jax.numpy.s_#
- jax.numpy.s_ = <numpy.lib._index_tricks_impl.IndexExpression object>#
A nicer way to build up index tuples for arrays.
Note
Use one of the two predefined instances
index_exp
or s_ rather than directly using IndexExpression.For any index combination, including slicing and axis insertion,
a[indices]
is the same asa[np.index_exp[indices]]
for any array a. However,np.index_exp[indices]
can be used anywhere in Python code and returns a tuple of slice objects that can be used in the construction of complex index expressions.- Parameters:
maketuple (bool) – If True, always returns a tuple.
See also
s_
Predefined instance without tuple conversion: s_ = IndexExpression(maketuple=False). The
index_exp
is another predefined instance that always returns a tuple: index_exp = IndexExpression(maketuple=True).
Notes
You can do all this with
slice
plus a few special objects, but there’s a lot to remember and this version is simpler because it uses the standard array indexing syntax.Examples
>>> import numpy as np >>> np.s_[2::2] slice(2, None, 2) >>> np.index_exp[2::2] (slice(2, None, 2),)
>>> np.array([0, 1, 2, 3, 4])[np.s_[2::2]] array([2, 4])