dask.array.choose
dask.array.choose¶
- dask.array.choose(a, choices)[source]¶
Construct an array from an index array and a list of arrays to choose from.
This docstring was copied from numpy.choose.
Some inconsistencies with the Dask version may exist.
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 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 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 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
- aint 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.- choicessequence 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”.- outarray, optional (Not supported in Dask)
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 (Not supported in Dask)
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_arrayarray
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]]])