3 h+@stddlZddlmZddlmZddlmZdddd gZd d Zd d ZdddZ dddZ dddZ ddd Z dS)N)normalize_axis_index) _ni_support) _nd_imagefourier_gaussianfourier_uniformfourier_ellipsoid fourier_shiftcCs|dkrH|jjtjtjtjgkr4tj|j|jd}qtj|jtjd}nRt|tkr|tjtjtjtjgkrtt dtj|j|d}n|j|jkrt d|S)N)dtypezoutput type not supportedzoutput shape not correct) r typenumpy complex64 complex128Zfloat32zerosshapefloat64 RuntimeError)outputinputr6/tmp/pip-build-riy7u7_k/scipy/scipy/ndimage/fourier.py_get_output_fourier(s    rcCs|dkrD|jjtjtjgkr0tj|j|jd}qtj|jtjd}nJt|tkrz|tjtjgkrhtdtj|j|d}n|j|jkrtd|S)N)r zoutput type not supportedzoutput shape not correct)r r r r rrrr)rrrrr_get_output_fourier_complex9s  rcCsftj|}t||}t||j}tj||j}tj|tjd}|jj sN|j }t j |||||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. None is returned in this case. Returns ------- fourier_gaussian : ndarray The filtered input. 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 r) r asarrayrrndimr_normalize_sequencerflags contiguouscopyrfourier_filter)rsigmanaxisrZsigmasrrrrHs/   cCsftj|}t||}t||j}tj||j}tj|tjd}|jj sN|j }t j |||||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. None is returned in this case. Returns ------- fourier_uniform : ndarray The filtered input. 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 r) r rrrrrrrrrrrr)rsizer!r"rsizesrrrrs/   cCsftj|}t||}t||j}tj||j}tj|tjd}|jj sN|j }t j |||||d|S)a Multidimensional 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 The filtered input. 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 ) r rrrrrrrrrrrr)rr#r!r"rr$rrrrs3   cCsdtj|}t||}t||j}tj||j}tj|tjd}|jj sN|j }t j ||||||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. None is returned in this case. Returns ------- fourier_shift : ndarray The shifted input. 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 )rshiftr!r"rZshiftsrrrr s.   r')r'r'Nr'r')r'r'Nr'r')r'r'Nr'r')r'r'N) r Znumpy.core.multiarrayrrr__all__rrrrrr rrrrs    ; : >