ó ʽ÷Xc@`s¸ddlmZmZmZddlZddlmZddlmZdddd gZd „Z d „Z d d dd „Z d d dd„Z d d dd„Zd d dd„ZdS(i(tdivisiontprint_functiontabsolute_importNi(t _ni_support(t _nd_imagetfourier_gaussiantfourier_uniformtfourier_ellipsoidt fourier_shiftcC`s |dkrr|jjtjtjtjgkrNtj|jd|jƒ}ntj|jdtj ƒ}|}nt|ƒtkrØ|tjtjtjtj gkr·t dƒ‚ntj|jd|ƒ}|}n'|j|jkrùt dƒ‚nd}||fS(Ntdtypesoutput type not supportedsoutput shape not correct( tNoneR ttypetnumpyt complex64t complex128tfloat32tzerostshapetfloat64t RuntimeError(toutputtinputt return_value((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyt_get_output_fourier)s    cC`s÷|dkrl|jjtjtjgkrHtj|jd|jƒ}ntj|jdtjƒ}|}nt|ƒtkrÆ|tjtjgkr¥tdƒ‚ntj|jd|ƒ}|}n'|j|jkrçtdƒ‚nd}||fS(NR soutput type not supportedsoutput shape not correct( R R R R R RRRR(RRR((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyt_get_output_fourier_complex>s   iÿÿÿÿcC`s¡tj|ƒ}t||ƒ\}}tj||jƒ}tj||jƒ}tj|dtjƒ}|jj s|j ƒ}nt j |||||dƒ|S(sj Multi-dimensional 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. None is returned in this case. Returns ------- fourier_gaussian : ndarray or None The filtered input. If `output` is given as a parameter, None is returned. Examples -------- >>> from scipy import ndimage, misc >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = misc.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 i( R tasarrayRRt _check_axistndimt_normalize_sequenceRtflagst contiguoustcopyRtfourier_filter(RtsigmatntaxisRRtsigmas((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyRQs0 cC`s¡tj|ƒ}t||ƒ\}}tj||jƒ}tj||jƒ}tj|dtjƒ}|jj s|j ƒ}nt j |||||dƒ|S(sl Multi-dimensional 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. None is returned in this case. Returns ------- fourier_uniform : ndarray or None The filtered input. If `output` is given as a parameter, None is returned. Examples -------- >>> from scipy import ndimage, misc >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = misc.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 i( R RRRRRRRRRRRR (RtsizeR"R#RRtsizes((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyRs0 cC`s¡tj|ƒ}t||ƒ\}}tj||jƒ}tj||jƒ}tj|dtjƒ}|jj s|j ƒ}nt j |||||dƒ|S(sÎ Multi-dimensional ellipsoid fourier filter. The array is multiplied with the fourier transform of a 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. None is returned in this case. Returns ------- fourier_ellipsoid : ndarray or None The filtered input. If `output` is given as a parameter, None is returned. Notes ----- This function is implemented for arrays of rank 1, 2, or 3. Examples -------- >>> from scipy import ndimage, misc >>> import numpy.fft >>> import matplotlib.pyplot as plt >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = misc.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() R i( R RRRRRRRRRRRR (RR%R"R#RRR&((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyRÈs4 cC`sžtj|ƒ}t||ƒ\}}tj||jƒ}tj||jƒ}tj|dtjƒ}|jj s|j ƒ}nt j |||||ƒ|S(sc Multi-dimensional 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. None is returned in this case. Returns ------- fourier_shift : ndarray or None The shifted input. If `output` is given as a parameter, None is returned. Examples -------- >>> from scipy import ndimage, misc >>> import matplotlib.pyplot as plt >>> import numpy.fft >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = misc.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 RRRRRRRRRRRR(RtshiftR"R#RRtshifts((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyRs/ (t __future__RRRR tRRt__all__RRR RRRR(((s4/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/fourier.pyts     <;?