+ LV-j,n^RIt^RIHt^RIHt^RIHt.R OtRtRt R Rlt R Rlt R R lt R R lt R#) N)normalize_axis_index) _ni_support) _nd_imagecVfVPP\P\P\P 39d/\P !VPVPR7pV#\P !VP\PR7pV#\V4\JdsV\P\P\P \P39d \R4h\P !VPVR7pV#VPVP8wd \R4hV#Ndtypezoutput type not supportedzoutput shape not correct) r typenp complex64 complex128float32zerosshapefloat64 RuntimeErroroutputinputs&&g/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/scipy/ndimage/_fourier.py_get_output_fourierr(s ~ ;;   bmmRZZH HXXekk=F MXXekk>> from scipy import ndimage, datasets >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = datasets.ascent() >>> input_ = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_gaussian(input_, sigma=4) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show() r r asarrayrrndimr_normalize_sequencerflags contiguouscopyrfourier_filter)rsigmanaxisrsigmass&&&&& rfourier_gaussianr(Gs\ JJu E  /F jj 1D  , ,UJJ ?F ZZbjj 1F << " " " UAVQ? Mrc\P!V4p\W@4p\W0P4p\ P !WP4p\P!V\PR7pVPP'gVP4p\P!WW#V^4V#)ae Multidimensional uniform fourier filter. The array is multiplied with the Fourier transform of a box of given size. Parameters ---------- input : array_like The input array. size : float or sequence The size of the box used for filtering. If a float, `size` is the same for all axes. If a sequence, `size` has to contain one value for each axis. n : int, optional If `n` is negative (default), then the input is assumed to be the result of a complex fft. If `n` is larger than or equal to zero, the input is assumed to be the result of a real fft, and `n` gives the length of the array before transformation along the real transform direction. axis : int, optional The axis of the real transform. output : ndarray, optional If given, the result of filtering the input is placed in this array. Returns ------- fourier_uniform : ndarray The filtered input. Examples -------- >>> from scipy import ndimage, datasets >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = datasets.ascent() >>> input_ = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_uniform(input_, size=20) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show() rrrsizer%r&rsizess&&&&& rfourier_uniformr-s\ JJu E  /F jj 1D  + +D** =E JJuBJJ /E ;; ! ! !  U1FA> Mrc\P!V4pVP^8d \R4h\ W@4pVP ^8XdV#\ W0P4p\P!WP4p\P!V\PR7pVPP'gVP4p\P!WW#V^4V#)a Multidimensional ellipsoid Fourier filter. The array is multiplied with the fourier transform of an ellipsoid of given sizes. Parameters ---------- input : array_like The input array. size : float or sequence The size of the box used for filtering. If a float, `size` is the same for all axes. If a sequence, `size` has to contain one value for each axis. n : int, optional If `n` is negative (default), then the input is assumed to be the result of a complex fft. If `n` is larger than or equal to zero, the input is assumed to be the result of a real fft, and `n` gives the length of the array before transformation along the real transform direction. axis : int, optional The axis of the real transform. output : ndarray, optional If given, the result of filtering the input is placed in this array. Returns ------- fourier_ellipsoid : ndarray The filtered input. Notes ----- This function is implemented for arrays of rank 1, 2, or 3. Examples -------- >>> from scipy import ndimage, datasets >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = datasets.ascent() >>> input_ = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_ellipsoid(input_, size=20) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show() z'Only 1d, 2d and 3d inputs are supportedr)r rrNotImplementedErrorrr+rrrrr r!r"rr#r*s&&&&& rfourier_ellipsoidr0sd JJu E zzA~!"KLL  /F {{a jj 1D  + +D** =E JJuBJJ /E ;; ! ! !  U1FA> Mrc\P!V4p\W@4p\W0P4p\ P !WP4p\P!V\PR7pVPP'gVP4p\P!WW#V4V#)a` Multidimensional Fourier shift filter. The array is multiplied with the Fourier transform of a shift operation. Parameters ---------- input : array_like The input array. shift : float or sequence The size of the box used for filtering. If a float, `shift` is the same for all axes. If a sequence, `shift` has to contain one value for each axis. n : int, optional If `n` is negative (default), then the input is assumed to be the result of a complex fft. If `n` is larger than or equal to zero, the input is assumed to be the result of a real fft, and `n` gives the length of the array before transformation along the real transform direction. axis : int, optional The axis of the real transform. output : ndarray, optional If given, the result of shifting the input is placed in this array. Returns ------- fourier_shift : ndarray The shifted input. Examples -------- >>> from scipy import ndimage, datasets >>> import matplotlib.pyplot as plt >>> import numpy.fft >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = datasets.ascent() >>> input_ = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_shift(input_, shift=200) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show() r) r rrrrrrrr r!r"r fourier_shift)rshiftr%r&rshiftss&&&&& rr2r2sZ JJu E ( 7F jj 1D  , ,UJJ ?F ZZbjj 1F << " " " E1F; Mr)r(r-r0r2)r5N)numpyr scipy._lib._utilrrr__all__rrr(r-r0r2rrr;s<>1   7t6r@F5r