def test_invalid_sizes(self):
with assert_raises(ValueError,
match="invalid number of data points"
r" \(\[1, 0\]\) specified"):
ifftn([[]])
with assert_raises(ValueError,
match="invalid number of data points"
r" \(\[4, -3\]\) specified"):
ifftn([[1, 1], [2, 2]], (4, -3))
def test_definition(self, dtype, cdtype, maxnlp):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype)
y = ifftn(x)
assert_equal(y.dtype, cdtype)
assert_array_almost_equal_nulp(y, direct_idftn(x), maxnlp)
x = random((20, 26))
assert_array_almost_equal_nulp(ifftn(x), direct_idftn(x), maxnlp)
x = random((5, 4, 3, 20))
assert_array_almost_equal_nulp(ifftn(x), direct_idftn(x), maxnlp)
def ifftn(x, shape=None, axes=None, overwrite_x=False):
"""
Return inverse multidimensional discrete Fourier transform.
The sequence can be of an arbitrary type.
The returned array contains::
y[j_1,..,j_d] = 1/p * sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
x[k_1,..,k_d] * prod[i=1..d] exp(sqrt(-1)*2*pi/n_i * j_i * k_i)
where ``d = len(x.shape)``, ``n = x.shape``, and ``p = prod[i=1..d] n_i``.
For description of parameters see `fftn`.
See Also
--------
fftn : for detailed information.
Examples
--------
>>> from scipy.fftpack import fftn, ifftn
>>> import numpy as np
>>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
>>> np.allclose(y, ifftn(fftn(y)))
True
"""
shape = _good_shape(x, shape, axes)
return _pocketfft.ifftn(x, shape, axes, None, overwrite_x)
def test_no_axes(self):
x = numpy.random.random((2, 2, 2))
assert_allclose(ifftn(x, axes=[]), x, atol=1e-7)
def test_random_complex(self, maxnlp, size):
x = random([size, size]) + 1j * random([size, size])
assert_array_almost_equal_nulp(ifftn(fftn(x)), x, maxnlp)
assert_array_almost_equal_nulp(fftn(ifftn(x)), x, maxnlp)
