Allen, Probably it needs to work in n-dimensions, like the existing np.fft.fftshift function does, with an optional axis=tuple parameter. I recall that fftshift is just an array indexing trick? It would be helpful to see what's faster, two fftshifts and a edge padding or your inter-padding. Probably it's faster to make a new zeros array of the appropriate padded size and do 2*ndim copies?
Robert On Wed, May 25, 2016 at 9:35 PM, Allen Welkie <allen.wel...@gmail.com> wrote: > I'd like to get some feedback on my [pull request]( > https://github.com/numpy/numpy/pull/7593). > > This pull request adds a function `ifftpad` which pads a spectrum by > inserting zeros where the highest frequencies would be. This is necessary > because the padding that `ifft` does simply inserts zeros at the end of the > array. But because of the way the spectrum is laid out, this changes which > bins represent which frequencies and in general messes up the result of > `ifft`. If you pad with the proposed `ifftpad` function, the zeros will be > inserted in the middle of the spectrum and the time signal that results > from `ifft` will be an interpolated version of the unpadded time signal. > See the discussion in [issue #1346]( > https://github.com/numpy/numpy/issues/1346). > > The following is a script to demonstrate what I mean: > > ``` > import numpy > from numpy import concatenate, zeros > from matplotlib import pyplot > > def correct_padding(a, n, scale=True): > """ A copy of the proposed `ifftpad` function. """ > spectrum = concatenate((a[:len(a) // 2], > zeros(n - len(a)), > a[len(a) // 2:])) > if scale: > spectrum *= (n / len(a)) > return spectrum > > def plot_real(signal, label): > time = numpy.linspace(0, 1, len(signal) + 1)[:-1] > pyplot.plot(time, signal.real, label=label) > > def main(): > spectrum = numpy.zeros(10, dtype=complex) > spectrum[-1] = 1 + 1j > > signal = numpy.fft.ifft(spectrum) > signal_bad_padding = numpy.fft.ifft(10 * spectrum, 100) > signal_good_padding = numpy.fft.ifft(correct_padding(spectrum, 100)) > > plot_real(signal, 'No padding') > plot_real(signal_bad_padding, 'Bad padding') > plot_real(signal_good_padding, 'Good padding') > > pyplot.legend() > pyplot.show() > > > if __name__ == '__main__': > main() > ``` > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- Robert McLeod, Ph.D. Center for Cellular Imaging and Nano Analytics (C-CINA) Biozentrum der Universität Basel Mattenstrasse 26, 4058 Basel Work: +41.061.387.3225 robert.mcl...@unibas.ch robert.mcl...@bsse.ethz.ch <robert.mcl...@ethz.ch> robbmcl...@gmail.com
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