Discrete Fourier transforms (scipy.fft)

Fast Fourier Transforms

cupyx.scipy.fft.fft	Compute the one-dimensional FFT.
cupyx.scipy.fft.ifft	Compute the one-dimensional inverse FFT.
cupyx.scipy.fft.fft2	Compute the two-dimensional FFT.
cupyx.scipy.fft.ifft2	Compute the two-dimensional inverse FFT.
cupyx.scipy.fft.fftn	Compute the N-dimensional FFT.
cupyx.scipy.fft.ifftn	Compute the N-dimensional inverse FFT.
cupyx.scipy.fft.rfft	Compute the one-dimensional FFT for real input.
cupyx.scipy.fft.irfft	Compute the one-dimensional inverse FFT for real input.
cupyx.scipy.fft.rfft2	Compute the two-dimensional FFT for real input.
cupyx.scipy.fft.irfft2	Compute the two-dimensional inverse FFT for real input.
cupyx.scipy.fft.rfftn	Compute the N-dimensional FFT for real input.
cupyx.scipy.fft.irfftn	Compute the N-dimensional inverse FFT for real input.
cupyx.scipy.fft.hfft	Compute the FFT of a signal that has Hermitian symmetry.
cupyx.scipy.fft.ihfft	Compute the FFT of a signal that has Hermitian symmetry.

Code compatibility features

1. The boolean switch cupy.fft.config.enable_nd_planning also affects the FFT functions in this module, see FFT Functions. Moreover, as with other FFT modules in CuPy, FFT functions in this module can take advantage of an existing cuFFT plan (returned by cupyx.scipy.fftpack.get_fft_plan()) when used as a context manager.
2. Like in scipy.fft, all FFT functions in this module have an optional argument overwrite_x (default is False), which has the same semantics as in scipy.fft: when it is set to True, the input array x can (not will) be overwritten arbitrarily. This is not an in-place FFT, the user should always use the return value from the functions, e.g. x = cupyx.scipy.fft.fft(x, ..., overwrite_x=True, ...).
3. The cupyx.scipy.fft module can also be used as a backend for scipy.fft e.g. by installing with scipy.fft.set_backend(cupyx.scipy.fft). This can allow scipy.fft to work with both numpy and cupy arrays.

Note

scipy.fft requires SciPy version 1.4.0 or newer.