Issue with negative indexes

[MatthewCaseres]

From another issue

I've found an issue with the indexing in Tables - if you have multiple keys, an integer key going from 18-90 (say), and you look up 2, then you can get a false positive - it will find an earlier index. (2 would find the value for 90-16 for the prior key)

possible solution:
Can resolve by bounds checking. Possibly use optional bounds checking, like Table(safe=False) and I think it is best that safe=True by default. Safe=True performs any important validations that take time and safe=False doesn't?

[lewisfogden]

Agree - I almost think rectify() should be automatic, and bounds checking on by default.

need to test this:

if safe:
    if not np.all((min_value <= key) & (key <= max_value)):
       raise KeyError('blah')

Some options analysed here.
https://stackoverflow.com/questions/10542240/easy-way-to-test-if-each-element-in-an-numpy-array-lies-between-two-values

[lewisfogden]

Added in 923009c checks suggest a bit of a performance hit (can be disabled if you are confident in your data), I've also added rectify as an option.

[MatthewCaseres]

np.take with mode clip will prevent the wraparound. Could that be useful?

https://numpy.org/doc/stable/reference/generated/numpy.take.html

[lewisfogden]

Not sure how I would use it (some of the np docs can't be a bit cryptic for me!). Does that do the same thing as np.clip? Wondering here whether we should just always clip (i.e. bound), could always add a min - 1 index and max + 1 index both with values np.nan.