ifft(a, n=None, axis=-1, norm=None)¶
Compute the one-dimensional inverse discrete Fourier Transform.
This function computes the inverse of the one-dimensional n-point
discrete Fourier transform computed by
fft. In other words,
ifft(fft(a)) == a to within numerical accuracy.
For a general description of the algorithm and definitions,
The input should be ordered in the same way as is returned by
a should contain the zero frequency term,
a[1:n//2] should contain the positive-frequency terms,
a[n//2 + 1:] should contain the negative-frequency terms, in
increasing order starting from the most negative frequency.
For an even number of input points,
A[n//2] represents the sum of
the values at the positive and negative Nyquist frequencies, as the two
are aliased together. See
numpy.fft for details.
Input array, can be complex.
Length of the transformed axis of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zeros. If n is not given, the length of the input along the axis specified by axis is used. See notes about padding issues.
Axis over which to compute the inverse DFT. If not given, the last axis is used.
New in version 1.10.0.
Normalization mode (see
numpy.fft). Default is None.
The truncated or zero-padded input, transformed along the axis indicated by axis, or the last one if axis is not specified.
If axes is larger than the last axis of a.
If the input parameter n is larger than the size of the input, the input
is padded by appending zeros at the end. Even though this is the common
approach, it might lead to surprising results. If a different padding is
desired, it must be performed before calling
>>> np.fft.ifft([0, 4, 0, 0]) array([ 1.+0.j, 0.+1.j, -1.+0.j, 0.-1.j]) # may vary
Create and plot a band-limited signal with random phases:
>>> import matplotlib.pyplot as plt >>> t = np.arange(400) >>> n = np.zeros((400,), dtype=complex) >>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,))) >>> s = np.fft.ifft(n) >>> plt.plot(t, s.real, 'b-', t, s.imag, 'r--') [<matplotlib.lines.Line2D object at ...>, <matplotlib.lines.Line2D object at ...>] >>> plt.legend(('real', 'imaginary')) <matplotlib.legend.Legend object at ...> >>> plt.show()