a BCCfy,@spddlZddlmZddlmZddlmZgdZddZd d Zdd d Z dddZ dddZ dddZ dS)N)normalize_axis_index) _ni_support) _nd_image)fourier_gaussianfourier_uniformfourier_ellipsoid fourier_shiftcCs|durH|jjtjtjtjfvr4tj|j|jd}qtj|jtjd}nRt|tur|tjtjtjtjfvrtt dtj|j|d}n|j|jkrt d|SNdtypezoutput type not supportedzoutput shape not correct) r typenumpy complex64 complex128Zfloat32zerosshapefloat64 RuntimeErroroutputinputrR/var/www/html/django/DPS/env/lib/python3.9/site-packages/scipy/ndimage/_fourier.py_get_output_fourier(s   rcCs|durD|jjtjtjfvr0tj|j|jd}qtj|jtjd}nJt|turz|tjtjfvrhtdtj|j|d}n|j|jkrtd|Sr )r r rrrrrrrrrr_get_output_fourier_complex9s  rcCsft|}t||}t||j}t||j}tj|tjd}|jj sN| }t |||||d|S)a Multidimensional Gaussian fourier filter. The array is multiplied with the fourier transform of a Gaussian kernel. Parameters ---------- input : array_like The input array. sigma : float or sequence The sigma of the Gaussian kernel. If a float, `sigma` is the same for all axes. If a sequence, `sigma` 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_gaussian : 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_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)rsigmanaxisrZsigmasrrrrHs.   rcCsft|}t||}t||j}t||j}tj|tjd}|jj sN| }t |||||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 rrrsizer&r'rsizesrrrrs.   rcCst|}|jdkrtdt||}|jdkr4|St||j}t||j}tj|tj d}|j j sn| }t |||||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 )rrrNotImplementedErrorrr)rrr rr!r"r#rr$r(rrrrs2     rcCsdt|}t||}t||j}t||j}tj|tjd}|jj sN| }t ||||||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 ) rrrrrrr rr!r"r#rr )rshiftr&r'rZshiftsrrrr s-   r )rrN)rrN)rrN)rrN) rZscipy._lib._utilrrr__all__rrrrrr rrrrs    : 9 C