8000 numpy.allclose does not deal well with the minimal value of int* dtypes (Trac #1684) · Issue #2280 · numpy/numpy · GitHub
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numpy.allclose does not deal well with the minimal value of int* dtypes (Trac #1684) #2280

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numpy-gitbot opened this issue Oct 19, 2012 · 2 comments
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numpy.allclose does not deal well with the minimal value of int* dtypes (Trac #1684) #2280

numpy-gitbot opened this issue Oct 19, 2012 · 2 comments
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00 - Bug component: numpy._core
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(historic) NumPy 2.0

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Original ticket http://projects.scipy.org/numpy/ticket/1684 on 2010-11-22 by trac user lamblin, assigned to unknown.

If I create an array containing the minimal value of an int dtype, for instance an 'int8' array containing -128, numpy.allclose fails to detect it is "close" to itself, although it is equal.

>>> a = numpy.asarray(-128, dtype='int8')
>>> numpy.all(a == a)
True
>>> numpy.allclose(a, a)
False

I would expect:

>>> numpy.allclose(a, a)
True

This is due to the fact that the tolerance uses numpy.absolute, and in that case, it is negative:

>>> numpy.absolute(a)
array([-128], dtype=int8)

I reproduced the same with int16 and -215, int32 and -231, and int64 and -2**63.
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trac user lamblin wrote on 2010-11-22

A workaround is to upcast the second argument to float64 if its absolute value has a negative element:

def _allclose(a, b, rtol=1e-5, atol=1e-8):
    if str(b.dtype).startswith('int') and (numpy.absolute(b) < 0).any(): 
        b = numpy.asarray(b, dtype='float64') 

    return numpy.allclose(a, b, rtol=rtol, atol=atol)

I don't know if this should be fixed at the level of allclose, or of absolute.

@charris charris mentioned this issue Feb 15, 2014
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charris commented Feb 15, 2014

Fixed by merging #4105. Integers are now cast to floats.

@charris charris closed this as completed Feb 15, 2014
@chebee7i chebee7i mentioned this issue Feb 21, 2015
Merged
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