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avoid use of deprecated numpy.bool alias #1485
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I believe that setting
key.dtype == bool
may be more efficient in this case. This comparison checks if thedata type (dtype)
of key is exactlybool
. On the other hand, usingkey.dtype in (bool, np.bool_)
checks if the dtype is eitherbool
ornp.bool_
. This approach involves iterating over the elements of the tuple to find a match, which could potentially be slower.There was a problem hiding this comment.
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You are right that my proposed change would be technically slower, involving 2 equality checks instead of one, but note that
bool
is the first element in_BOOLEAN_TYPES
, the second check is only performed in casekey.dtype != bool
key.dtype is bool
would be the way to go (identity check is always cheaper than equality check, although here it's 20ns VS 24ns on my machine)There was a problem hiding this comment.
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Considering the potential impact on simulation time when the code is called multiple times (considering large data), it becomes more important to optimize the comparison. In that case, using
key.dtype == bool
would be more efficient compared tokey.dtype in (bool, np.bool_)
due to the reduced number of operations involved. However, it's still advisable to measure the actual performance gain before making a decision, as the difference might still be minimal.There was a problem hiding this comment.
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Does the proposed approach ensure some kind of backward compatibility? Are there configurations that may use one vs the other? Or is
bool
always available so that any invocation will always work if we restrict tobool
? Put another way, what's the benefit to this proposed approach?There was a problem hiding this comment.
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My approach is supposed to be 100% backward compatible. Additionally, it survives
np.bool_
(current implementation doesn't)