scipy.fftpack.fft¶

scipy.fftpack.
fft
(x, n=None, axis=1, overwrite_x=False)[source]¶ Return discrete Fourier transform of real or complex sequence.
The returned complex array contains
y(0), y(1),..., y(n1)
wherey(j) = (x * exp(2*pi*sqrt(1)*j*np.arange(n)/n)).sum()
. Parameters
 xarray_like
Array to Fourier transform.
 nint, optional
Length of the Fourier transform. If
n < x.shape[axis]
, x is truncated. Ifn > x.shape[axis]
, x is zeropadded. The default results inn = x.shape[axis]
. axisint, optional
Axis along which the fft’s are computed; the default is over the last axis (i.e.,
axis=1
). overwrite_xbool, optional
If True, the contents of x can be destroyed; the default is False.
 Returns
 zcomplex ndarray
with the elements:
[y(0),y(1),..,y(n/2),y(1n/2),...,y(1)] if n is even [y(0),y(1),..,y((n1)/2),y((n1)/2),...,y(1)] if n is odd
where:
y(j) = sum[k=0..n1] x[k] * exp(sqrt(1)*j*k* 2*pi/n), j = 0..n1
Notes
The packing of the result is “standard”: If
A = fft(a, n)
, thenA[0]
contains the zerofrequency term,A[1:n/2]
contains the positivefrequency terms, andA[n/2:]
contains the negativefrequency terms, in order of decreasingly negative frequency. So for an 8point transform, the frequencies of the result are [0, 1, 2, 3, 4, 3, 2, 1]. To rearrange the fft output so that the zerofrequency component is centered, like [4, 3, 2, 1, 0, 1, 2, 3], usefftshift
.Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non floatingpoint inputs will be converted to double precision. Longdouble precision inputs are not supported.
This function is most efficient when n is a power of two, and least efficient when n is prime.
Note that if
x
is realvalued thenA[j] == A[nj].conjugate()
. Ifx
is realvalued andn
is even thenA[n/2]
is real.If the data type of x is real, a “real FFT” algorithm is automatically used, which roughly halves the computation time. To increase efficiency a little further, use
rfft
, which does the same calculation, but only outputs half of the symmetrical spectrum. If the data is both real and symmetrical, thedct
can again double the efficiency, by generating half of the spectrum from half of the signal.Examples
>>> from scipy.fftpack import fft, ifft >>> x = np.arange(5) >>> np.allclose(fft(ifft(x)), x, atol=1e15) # within numerical accuracy. True