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numpy.s_ =
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A nicer way to build up index tuples for arrays.
Note
Use one of the two predefined instances
index_exp
ors_
rather than directly usingIndexExpression
.For any index combination, including slicing and axis insertion,
a[indices]
is the same asa[np.index_exp[indices]]
for any arraya
. 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
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index_exp
- Predefined instance that always returns a tuple:
index_exp = IndexExpression(maketuple=True)
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s_
- Predefined instance without tuple conversion:
s_ = IndexExpression(maketuple=False)
.
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
>>> 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])
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numpy.s_
2017-01-10 18:18:50
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