Legacy Discrete Fourier transforms (scipy.fftpack)


As of SciPy version 1.4.0, scipy.fft is recommended over scipy.fftpack. Consider using cupyx.scipy.fft instead.

Fast Fourier Transforms


Compute the one-dimensional FFT.


Compute the one-dimensional inverse FFT.


Compute the two-dimensional FFT.


Compute the two-dimensional inverse FFT.


Compute the N-dimensional FFT.


Compute the N-dimensional inverse FFT.


Compute the one-dimensional FFT for real input.


Compute the one-dimensional inverse FFT for real input.


Generate a CUDA FFT plan for transforming up to three axes.

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 plan argument or used as a context manager. The argument plan is currently experimental and the interface may be changed in the future version. The get_fft_plan() function has no counterpart in scipy.fftpack.

  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.fftpack, all FFT functions in this module have an optional argument overwrite_x (default is False), which has the same semantics as in scipy.fftpack: 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 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().