^Mh,fddlZddlmZddlmZddlmZgdZdZdZ dd Z dd Z dd Z dd Z dS)N)normalize_axis_index) _ni_support) _nd_image)fourier_gaussianfourier_uniformfourier_ellipsoid fourier_shiftc|v|jjtjtjtjfvr!tj|j|j}ntj|jtj}nt|tur[|tjtjtjtjfvrtdtj|j|}n|j|jkrtd|SNdtypezoutput type not supportedzoutput shape not correct) rtypenp complex64 complex128float32zerosshapefloat64 RuntimeErroroutputinputs V/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/scipy/ndimage/_fourier.py_get_output_fourierr(s ~ ;  bmRZH H HXek===FFXek<<>> 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 rasarrayrrndimr_normalize_sequencerflags contiguouscopyrfourier_filter)rsigmanaxisrsigmass rrrGs\ Ju  E  / /F ej 1 1D  ,UEJ ? ?F Zbj 1 1 1F < " UFAtVQ??? Mrc^tj|}t||}t||j}t j||j}tj|tj}|jj s| }tj |||||d|S)a 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() r rr"rsizer+r,rsizess rrrs\ Ju  E  / /F ej 1 1D  +D%* = =E JuBJ / / /E ; !  UE1dFA>>> Mrctj|}|jdkrtdt ||}|jdkr|St ||j}tj||j}tj|tj }|j j s| }tj|||||d|S)ah 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 supportedrr )rr#r$NotImplementedErrorrr0rrr%rr&r'r(rr)r/s rr r sd Ju  E zA~~!"KLLL  / /F {a ej 1 1D  +D%* = =E JuBJ / / /E ; !  UE1dFA>>> Mrc\tj|}t||}t||j}t j||j}tj|tj}|jj s| }tj ||||||S)a Multidimensional Fourier shift filter. The array is multiplied with the Fourier transform of a shift operation. Parameters ---------- input : array_like The input array. shift : float or sequence The size of the box used for filtering. If a float, `shift` is the same for all axes. If a sequence, `shift` has to contain one value for each axis. n : int, optional If `n` is negative (default), then the input is assumed to be the result of a complex fft. If `n` is larger than or equal to zero, the input is assumed to be the result of a real fft, and `n` gives the length of the array before transformation along the real transform direction. axis : int, optional The axis of the real transform. output : ndarray, optional If given, the result of shifting the input is placed in this array. Returns ------- fourier_shift : ndarray The shifted input. Examples -------- >>> from scipy import ndimage, datasets >>> import matplotlib.pyplot as plt >>> import numpy.fft >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = datasets.ascent() >>> input_ = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_shift(input_, shift=200) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show() r ) rr#rrr$rr%rr&r'r(rr )rshiftr+r,rshiftss rr r sZ Ju  E ( 7 7F ej 1 1D  ,UEJ ? ?F Zbj 1 1 1F < " E61dF;;; Mr)r r N)numpyrscipy._lib._utilrrr__all__rrrrr r rrr>s>111111         7777t6666r@@@@F555555r