parameters: (a, choices, out=None, mode='raise')
return_statement: return _wrapfunc(a, 'choose', choices, out=out, mode=mode)
docstring: Construct an array from an index array and a set 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.
      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
  
      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]]])
docstring_summary: Construct an array from an index array and a set of arrays to choose from.
docstring_tokens: Construct an array from an index array and a set of arrays to choose from .
function: def choose(a, choices, out=None, mode='raise'):
      """
      Construct an array from an index array and a set 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.
      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
  
      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]]])
  
      """
      return _wrapfunc(a, 'choose', choices, out=out, mode=mode)
function_tokens: def choose ( a , choices , out = None , mode = 'raise' ) : return _wrapfunc ( a , 'choose' , choices , out = out , mode = mode )