def genarray(shape):
fft = randn(shape, dtype) + 1j * randn(shape, dtype)
k = fftfreq(shape[1:-1], mode='absolute')
shape_fac = math.sqrt(math.mean(shape[1:-1])) # 16: 4, 64: 8, 256: 24,
fft *= (1 / (k + 1))**power * power * shape_fac
array = math.real(math.ifft(fft)).astype(dtype)
return array
def fourier_poisson(tensor, times=1):
""" Inverse operation to `fourier_laplace`. """
frequencies = math.fft(math.to_complex(tensor))
k = fftfreq(math.staticshape(tensor)[1:-1], mode='square')
fft_laplace = -(2 * np.pi)**2 * k
fft_laplace[(0, ) * math.ndims(k)] = np.inf
inv_fft_laplace = 1 / fft_laplace
inv_fft_laplace[(0, ) * math.ndims(k)] = 0
return math.real(math.ifft(frequencies * inv_fft_laplace**times))
def fourier_laplace(tensor, times=1):
"""
Applies the spatial laplce operator to the given tensor with periodic boundary conditions.
*Note:* The results of `fourier_laplace` and `laplace` are close but not identical.
This implementation computes the laplace operator in Fourier space.
The result for periodic fields is exact, i.e. no numerical instabilities can occur, even for higher-order derivatives.
:param tensor: tensor, assumed to have periodic boundary conditions
:param times: number of times the laplace operator is applied. The computational cost is independent of this parameter.
:return: tensor of same shape as `tensor`
"""
frequencies = math.fft(math.to_complex(tensor))
k = fftfreq(math.staticshape(tensor)[1:-1], mode='square')
fft_laplace = -(2 * np.pi)**2 * k
return math.real(math.ifft(frequencies * fft_laplace**times))
def fourier_laplace(tensor):
frequencies = math.fft(math.to_complex(tensor))
k = fftfreq(math.staticshape(tensor)[1:-1], mode='square')
fft_laplace = -(2 * np.pi)**2 * k
return math.real(math.ifft(frequencies * fft_laplace))