def ifftn(x, shape=None, axes=None, overwrite_x=False, plan=None):
"""Compute the N-dimensional inverse FFT.
Args:
x (cupy.ndarray): Array to be transformed.
shape (None or tuple of ints): Shape of the transformed axes of the
output. If ``shape`` is not given, the lengths of the input along
the axes specified by ``axes`` are used.
axes (tuple of ints): Axes over which to compute the FFT.
overwrite_x (bool): If True, the contents of ``x`` can be destroyed.
plan (:class:`cupy.cuda.cufft.PlanNd` or ``None``): a cuFFT plan for
transforming ``x`` over ``axes``, which can be obtained using::
plan = cupyx.scipy.fftpack.get_fft_plan(x, axes)
Note that `plan` is defaulted to None, meaning CuPy will either
use an auto-generated plan behind the scene if cupy.fft.config.
enable_nd_planning = True, or use no cuFFT plan if it is set to
False.
Returns:
cupy.ndarray:
The transformed array which shape is specified by ``shape`` and
type will convert to complex if that of the input is another.
.. seealso:: :func:`scipy.fftpack.ifftn`
.. note::
The argument `plan` is currently experimental and the interface may be
changed in the future version.
"""
func = _default_fft_func(x, shape, axes, plan)
return func(x, shape, axes, None, cufft.CUFFT_INVERSE,
overwrite_x=overwrite_x, plan=plan)
def rfftn(x, s=None, axes=None, norm=None, overwrite_x=False, *, plan=None):
"""Compute the N-dimensional FFT for real input.
Args:
a (cupy.ndarray): Array to be transform.
s (None or tuple of ints): Shape to use from the input. If ``s`` is not
given, the lengths of the input along the axes specified by
``axes`` are used.
axes (tuple of ints): Axes over which to compute the FFT.
norm (None or ``"ortho"``): Keyword to specify the normalization mode.
overwrite_x (bool): If True, the contents of ``x`` can be destroyed.
plan (:class:`cupy.cuda.cufft.PlanNd` or ``None``): a cuFFT plan for
transforming ``x`` over ``axes``, which can be obtained using::
plan = cupyx.scipy.fftpack.get_fft_plan(x, s, axes,
value_type='R2C')
Note that ``plan`` is defaulted to ``None``, meaning CuPy will use
an auto-generated plan behind the scene.
Returns:
cupy.ndarray:
The transformed array which shape is specified by ``s`` and type
will convert to complex if the input is other. The length of the
last axis transformed will be ``s[-1]//2+1``.
.. seealso:: :func:`scipy.fft.rfftn`
"""
s = _assequence(s)
axes = _assequence(axes)
func = _default_fft_func(x, s, axes, value_type='R2C')
return func(x, s, axes, norm, cufft.CUFFT_FORWARD, 'R2C',
overwrite_x=overwrite_x, plan=plan)
def ifftn(x, s=None, axes=None, norm=None, overwrite_x=False, *, plan=None):
"""Compute the N-dimensional inverse FFT.
Args:
x (cupy.ndarray): Array to be transformed.
s (None or tuple of ints): Shape of the transformed axes of the
output. If ``s`` is not given, the lengths of the input along
the axes specified by ``axes`` are used.
axes (tuple of ints): Axes over which to compute the FFT.
norm (None or ``'ortho'``): Normalization mode.
overwrite_x (bool): If True, the contents of ``x`` can be destroyed.
plan (:class:`cupy.cuda.cufft.PlanNd` or ``None``): a cuFFT plan for
transforming ``x`` over ``axes``, which can be obtained using::
plan = cupyx.scipy.fftpack.get_fft_plan(x, s, axes)
Note that ``plan`` is defaulted to ``None``, meaning CuPy will use
an auto-generated plan behind the scene.
Returns:
cupy.ndarray:
The transformed array which shape is specified by ``s`` and
type will convert to complex if that of the input is another.
.. seealso:: :func:`scipy.fft.ifftn`
"""
s = _assequence(s)
axes = _assequence(axes)
func = _default_fft_func(x, s, axes)
return func(x, s, axes, norm, cufft.CUFFT_INVERSE, overwrite_x=overwrite_x,
plan=plan)
def test_fftn_multiple_plan_error(self, dtype):
import cupy
import cupyx.scipy.fftpack as fftpack
x = testing.shaped_random(self.shape, cupy, dtype)
# hack: avoid testing the cases when getting a cuFFT plan is impossible
if _default_fft_func(x, s=self.s, axes=self.axes) is not _fftn:
return
plan = fftpack.get_fft_plan(x, shape=self.s, axes=self.axes)
with pytest.raises(RuntimeError) as ex, plan:
fftpack.fftn(x, shape=self.s, axes=self.axes, plan=plan)
assert 'Use the cuFFT plan either as' in str(ex.value)
def test_ifftn_plan(self, xp, scp, dtype):
x = testing.shaped_random(self.shape, xp, dtype)
# hack: avoid testing the cases when getting a cuFFT plan is impossible
if _default_fft_func(x, s=self.s, axes=self.axes) is not _fftn:
return x
if scp is cupyx.scipy:
import cupy.fft.config as config
config.enable_nd_planning = False # use explicit plan
plan = scp.fftpack.get_fft_plan(x, shape=self.s, axes=self.axes)
out = scp.fftpack.ifftn(x, shape=self.s, axes=self.axes, plan=plan)
config.enable_nd_planning = True # default
else: # scipy
out = scp.fftpack.ifftn(x, shape=self.s, axes=self.axes)
return out
def test_ifftn_plan_manager(self, xp, scp, dtype):
x = testing.shaped_random(self.shape, xp, dtype)
# hack: avoid testing the cases when getting a cuFFT plan is impossible
if _default_fft_func(x, s=self.s, axes=self.axes) is not _fftn:
return x
if scp is cupyx.scipy:
from cupy.cuda.cufft import get_current_plan
plan = scp.fftpack.get_fft_plan(x, shape=self.s, axes=self.axes)
with plan:
assert id(plan) == id(get_current_plan())
out = scp.fftpack.ifftn(x, shape=self.s, axes=self.axes)
assert get_current_plan() is None
else: # scipy
out = scp.fftpack.ifftn(x, shape=self.s, axes=self.axes)
return out
def irfftn(x, s=None, axes=None, norm=None, overwrite_x=False, *, plan=None):
"""Compute the N-dimensional inverse FFT for real input.
Args:
a (cupy.ndarray): Array to be transform.
s (None or tuple of ints): Shape of the output. If ``s`` is not given,
they are determined from the lengths of the input along the axes
specified by ``axes``.
axes (tuple of ints): Axes over which to compute the FFT.
norm (``"backward"``, ``"ortho"``, or ``"forward"``): Optional keyword
to specify the normalization mode. Default is ``None``, which is
an alias of ``"backward"``.
overwrite_x (bool): If True, the contents of ``x`` can be destroyed.
plan (:class:`cupy.cuda.cufft.PlanNd` or ``None``): a cuFFT plan for
transforming ``x`` over ``axes``, which can be obtained using::
plan = cupyx.scipy.fftpack.get_fft_plan(x, s, axes,
value_type='C2R')
Note that ``plan`` is defaulted to ``None``, meaning CuPy will use
an auto-generated plan behind the scene.
Returns:
cupy.ndarray:
The transformed array which shape is specified by ``s`` and type
will convert to complex if the input is other. If ``s`` is not
given, the length of final transformed axis of output will be
``2*(m-1)`` where `m` is the length of the final transformed axis
of the input.
.. seealso:: :func:`scipy.fft.irfftn`
"""
s = _assequence(s)
axes = _assequence(axes)
if (10020 >= cupy.cuda.runtime.runtimeGetVersion() >= 10010
and int(cupy.cuda.device.get_compute_capability()) < 70
and _size_last_transform_axis(x.shape, s, axes) == 2):
warnings.warn('Output of irfftn might not be correct due to issue '
'of cuFFT in CUDA 10.1/10.2 on Pascal or older GPUs.')
func = _default_fft_func(x, s, axes, value_type='C2R')
return func(x,
s,
axes,
norm,
cufft.CUFFT_INVERSE,
'C2R',
overwrite_x=overwrite_x,
plan=plan)
