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.

Helper functions for FFT

cupyx.scipy.fft.next_fast_len

Find the next fast size to fft.

Code compatibility features

1. As with other FFT modules in CuPy, FFT functions in this module can take advantage of an existing cuFFT plan (returned by get_fft_plan()) to accelarate the computation. The plan can be either passed in explicitly via the keyword-only plan argument or used as a context manager.

2. The boolean switch cupy.fft.config.enable_nd_planning also affects the FFT functions in this module, see FFT Functions. This switch is neglected when planning manually using get_fft_plan().

3. 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. For this reason, when an in-place FFT is desired, the user should always reassign the input in the following manner: x = cupyx.scipy.fftpack.fft(x, ..., overwrite_x=True, ...).

4. 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.

5. The boolean switch cupy.fft.config.use_multi_gpus also affects the FFT functions in this module, see FFT Functions. Moreover, this switch is honored when planning manually using get_fft_plan().

Note

scipy.fft requires SciPy version 1.4.0 or newer.

Note

To use scipy.fft.set_backend() together with an explicit plan argument requires SciPy version 1.5.0 or newer.