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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# for fft, the best implementation is given for N = 2^p
size = 16
# collection for test
collection = ot.ComplexCollection(size)
# Fill the data with artificial values
# Create a complex gaussian sample
for index in range(size):
realPart = 0.1 * (index + 1.0) / size
imagPart = 0.3 * (index + 1.0) / size
collection[index] = realPart + 1j * imagPart
myFFT = ot.KissFFT()
print("myFFT = ", myFFT)
# Initial transformation
print("collection = ", collection)
# FFT transform
transformedCollection = ot.ComplexCollection(myFFT.transform(collection))
print("FFT result = ", transformedCollection)
# Inverse transformation
inverseTransformedCollection = ot.ComplexCollection(
myFFT.inverseTransform(transformedCollection)
)
print("FFT back=", inverseTransformedCollection)
# 2D case now
N = 8
distribution = ot.Normal(N)
sample = distribution.getSample(2 * N)
# FFT transform
transformedSample = myFFT.transform2D(sample)
print("2D FFT result = ", repr(transformedSample))
# Inverse transformation
inverseTransformedSample = myFFT.inverseTransform2D(transformedSample)
print("2D FFT back=", repr(inverseTransformedSample.real()))
# 3D case
elements = [ot.RandomGenerator.Generate() for i in range(N * N * N)]
tensor = ot.ComplexTensor(N, N, N, elements)
# FFT transform
transformedTensor = myFFT.transform3D(tensor)
print("3D FFT result = ", repr(transformedTensor))
# Inverse transformation
inverseTransformedTensor = myFFT.inverseTransform3D(transformedTensor)
print("3D FFT back=", repr(inverseTransformedTensor.real()))
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