constructed as above.

Parameters
• a (int array) – This array must contain integers in `[0, n-1]`, where `n` is the number of choices, unless `mode=wrap` or `mode=clip`, in which cases any integers are permissible.

• choices (sequence of arrays) – Choice arrays. a and all of the choices must be broadcastable to the same shape. If choices is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to `choices.shape[0]`) is taken as defining the “sequence”.

• out (array, optional) – If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Note that out is always buffered if `mode='raise'`; use other modes for better performance.

• mode ({'raise' (default), 'wrap', 'clip'}, optional) –

Specifies how indices outside `[0, n-1]` will be treated:

• ’raise’ : an exception is raised

• ’wrap’ : value becomes value mod `n`

• ’clip’ : values < 0 are mapped to 0, values > n-1 are mapped to n-1

Returns

merged_array – The merged result.

Return type

array

Raises

ValueError – shape mismatch: If a and each choice array are not all broadcastable to the same shape.

`ndarray.choose`

equivalent method

`numpy.take_along_axis`

Preferable if choices is an array

Notes

To reduce the chance of misinterpretation, even though the following “abuse” is nominally supported, choices should neither be, nor be thought of as, a single array, i.e., the outermost sequence-like container should be either a list or a tuple.

Examples

```>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
...   [20, 21, 22, 23], [30, 31, 32, 33]]
>>> np.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... )
array([20, 31, 12,  3])
>>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
array([20, 31, 12,  3])
>>> # because there are 4 choice arrays
>>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
array([20,  1, 12,  3])
>>> # i.e., 0
```

A couple examples illustrating how choose broadcasts:

```>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> np.choose(a, choices)
array([[ 10, -10,  10],
[-10,  10, -10],
[ 10, -10,  10]])
```
```>>> # With thanks to Anne Archibald
>>> a = np.array([0, 1]).reshape((2,1,1))
>>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1,  1,  1,  1,  1],
[ 2,  2,  2,  2,  2],
[ 3,  3,  3,  3,  3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
```

This docstring was copied from numpy.