bundles / scipy 1.18.0 / scipy / fft / _basic / irfft2
_Function
scipy.fft._basic:irfft2
source: /scipy/fft/_basic.py:1306
Signature
def irfft2(x, s = None, axes = (-2, -1), norm = None, overwrite_x = False, workers = None, *, plan = None)Summary
Computes the inverse of rfft2.
Parameters
x: array_likeThe input array
s: sequence of ints, optionalShape of the real output to the inverse FFT.
axes: sequence of ints, optionalThe axes over which to compute the inverse fft. Default is the last two axes.
norm: {"backward", "ortho", "forward"}, optionalNormalization mode (see fft). Default is "backward".
overwrite_x: bool, optionalIf True, the contents of
xcan be destroyed; the default is False. See fft for more details.workers: int, optionalMaximum number of workers to use for parallel computation. If negative, the value wraps around from
os.cpu_count(). See fft for more details.plan: object, optionalThis argument is reserved for passing in a precomputed plan provided by downstream FFT vendors. It is currently not used in SciPy.
Returns
out: ndarrayThe result of the inverse real 2-D FFT.
Notes
This is really irfftn with different defaults. For more details see irfftn.
Array API Standard Support
irfft2 has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.
==================== ==================== ==================== Library CPU GPU ==================== ==================== ==================== NumPy ✅ n/a CuPy n/a ✅ PyTorch ✅ ✅ JAX ✅ ✅ Dask ⚠️ computes graph n/a ==================== ==================== ====================
See dev-arrayapi for more information.
See also
Aliases
scipy.fft.irfft2