# numpy.choose

numpy.choose(a, choices, out=None, mode='raise')

  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 array

      The merged result.

  Raises:
    ValueError: shape mismatch

      If a and each choice array are not all broadcastable to the same shape.

  See also

  `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]]])