131 lines
2.7 KiB
Python
131 lines
2.7 KiB
Python
|
# mypy: ignore-errors
|
||
|
|
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import functools
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from . import _dtypes_impl, _util
|
||
|
|
from ._normalizations import ArrayLike, normalizer
|
||
|
|
|
||
|
|
|
||
|
|
def upcast(func):
|
||
|
|
"""NumPy fft casts inputs to 64 bit and *returns 64-bit results*."""
|
||
|
|
|
||
|
|
@functools.wraps(func)
|
||
|
|
def wrapped(tensor, *args, **kwds):
|
||
|
|
target_dtype = (
|
||
|
|
_dtypes_impl.default_dtypes().complex_dtype
|
||
|
|
if tensor.is_complex()
|
||
|
|
else _dtypes_impl.default_dtypes().float_dtype
|
||
|
|
)
|
||
|
|
tensor = _util.cast_if_needed(tensor, target_dtype)
|
||
|
|
return func(tensor, *args, **kwds)
|
||
|
|
|
||
|
|
return wrapped
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def fft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.fft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def ifft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.ifft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def rfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.rfft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def irfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.irfft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def fftn(a: ArrayLike, s=None, axes=None, norm=None):
|
||
|
|
return torch.fft.fftn(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def ifftn(a: ArrayLike, s=None, axes=None, norm=None):
|
||
|
|
return torch.fft.ifftn(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def rfftn(a: ArrayLike, s=None, axes=None, norm=None):
|
||
|
|
return torch.fft.rfftn(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def irfftn(a: ArrayLike, s=None, axes=None, norm=None):
|
||
|
|
return torch.fft.irfftn(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def fft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
||
|
|
return torch.fft.fft2(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def ifft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
||
|
|
return torch.fft.ifft2(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def rfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
||
|
|
return torch.fft.rfft2(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def irfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
||
|
|
return torch.fft.irfft2(a, s, dim=axes, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def hfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.hfft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
@upcast
|
||
|
|
def ihfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
||
|
|
return torch.fft.ihfft(a, n, dim=axis, norm=norm)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
def fftfreq(n, d=1.0):
|
||
|
|
return torch.fft.fftfreq(n, d)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
def rfftfreq(n, d=1.0):
|
||
|
|
return torch.fft.rfftfreq(n, d)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
def fftshift(x: ArrayLike, axes=None):
|
||
|
|
return torch.fft.fftshift(x, axes)
|
||
|
|
|
||
|
|
|
||
|
|
@normalizer
|
||
|
|
def ifftshift(x: ArrayLike, axes=None):
|
||
|
|
return torch.fft.ifftshift(x, axes)
|