On Tue, Mar 15, 2011 at 10:20 AM, Mark Sienkiewicz <[email protected]>wrote:
> The usual expectation is that (when possible) repr() returns a value
> that you can eval() to get the original data back. But,
>
> >>> from numpy import *
> >>> a = array( [ 16.50698631688883822 ] )
> >>> b = eval(repr(a))
> >>> a-b
> array([ -3.11116111e-09])
> >>> import numpy.testing
> >>> numpy.testing.assert_array_almost_equal(a,b,decimal=15)
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/usr/stsci/pyssgdev/2.7/numpy/testing/utils.py", line 775, in
> assert_array_almost_equal
> header=('Arrays are not almost equal to %d decimals' % decimal))
> File "/usr/stsci/pyssgdev/2.7/numpy/testing/utils.py", line 618, in
> assert_array_compare
> raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal to 15 decimals
>
> (mismatch 100.0%)
> x: array([ 16.50698632])
> y: array([ 16.50698632])
> >>>
>
> I noticed this because a bunch of tests failed exactly this way. Of
> course, the problem is that assert_array_almost_equal does not print
> with the precision that it compared, which in turn happens because it
> just uses repr() to convert the array.
>
> I would expect that repr would print the values at least to the
> resolution that they are stored, so I think this is a bug.
>
> This happens with the current trunk of numpy in python 2.7 on Red Hat
> Enterprise linux in 32 and 64 bits, and on Macintosh Leopard in 32
> bits. I did not try any other configuration.
>
>
Yes, I think it is a bug. IIRC, it also shows up for object arrays.
Chuck
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