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