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xݕɬ['dZddlmZmZmZmZmZddlmZm Z gdZ e efZ ddZ ee ddd Zee ddd Ze ddd Ze ddd ZdS)z* Discrete Fourier Transforms - helper.py )integeremptyarangeasarrayroll)array_function_dispatch set_module)fftshift ifftshiftfftfreqrfftfreqNc|fSN)xaxess c/builddir/build/BUILD/cloudlinux-venv-1.0.8/venv/lib64/python3.11/site-packages/numpy/fft/helper.py_fftshift_dispatcherrs 4Kz numpy.fft)modulect|3ttj}djD}n4t |t rj|dz}nfd|D}t||S)ad 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 -------- >>> freqs = np.fft.fftfreq(10, 0.1) >>> freqs array([ 0., 1., 2., ..., -3., -2., -1.]) >>> np.fft.fftshift(freqs) array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.]) Shift the zero-frequency component only along the second axis: >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3) >>> freqs array([[ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.]]) >>> np.fft.fftshift(freqs, axes=(1,)) array([[ 2., 0., 1.], [-4., 3., 4.], [-1., -3., -2.]]) Ncg|]}|dzSr.0dims r zfftshift..Cs---c---rrc0g|]}j|dzSrshaperaxrs rrzfftshift..Gs$111b!111rrtuplerangendimr! isinstance integer_typesrrrshifts` rr r sZ  A |U16]]##--QW--- D- ( (2 "1111D111 5$  rct|3ttj}djD}n5t |t rj|dz }nfd|D}t||S)a/ 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).reshape(3, 3) >>> freqs array([[ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.]]) >>> np.fft.ifftshift(np.fft.fftshift(freqs)) array([[ 0., 1., 2.], [ 3., 4., -4.], [-3., -2., -1.]]) Ncg|]}|dz  Srrrs rrzifftshift..rs0003!8000rrc2g|]}j|dz Srr r"s rrzifftshift..vs'444172;!#$444rr$r*s` rr r LsF  A |U16]]##00000 D- ( (5'$-1$%4444t444 5$  r?c$t|tstdd||zz }t|t}|dz dzdz}t d|t}||d|<t |dz dt}|||d<||zS)a- Return the Discrete Fourier Transform sample frequencies. The returned float array `f` contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length `n` and a sample spacing `d`:: f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd Parameters ---------- n : int Window length. d : scalar, optional Sample spacing (inverse of the sampling rate). Defaults to 1. Returns ------- f : ndarray Array of length `n` containing the sample frequencies. Examples -------- >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float) >>> fourier = np.fft.fft(signal) >>> n = signal.size >>> timestep = 0.1 >>> freq = np.fft.fftfreq(n, d=timestep) >>> freq array([ 0. , 1.25, 2.5 , ..., -3.75, -2.5 , -1.25]) n should be an integerr/rrdtypeN)r(r) ValueErrorrintr)ndvalresultsNp1p2s rr r {sJ a ' '31222 Q-CAsmmG 1q1 A 1C BGBQBK !Q$# & & &BGABBK S=rct|tstdd||zz }|dzdz}td|t}||zS)a? Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft). The returned float array `f` contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. Given a window length `n` and a sample spacing `d`:: f = [0, 1, ..., n/2-1, n/2] / (d*n) if n is even f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n) if n is odd Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`) the Nyquist frequency component is considered to be positive. Parameters ---------- n : int Window length. d : scalar, optional Sample spacing (inverse of the sampling rate). Defaults to 1. Returns ------- f : ndarray Array of length ``n//2 + 1`` containing the sample frequencies. Examples -------- >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float) >>> fourier = np.fft.rfft(signal) >>> n = signal.size >>> sample_rate = 100 >>> freq = np.fft.fftfreq(n, d=1./sample_rate) >>> freq array([ 0., 10., 20., ..., -30., -20., -10.]) >>> freq = np.fft.rfftfreq(n, d=1./sample_rate) >>> freq array([ 0., 10., 20., 30., 40., 50.]) r1r/rr2rr3)r(r)r5rr6)r7r8r9r;r:s rr r s^X a ' '31222 qs)C 1qAQ%%%G S=rr)r/)__doc__ numpy.corerrrrrnumpy.core.overridesrr __all__r6r)rr r r r rrrrCsF=<<<<<<<<<<<<<DDDDDDDD ; : :g -kBBB5 5 5 CB5 p-kBBB+ + + CB+ \ K----` K000000r