Re: ezfftf

FYI: I have added Example 3 to the ezfftf documentation.
This shows how to compute the (biased) variance of a series
depending upon odd or even series length.

http://www.ncl.ucar.edu/Document/Functions/Built-in/ezfftf.shtml

On 12/2/11 9:21 AM, Dennis Shea wrote:
> Actually, after another test, it depends on if the
> the series length is even or odd. This determine
> how the last coefficient should be handled.
>
> if (N%2 .eq. 0) then
> var_zfft2 = 0.5*sum(z(0:nfreq-2)) + z(nfreq-1) ; even
> else
> var_zfft2 = 0.5*sum(z) ; odd
> end if
>
> On 12/2/11 8:33 AM, Dennis Shea wrote:
>> ezfftf is a direct call to the FFTPACK routine at:
>> http://www.netlib.org/fftpack/ezfftf.f
>> or, with better documentation
>> ftp://ftp.ucar.edu/dsl/lib/fftpack/ezfftf.f
>>
>> ===
>> As with any FFT, the issue is with how the coefficients are normalized.
>> For ezfftf:
>>
>> var_zfft2 = 0.5*sum(z(0:nfreq-2)) + z(nfreq-1)
>>
>> This is a *biased* estimate of the variance. [ie: 1/N and not 1/(N-1) ]
>> ===
>> I was a bit surprised. I thought the variance would be
>> var_zfft2 = 0.5*sum(z)
>> but the core documentation indicates differently.
>>
>> Attached is a test script.
>>
>>
>> On 12/2/11 2:01 AM, Wolfgang Langhans wrote:
>>> Hi,
>>>
>>> I am a bit confused about the outcome of the ezfftf Fourier Forward
>>> analysis. Should not the sum over all squared norms equal the total
>>> biased variance of the data series? How can I understand the different
>>> results obtained in the little example below? How comes that the sum
>>> amounts almost twice the actual variance?
>>>
>>> Thanks for your help in understanding this!
>>> Wolfgang
>>>
>>> ;======================================================
>>> low = -5.0
>>> high = 5.0
>>> con = (high - low) / 32766.0 ; 32766.0 forces a 0.0 to 1.0 range
>>> Z = new(100,float)
>>> do i=0,dimsizes(Z)-1
>>> Z(i) = low + con * rand()
>>> end do
>>>
>>> zmean = avg(Z)
>>> zvarbiased = 1./dimsizes(Z) * sum( (Z-zmean)^2 ) ;total biased
>>> variance of original data
>>>
>>> zfft = ezfftf(Z)
>>> z = zfft(0,:)^2 + zfft(1,:)^2
>>> nfreq = dimsizes(z)
>>> totvar = sum(z(0:nfreq-1)) ; sum over all squared norms of the
>>> complex Fourier transforms
>>>
>>> print("Variance: "+ zvarbiased + " Sum over spectrum: "+totvar )
>>> ;======================================================
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>>
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Received on Fri Dec 2 09:39:51 2011