choose(a, choices, out=None, mode='raise')
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 (:None:None:`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 :None:None:`Ba`) may be any (signed) integer; modular arithmetic is used to map integers outside the range :None:None:`[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.
To reduce the chance of misinterpretation, even though the following "abuse" is nominally supported, :None:None:`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.
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.
Choice arrays. a and all of the choices must be broadcastable to the same shape. If :None:None:`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".
If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Note that :None:None:`out` is always buffered if mode='raise'
; use other modes for better performance.
Specifies how indices outside [0, n-1]
will be treated:
If a and each choice array are not all broadcastable to the same shape.
The merged result.
Construct an array from an index array and a list of arrays to choose from.
ndarray.choose
equivalent method
numpy.take_along_axis
Preferable if :None:None:`choices` is an array
>>> 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 ArchibaldSee :
... 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]]])
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.piecewise
dask.array.routines.choose
numpy.core._multiarray_umath.where
numpy.compress
numpy.where
dask.array.core.Array.choose
numpy.ma.core.choose
numpy.select
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