-
numpy.choose(a, choices, out=None, mode='raise')
[source] -
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 ofn
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 eachi
. Then, a new array with shapeBa.shape
is created as follows:- if
mode=raise
(the default), then, first of all, each element ofa
(and thusBa
) must be in the range[0, n-1]
; now, suppose thati
(in that range) is the value at the(j0, j1, ..., jm)
position inBa
- then the value at the same position in the new array is the value inBchoices[i]
at that same position; - if
mode=wrap
, values ina
(and thusBa
) 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 ina
(and thusBa
) may be any (signed) integer; negative integers are mapped to 0; values greater thann-1
are mapped ton-1
; and then the new array is constructed as above.
Parameters: a : int array
This array must contain integers in
[0, n-1]
, wheren
is the number of choices, unlessmode=wrap
ormode=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. Ifchoices
is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding tochoices.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]]])
- if
numpy.choose()
2017-01-10 18:13:04
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