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import numpy
import nlcpy
from nlcpy import core
from nlcpy.request import request
[ドキュメント]def where(condition, x=None, y=None):
"""Returns elements chosen from *x* or *y* depending on *condition*.
Note
----
When only condition is provided, this function is a shorthand for
``nlcpy.asarray(condition).nonzero()``. Using nonzero directly should be preferred,
as it behaves correctly for subclasses. The rest of this documentation covers only
the case where all three arguments are provided.
Parameters
----------
condition : array_like, bool
Where True, yield *x*, otherwise yield *y*.
x, y : array_like
Values from which to choose. *x*, *y* and *condition* need to be broadcastable to
some shape.
Returns
-------
out : ndarray
An array with elements from *x* where *condition* is True, and elements from *y*
elsewhere.
Note
----
If all the arrays are 1-D, :func:`where` is equivalent to::
[xv if c else yv for c, xv, yv in zip(condition, x, y)]
See Also
--------
nonzero : Returns the indices of the elements
that are non-zero.
Examples
--------
>>> import nlcpy as vp
>>> a = vp.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> vp.where(a < 5, a, 10*a)
array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])
This can be used on multidimensional arrays too:
>>> vp.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
The shapes of x, y, and the condition are broadcast together:
>>> x = vp.arange(3).reshape([3,1])
>>> y = vp.arange(4).reshape([1,4])
>>> vp.where(x < y, x, 10 + y) # both x and 10+y are broadcast
array([[10, 0, 0, 0],
[10, 11, 1, 1],
[10, 11, 12, 2]])
>>> a = vp.array([[0, 1, 2],
... [0, 2, 4],
... [0, 3, 6]])
>>> vp.where(a < 4, a, -1) # -1 is broadcast
array([[ 0, 1, 2],
[ 0, 2, -1],
[ 0, 3, -1]])
"""
if condition is None:
condition = False
arr = nlcpy.asarray(condition)
if x is None and y is None:
return nlcpy.nonzero(arr)
if x is None or y is None:
raise ValueError("either both or neither of x and y should be given")
if not isinstance(x, nlcpy.ndarray):
x = numpy.asarray(x)
if not isinstance(y, nlcpy.ndarray):
y = numpy.asarray(y)
ret_type = numpy.result_type(x, y)
arr_x = nlcpy.asarray(x, dtype=ret_type)
arr_y = nlcpy.asarray(y, dtype=ret_type)
if arr.dtype != bool:
arr = (arr != 0)
values, shape = core._broadcast_core((arr, arr_x, arr_y))
ret = nlcpy.ndarray(shape=shape, dtype=ret_type)
request._push_request(
"nlcpy_where",
"indexing_op",
(ret, values[0], values[1], values[2]),)
return ret
[ドキュメント]def diag_indices(n, ndim=2):
"""Returns the indices to access the main diagonal of an array.
This returns a tuple of indices that can be used to access the main diagonal of an
array *a* with ``a.ndim >= 2`` dimensions and shape (n, n, ..., n).
For ``a.ndim = 2`` this is the usual diagonal, for ``a.ndim > 2`` this is the set of
indices to access ``a[i, i, ..., i]`` for ``i = [0..n-1]``.
Parameters
----------
n : int
The size, along each dimension, of the arrays for which the returned indices can
be used.
ndim : int, optional
The number of dimensions.
Examples
--------
Create a set of indices to access the diagonal of a (4, 4) array:
>>> import nlcpy as vp
>>> di = vp.diag_indices(4)
>>> di
(array([0, 1, 2, 3]), array([0, 1, 2, 3]))
>>> a = vp.arange(16).reshape(4, 4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> a[di] = 100
>>> a
array([[100, 1, 2, 3],
[ 4, 100, 6, 7],
[ 8, 9, 100, 11],
[ 12, 13, 14, 100]])
Now, we create indices to manipulate a 3-D array:
>>> d3 = vp.diag_indices(2, 3)
>>> d3
(array([0, 1]), array([0, 1]), array([0, 1]))
And use it to set the diagonal of an array of zeros to 1:
>>> a = vp.zeros((2, 2, 2), dtype=int)
>>> a[d3] = 1
>>> a
array([[[1, 0],
[0, 0]],
<BLANKLINE>
[[0, 0],
[0, 1]]])
"""
idx = nlcpy.arange(n)
return (idx,) * ndim