numpy.fft.irfftn(a, s=None, axes=None, norm=None) [source] Compute the inverse of the N-dimensional FFT of real input. This function computes the inverse of the N-dimensional discrete Fourier Transform for real input over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, irfftn(rfftn(a), a.shape) == a to within numerical accuracy. (The a.shape is necessary like len(a) is for irfft, and for the same reason.) The input should be ordered

numpy.fft.irfft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional inverse FFT of a real array. Parameters: a : array_like The input array s : sequence of ints, optional Shape of the inverse FFT. axes : sequence of ints, optional The axes over which to compute the inverse fft. Default is the last two axes. norm : {None, ?ortho?}, optional New in version 1.10.0. Normalization mode (see numpy.fft). Default is None. Returns: out : ndarray The result of the i

numpy.fft.irfft(a, n=None, axis=-1, norm=None) [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 frequenc

numpy.fft.ihfft(a, n=None, axis=-1, norm=None) [source] Compute the inverse FFT of a signal which has Hermitian symmetry. Parameters: a : array_like Input array. n : int, optional Length of the inverse FFT. Number of points along transformation axis in the input to use. 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. axis : int, optio

numpy.fft.ifftshift(x, axes=None) [source] The inverse of fftshift. Although identical for even-length x, the functions differ by one sample for odd-length x. Parameters: x : array_like Input array. axes : int or shape tuple, optional Axes over which to calculate. Defaults to None, which shifts all axes. Returns: y : ndarray The shifted array. See also fftshift Shift zero-frequency component to the center of the spectrum. Examples >>> freqs = np.fft.fftfreq(9, d=1./9

numpy.fft.ifftn(a, s=None, axes=None, norm=None) [source] Compute the N-dimensional inverse discrete Fourier Transform. This function computes the inverse of the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ifftn(fftn(a)) == a to within numerical accuracy. For a description of the definitions and conventions used, see numpy.fft. The input, analogously to ifft, should be ordered in the

numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional inverse discrete Fourier Transform. This function computes the inverse of the 2-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ifft2(fft2(a)) == a to within numerical accuracy. By default, the inverse transform is computed over the last two axes of the input array. The input, analogously to ifft, shoul

numpy.fft.ifft(a, n=None, axis=-1, norm=None) [source] 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, see numpy.fft. The input should be ordered in the same way as is returned by fft, i.e., a[0] should contain the zero frequency term, a[1:n//

numpy.fft.hfft(a, n=None, axis=-1, norm=None) [source] Compute the FFT of a signal which has Hermitian symmetry (real spectrum). 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 axi

numpy.fft.fftshift(x, axes=None) [source] Shift the zero-frequency component to the center of the spectrum. This function swaps half-spaces for all axes listed (defaults to all). Note that y[0] is the Nyquist component only if len(x) is even. Parameters: x : array_like Input array. axes : int or shape tuple, optional Axes over which to shift. Default is None, which shifts all axes. Returns: y : ndarray The shifted array. See also ifftshift The inverse of fftshift. Examples &g