fft of numpy and octave different on transpose - numpy

Question

First of i know there is an identical question with answer in SO here: FFT in Matlab and numpy / scipy give different results
but the answer given there does not work on the test i did:
when i do an fft from numpy.fft i get following result:
In [30]: numpy.fft.fft(numpy.array([1+0.5j, 3+0j, 2+0j, 8+3j]))
Out[30]: array([ 14.+3.5j, -4.+5.5j, -8.-2.5j, 2.-4.5j])
which is identical to the output of in my case octave)
octave:39> fft([1+0.5j,3+0j,2+0j,8+3j])
ans =
Columns 1 through 3:
14.0000 + 3.5000i -4.0000 + 5.5000i -8.0000 - 2.5000i
Column 4:
2.0000 - 4.5000i
but if i transpose the list in octave and python i get:
In [9]: numpy.fft.fft(numpy.array([1+0.5j, 3+0j, 2+0j, 8+3j]).transpose())
Out[9]: array([ 14.+3.5j, -4.+5.5j, -8.-2.5j, 2.-4.5j])
and for octave:
octave:40> fft([1+0.5j,3+0j,2+0j,8+3j]')
ans =
14.0000 - 3.5000i
2.0000 + 4.5000i
-8.0000 + 2.5000i
-4.0000 - 5.5000i
I also tried to reshape in python but this results in:
In [33]: numpy.fft.fft(numpy.reshape(numpy.array([1+0.5j,3+0j,2+0j,8+3j]), (4,1)))
Out[33]:
array([[ 1.+0.5j],
[ 3.+0.j ],
[ 2.+0.j ],
[ 8.+3.j ]])
how do i get the same result in python as in octave?
+ i don't have matlab to test, otherwise i would check if it returns the same as octave just to be sure.

Answer 1

Why NumPy and octave gave different results:
The inputs were different. The ' in octave is returning the complex conjugate transpose, not the transpose, .':
octave:6> [1+0.5j,3+0j,2+0j,8+3j]'
ans =
1.0000 - 0.5000i
3.0000 - 0.0000i
2.0000 - 0.0000i
8.0000 - 3.0000i
So to make NumPy's result match octave's:
In [115]: np.fft.fft(np.array([1+0.5j, 3+0j, 2+0j, 8+3j]).conj()).reshape(-1, 1)
Out[115]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])
octave:7> fft([1+0.5j,3+0j,2+0j,8+3j]')
ans =
14.0000 - 3.5000i
2.0000 + 4.5000i
-8.0000 + 2.5000i
-4.0000 - 5.5000i
In NumPy, the transpose of a 1D array is the same 1D array.
That's why fft(np.array([1+0.5j, 3+0j, 2+0j, 8+3j]).transpose()) returns a 1D array.
Reshaping after taking the FFT of a 1D array:
You could take the FFT first, and then reshape. To make a 1D array 2-dimensional you could use reshape to obtain a column-like array of shape (4,1), or use np.atleast_2d followed by transpose:
In [115]: np.fft.fft(np.array([1+0.5j, 3+0j, 2+0j, 8+3j]).conj()).reshape(-1, 1)
Out[115]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])
or
In [116]: np.atleast_2d(np.fft.fft(np.array([1+0.5j, 3+0j, 2+0j, 8+3j]).conj())).T
Out[116]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])
Taking the FFT of a 2D array:
np.fft.fft takes the FFT over the last axis by default.
This is why reshaping to shape (4, 1) did not work. Instead, reshape the array to (1, 4):
In [117]: np.fft.fft(np.reshape(np.array([1+0.5j,3+0j,2+0j,8+3j]), (1,4)).conj()).T
Out[117]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])
Or you could use np.matrix to
make a 2D matrix of shape (1, 4).
Again the FFT is taken over the last axis, returns an array of shape (1, 4), which you can then transpose to get the desired result:
In [121]: np.fft.fft(np.matrix([1+0.5j, 3+0j, 2+0j, 8+3j]).conj()).T
Out[121]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])
This, perhaps, gives you the neatest syntax. But be aware that this passes a np.matrix as input but returns a np.ndarray as output.
As Warren Weckesser pointed out, if you already have a 2D NumPy array, and wish to take the FFT of its columns, then you could pass axis=0 to the call to np.fft.fft.
Also, The matrix class (unlike the ndarray class) has a H property which returns the complex conjugate transpose. Thus
In [114]: np.fft.fft(np.matrix([1+0.5j, 3+0j, 2+0j, 8+3j]).H, axis=0)
Out[114]:
array([[ 14.-3.5j],
[ 2.+4.5j],
[ -8.+2.5j],
[ -4.-5.5j]])