Hi,
numpy's Fourier transforms have the handy feature of being able to
upsample and downsample signals; for example the documentation cites
irfft(rfft(A),16*len(A)) as a way to get a Fourier interpolation of A.
However, there is a peculiarity with the way numpy handles the
highest-frequency
Anne,
On 8/29/07, Anne Archibald [EMAIL PROTECTED] wrote:
Hi,
numpy's Fourier transforms have the handy feature of being able to
upsample and downsample signals; for example the documentation cites
irfft(rfft(A),16*len(A)) as a way to get a Fourier interpolation of A.
However, there is a
On 8/29/07, Charles R Harris [EMAIL PROTECTED] wrote:
Anne,
On 8/29/07, Anne Archibald [EMAIL PROTECTED] wrote:
Hi,
numpy's Fourier transforms have the handy feature of being able to
upsample and downsample signals; for example the documentation cites
irfft(rfft(A),16*len(A)) as a
On 29/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
What is going on is that the coefficient at the Nyquist frequency appears
once in the unextended array, but twice when the array is extended with
zeros because of the Hermitean symmetry. That should probably be fixed in
the upsampling
On 29/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
Is this also appropriate for the other FFTs? (inverse real, complex,
hermitian, what have you) I have written a quick hack (attached) that
should do just that rescaling, but I don't know that it's a good idea,
as implemented.
On 8/29/07, Anne Archibald [EMAIL PROTECTED] wrote:
On 29/08/2007, Charles R Harris [EMAIL PROTECTED] wrote:
Is this also appropriate for the other FFTs? (inverse real, complex,
hermitian, what have you) I have written a quick hack (attached) that
should do just that rescaling, but I
