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Editing: _fourier.cpython-311.pyc
� d�c~, � �f � d dl Z d dlmZ ddlmZ ddlmZ g d�Zd� Zd� Zdd �Z dd �Z dd �Z dd �Z dS )� N)�normalize_axis_index� )� _ni_support)� _nd_image)�fourier_gaussian�fourier_uniform�fourier_ellipsoid� fourier_shiftc � � | �v|j j t j t j t j fv r!t j |j |j �� � } n�t j |j t j �� � } n�t | � � t u r[| t j t j t j t j fvrt d� � �t j |j | �� � } n | j |j k rt d� � �| S �N��dtypezoutput type not supportedzoutput shape not correct) r �type�numpy� complex64� complex128�float32�zeros�shape�float64� RuntimeError��output�inputs �8/usr/lib/python3/dist-packages/scipy/ndimage/_fourier.py�_get_output_fourierr ( s� � � �~� �;� ����1A� %� � /� /� /��[���E�K�@�@�@�F�F��[���E�M�B�B�B�F�F� �f� � �� � � �%�/�5�+;� �-�� �8� 8� 8� � :�;�;� ;���U�[��7�7�7��� � ��� $� $��5�6�6�6� �M� c �� � | �k|j j t j t j fv r!t j |j |j �� � } n�t j |j t j �� � } nzt | � � t u rE| t j t j fvrt d� � �t j |j | �� � } n | j |j k rt d� � �| S r )r r r r r r r r r s r �_get_output_fourier_complexr 9 s� � � �~� �;� ����1A� B� B� B��[���E�K�@�@�@�F�F��[���E�4D�E�E�E�F�F� �f� � �� � � �%�/�5�+;�<� <� <� � :�;�;� ;���U�[��7�7�7��� � ��� $� $��5�6�6�6� �Mr ���c �^ � t j | � � } t || � � }t || j � � }t j || j � � }t j |t j �� � }|j j s|� � � }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. 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 � r �asarrayr r �ndimr �_normalize_sequencer �flags� contiguous�copyr �fourier_filter)r �sigma�n�axisr �sigmass r r r H s� � �\ �M�%� � �E� ��� /� /�F� ��e�j� 1� 1�D� � ,�U�E�J� ?� ?�F� �]�6�� � 7� 7� 7�F� �<� "� ���� � �� � �U�F�A�t�V�Q�?�?�?� �Mr c �^ � t j | � � } t || � � }t || j � � }t j || j � � }t j |t j �� � }|j j s|� � � }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. 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 r r" �r �sizer+ r, r �sizess r r r � s� � �\ �M�%� � �E� ��� /� /�F� ��e�j� 1� 1�D� � +�D�%�*� =� =�E� �M�%�u�}� 5� 5� 5�E� �;� !� �� � � � �� � �U�E�1�d�F�A�>�>�>� �Mr c � � t j | � � } | j dk rt d� � �t || � � }|j dk r|S t || j � � }t j || j � � }t j |t j �� � }|j j s|� � � }t j | ||||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 supportedr r � )r r# r$ �NotImplementedErrorr r0 r r r% r r& r' r( r r) r/ s r r r � s� � �d �M�%� � �E� �z�A�~�~�!�"K�L�L�L� ��� /� /�F� �{�a��� � � ��e�j� 1� 1�D� � +�D�%�*� =� =�E� �M�%�u�}� 5� 5� 5�E� �;� !� �� � � � �� � �U�E�1�d�F�A�>�>�>� �Mr c �\ � t j | � � } t || � � }t || j � � }t j || j � � }t j |t j �� � }|j j s|� � � }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. 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 r# r r r$ r r% r r& r' r( r r )r �shiftr+ r, r �shiftss r r r � s� � �Z �M�%� � �E� (��� 7� 7�F� ��e�j� 1� 1�D� � ,�U�E�J� ?� ?�F� �]�6�� � 7� 7� 7�F� �<� "� ���� � �� ��E�6�1�d�F�;�;�;� �Mr )r r N) r �numpy.core.multiarrayr � r r �__all__r r r r r r � r r �<module>r= s� ��> � � � � 6� 6� 6� 6� 6� 6� � � � � � � � � � � � � � � ��� � �" � � � 7� 7� 7� 7�t6� 6� 6� 6�r@� @� @� @�F5� 5� 5� 5� 5� 5r
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