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Editing: _extract.cpython-311.pyc
� d�c( � �> � d Z dZg d�ZddlmZ d� Zd d�Zd d �Zd � ZdS ) z.Functions to extract parts of sparse matrices zrestructuredtext en)�find�tril�triu� )� coo_matrixc � � t | d�� � } | � � � | j dk }| j | | j | | j | fS )aJ Return the indices and values of the nonzero elements of a matrix Parameters ---------- A : dense or sparse matrix Matrix whose nonzero elements are desired. Returns ------- (I,J,V) : tuple of arrays I,J, and V contain the row indices, column indices, and values of the nonzero matrix entries. Examples -------- >>> from scipy.sparse import csr_matrix, find >>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]]) >>> find(A) (array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7., 8., 9.])) T��copy� )r �sum_duplicates�data�row�col)�A�nz_masks �7/usr/lib/python3/dist-packages/scipy/sparse/_extract.pyr r sR � �0 �1�4� � � �A��������f��k�G� �5��>�1�5��>�1�6�'�?� :�:� r Nc � � t | d�� � } | j |z | j k }t | |� � � |� � S )a Return the lower triangular portion of a matrix in sparse format Returns the elements on or below the k-th diagonal of the matrix A. - k = 0 corresponds to the main diagonal - k > 0 is above the main diagonal - k < 0 is below the main diagonal Parameters ---------- A : dense or sparse matrix Matrix whose lower trianglar portion is desired. k : integer : optional The top-most diagonal of the lower triangle. format : string Sparse format of the result, e.g. format="csr", etc. Returns ------- L : sparse matrix Lower triangular portion of A in sparse format. See Also -------- triu : upper triangle in sparse format Examples -------- >>> from scipy.sparse import csr_matrix, tril >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]], ... dtype='int32') >>> A.toarray() array([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]]) >>> tril(A).toarray() array([[1, 0, 0, 0, 0], [4, 5, 0, 0, 0], [0, 0, 8, 0, 0]]) >>> tril(A).nnz 4 >>> tril(A, k=1).toarray() array([[1, 2, 0, 0, 0], [4, 5, 0, 0, 0], [0, 0, 8, 9, 0]]) >>> tril(A, k=-1).toarray() array([[0, 0, 0, 0, 0], [4, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) >>> tril(A, format='csc') <3x5 sparse matrix of type '<class 'numpy.int32'>' with 4 stored elements in Compressed Sparse Column format> Fr �r r r � _masked_coo�asformat�r �k�format�masks r r r + �G � �p �1�5�!�!�!�A� �5�1�9��� �D� �q�$� � � (� (�� 0� 0�0r c � � t | d�� � } | j |z | j k }t | |� � � |� � S )a Return the upper triangular portion of a matrix in sparse format Returns the elements on or above the k-th diagonal of the matrix A. - k = 0 corresponds to the main diagonal - k > 0 is above the main diagonal - k < 0 is below the main diagonal Parameters ---------- A : dense or sparse matrix Matrix whose upper trianglar portion is desired. k : integer : optional The bottom-most diagonal of the upper triangle. format : string Sparse format of the result, e.g. format="csr", etc. Returns ------- L : sparse matrix Upper triangular portion of A in sparse format. See Also -------- tril : lower triangle in sparse format Examples -------- >>> from scipy.sparse import csr_matrix, triu >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]], ... dtype='int32') >>> A.toarray() array([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]]) >>> triu(A).toarray() array([[1, 2, 0, 0, 3], [0, 5, 0, 6, 7], [0, 0, 8, 9, 0]]) >>> triu(A).nnz 8 >>> triu(A, k=1).toarray() array([[0, 2, 0, 0, 3], [0, 0, 0, 6, 7], [0, 0, 0, 9, 0]]) >>> triu(A, k=-1).toarray() array([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]]) >>> triu(A, format='csc') <3x5 sparse matrix of type '<class 'numpy.int32'>' with 8 stored elements in Compressed Sparse Column format> Fr r r s r r r h r r c � � | j | }| j | }| j | }t |||ff| j | j �� � S )N)�shape�dtype)r r r r r r )r r r r r s r r r � sE � � �%��+�C� �%��+�C� �6�$�<�D� �t�c�3�Z�(���q�w� G� G� G�Gr )r N) �__doc__� __docformat__�__all__�_coor r r r r � r r �<module>r% s� ��� � &� � "� "� "�� � � � � � � ;� ;� ;�>:1� :1� :1� :1�z:1� :1� :1� :1�zH� H� H� H� Hr
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