[SciPy-Dev] fftpack test failures for 0.8.0b1

David Goldsmith d.l.goldsmith@gmail....
Thu Jun 24 09:57:50 CDT 2010


On Thu, Jun 24, 2010 at 7:31 AM, Ralf Gommers
<ralf.gommers@googlemail.com>wrote:

>
> On Sat, Jun 19, 2010 at 2:55 AM, Ralf Gommers <ralf.gommers@googlemail.com
> > wrote:
>
>>
>> On Thu, Jun 17, 2010 at 11:48 PM, Charles R Harris <
>> charlesr.harris@gmail.com> wrote:
>>
>>>
>>> On Thu, Jun 17, 2010 at 8:50 AM, Ralf Gommers <
>>> ralf.gommers@googlemail.com> wrote:
>>>
>>>> Below are two test failures in fftpack. The test data is random, so they
>>>> happen only occasionally. I decreased the precision to decimal=5 and
>>>> maxulp=1e4, but still got one failure of each test in 200 test runs. The
>>>> TestSingleIFFT failure always occurs with an array of size 111. This is on
>>>> OS X 10.6, the same first error was also reported on 10.5.
>>>>
>>>> What should be done with these failures?
>>>>
>>>
>>> I guess the first thing is to figure out what is going on, and that needs
>>> something repeatable. Maybe the test could be repeated with a set of seeds
>>> until the problem appears. Also, since 111 = 3*37, I wonder what happens
>>> with other odd primes besides 37. It might be worth testing with 97 and
>>> other such.
>>>
>>> Attached is a small script to test with larger primes. You can supply a
>> fixed seed but it's not even necessary. Typical result:
>> arraysize, max error
>>    1 0.0
>>   17 4.76837e-07
>>   37 2.98023e-06
>>   97 0.000104427
>>  313 0.000443935
>>  701 0.00112867
>> 1447 0.00620008
>> 2011 0.0138307
>> 3469 0.16958
>>
>> So even decimal=4 would fail for 97 already. For larger primes the FFT
>> should be slower but not less accurate, right?
>>
>
> Any opinion on this? Is it easily fixable? This is the last thing holding
> up 0.8.0 I think, can we mark it knownfail
>

Is that something that just get's done in the release notes?


> for that or does anyone think it's important enough to delay the release
> for?
>
> Ralf
>
> If I use the numpy.fft.fft/ifft the result is:
>>    1 0.0
>>   17 7.54951656745e-15
>>   37 2.99760216649e-15
>>   97 4.99600361081e-15
>>  313 1.65423230669e-14
>>  701 5.66213742559e-15
>> 1447 3.0253577421e-14
>> 2011 1.87072579649e-14
>> 3469 1.09912079438e-14
>>
>> Looks a lot better.
>>
>
So how hard would it be to bypass whatever's doing the work in fftpack and
just call numpy.fft.fft?  Is it just a matter of someone writing such a
patch and testing it?

DG


>
>>
>
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