numpy.fft.irfft¶
- numpy.fft.irfft(a, n=None, axis=-1)[source]¶
Compute the inverse of the n-point DFT for real input.
This function computes the inverse of the one-dimensional n-point discrete Fourier Transform of real input computed by rfft. In other words, irfft(rfft(a), len(a)) == a to within numerical accuracy. (See Notes below for why len(a) is necessary here.)
The input is expected to be in the form returned by rfft, i.e. the real zero-frequency term followed by the complex positive frequency terms in order of increasing frequency. Since the discrete Fourier Transform of real input is Hermite-symmetric, the negative frequency terms are taken to be the complex conjugates of the corresponding positive frequency terms.
Parameters : a : array_like
The input array.
n : int, optional
Length of the transformed axis of the output. For n output points, n//2+1 input points are necessary. If the input is longer than this, it is cropped. If it is shorter than this, it is padded with zeros. If n is not given, it is determined from the length of the input (along the axis specified by axis).
axis : int, optional
Axis over which to compute the inverse FFT.
Returns : out : ndarray
The truncated or zero-padded input, transformed along the axis indicated by axis, or the last one if axis is not specified. The length of the transformed axis is n, or, if n is not given, 2*(m-1) where m is the length of the transformed axis of the input. To get an odd number of output points, n must be specified.
Raises : IndexError
If axis is larger than the last axis of a.
See also
Notes
Returns the real valued n-point inverse discrete Fourier transform of a, where a contains the non-negative frequency terms of a Hermite-symmetric sequence. n is the length of the result, not the input.
If you specify an n such that a must be zero-padded or truncated, the extra/removed values will be added/removed at high frequencies. One can thus resample a series to m points via Fourier interpolation by: a_resamp = irfft(rfft(a), m).
Examples
>>> np.fft.ifft([1, -1j, -1, 1j]) array([ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]) >>> np.fft.irfft([1, -1j, -1]) array([ 0., 1., 0., 0.])
Notice how the last term in the input to the ordinary ifft is the complex conjugate of the second term, and the output has zero imaginary part everywhere. When calling irfft, the negative frequencies are not specified, and the output array is purely real.