Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/scipy/sparse/_extract.py: 33%

1"""Functions to extract parts of sparse matrices 

2""" 

3 

4__docformat__ = "restructuredtext en" 

5 

6__all__ = ['find', 'tril', 'triu'] 

7 

8 

9from ._coo import coo_matrix 

10 

11 

12def find(A): 

13 """Return the indices and values of the nonzero elements of a matrix 

14 

15 Parameters 

16 ---------- 

17 A : dense or sparse matrix 

18 Matrix whose nonzero elements are desired. 

19 

20 Returns 

21 ------- 

22 (I,J,V) : tuple of arrays 

23 I,J, and V contain the row indices, column indices, and values 

24 of the nonzero matrix entries. 

25 

26 

27 Examples 

28 -------- 

29 >>> from scipy.sparse import csr_matrix, find 

30 >>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]]) 

31 >>> find(A) 

32 (array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7., 8., 9.])) 

33 

34 """ 

35 

36 A = coo_matrix(A, copy=True) 

37 A.sum_duplicates() 

38 # remove explicit zeros 

39 nz_mask = A.data != 0 

40 return A.row[nz_mask], A.col[nz_mask], A.data[nz_mask] 

41 

42 

43def tril(A, k=0, format=None): 

44 """Return the lower triangular portion of a matrix in sparse format 

45 

46 Returns the elements on or below the k-th diagonal of the matrix A. 

47 - k = 0 corresponds to the main diagonal 

48 - k > 0 is above the main diagonal 

49 - k < 0 is below the main diagonal 

50 

51 Parameters 

52 ---------- 

53 A : dense or sparse matrix 

54 Matrix whose lower trianglar portion is desired. 

55 k : integer : optional 

56 The top-most diagonal of the lower triangle. 

57 format : string 

58 Sparse format of the result, e.g. format="csr", etc. 

59 

60 Returns 

61 ------- 

62 L : sparse matrix 

63 Lower triangular portion of A in sparse format. 

64 

65 See Also 

66 -------- 

67 triu : upper triangle in sparse format 

68 

69 Examples 

70 -------- 

71 >>> from scipy.sparse import csr_matrix, tril 

72 >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]], 

73 ... dtype='int32') 

74 >>> A.toarray() 

75 array([[1, 2, 0, 0, 3], 

76 [4, 5, 0, 6, 7], 

77 [0, 0, 8, 9, 0]]) 

78 >>> tril(A).toarray() 

79 array([[1, 0, 0, 0, 0], 

80 [4, 5, 0, 0, 0], 

81 [0, 0, 8, 0, 0]]) 

82 >>> tril(A).nnz 

83 4 

84 >>> tril(A, k=1).toarray() 

85 array([[1, 2, 0, 0, 0], 

86 [4, 5, 0, 0, 0], 

87 [0, 0, 8, 9, 0]]) 

88 >>> tril(A, k=-1).toarray() 

89 array([[0, 0, 0, 0, 0], 

90 [4, 0, 0, 0, 0], 

91 [0, 0, 0, 0, 0]]) 

92 >>> tril(A, format='csc') 

93 <3x5 sparse matrix of type '<class 'numpy.int32'>' 

94 with 4 stored elements in Compressed Sparse Column format> 

95 

96 """ 

97 

98 # convert to COOrdinate format where things are easy 

99 A = coo_matrix(A, copy=False) 

100 mask = A.row + k >= A.col 

101 return _masked_coo(A, mask).asformat(format) 

102 

103 

104def triu(A, k=0, format=None): 

105 """Return the upper triangular portion of a matrix in sparse format 

106 

107 Returns the elements on or above the k-th diagonal of the matrix A. 

108 - k = 0 corresponds to the main diagonal 

109 - k > 0 is above the main diagonal 

110 - k < 0 is below the main diagonal 

111 

112 Parameters 

113 ---------- 

114 A : dense or sparse matrix 

115 Matrix whose upper trianglar portion is desired. 

116 k : integer : optional 

117 The bottom-most diagonal of the upper triangle. 

118 format : string 

119 Sparse format of the result, e.g. format="csr", etc. 

120 

121 Returns 

122 ------- 

123 L : sparse matrix 

124 Upper triangular portion of A in sparse format. 

125 

126 See Also 

127 -------- 

128 tril : lower triangle in sparse format 

129 

130 Examples 

131 -------- 

132 >>> from scipy.sparse import csr_matrix, triu 

133 >>> A = csr_matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], [0, 0, 8, 9, 0]], 

134 ... dtype='int32') 

135 >>> A.toarray() 

136 array([[1, 2, 0, 0, 3], 

137 [4, 5, 0, 6, 7], 

138 [0, 0, 8, 9, 0]]) 

139 >>> triu(A).toarray() 

140 array([[1, 2, 0, 0, 3], 

141 [0, 5, 0, 6, 7], 

142 [0, 0, 8, 9, 0]]) 

143 >>> triu(A).nnz 

144 8 

145 >>> triu(A, k=1).toarray() 

146 array([[0, 2, 0, 0, 3], 

147 [0, 0, 0, 6, 7], 

148 [0, 0, 0, 9, 0]]) 

149 >>> triu(A, k=-1).toarray() 

150 array([[1, 2, 0, 0, 3], 

151 [4, 5, 0, 6, 7], 

152 [0, 0, 8, 9, 0]]) 

153 >>> triu(A, format='csc') 

154 <3x5 sparse matrix of type '<class 'numpy.int32'>' 

155 with 8 stored elements in Compressed Sparse Column format> 

156 

157 """ 

158 

159 # convert to COOrdinate format where things are easy 

160 A = coo_matrix(A, copy=False) 

161 mask = A.row + k <= A.col 

162 return _masked_coo(A, mask).asformat(format) 

163 

164 

165def _masked_coo(A, mask): 

166 row = A.row[mask] 

167 col = A.col[mask] 

168 data = A.data[mask] 

169 return coo_matrix((data, (row, col)), shape=A.shape, dtype=A.dtype)