Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/scipy/sparse/_matrix_io.py: 14%

1import numpy as np 

2import scipy as sp 

3 

4__all__ = ['save_npz', 'load_npz'] 

5 

6 

7# Make loading safe vs. malicious input 

8PICKLE_KWARGS = dict(allow_pickle=False) 

9 

10 

11def save_npz(file, matrix, compressed=True): 

12 """ Save a sparse matrix or array to a file using ``.npz`` format. 

13 

14 Parameters 

15 ---------- 

16 file : str or file-like object 

17 Either the file name (string) or an open file (file-like object) 

18 where the data will be saved. If file is a string, the ``.npz`` 

19 extension will be appended to the file name if it is not already 

20 there. 

21 matrix: spmatrix or sparray 

22 The sparse matrix or array to save. 

23 Supported formats: ``csc``, ``csr``, ``bsr``, ``dia`` or ``coo``. 

24 compressed : bool, optional 

25 Allow compressing the file. Default: True 

26 

27 See Also 

28 -------- 

29 scipy.sparse.load_npz: Load a sparse matrix from a file using ``.npz`` format. 

30 numpy.savez: Save several arrays into a ``.npz`` archive. 

31 numpy.savez_compressed : Save several arrays into a compressed ``.npz`` archive. 

32 

33 Examples 

34 -------- 

35 Store sparse matrix to disk, and load it again: 

36 

37 >>> import numpy as np 

38 >>> import scipy as sp 

39 >>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]]) 

40 >>> sparse_matrix 

41 <2x3 sparse matrix of type '<class 'numpy.int64'>' 

42 with 2 stored elements in Compressed Sparse Column format> 

43 >>> sparse_matrix.toarray() 

44 array([[0, 0, 3], 

45 [4, 0, 0]], dtype=int64) 

46 

47 >>> sp.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix) 

48 >>> sparse_matrix = sp.sparse.load_npz('/tmp/sparse_matrix.npz') 

49 

50 >>> sparse_matrix 

51 <2x3 sparse matrix of type '<class 'numpy.int64'>' 

52 with 2 stored elements in Compressed Sparse Column format> 

53 >>> sparse_matrix.toarray() 

54 array([[0, 0, 3], 

55 [4, 0, 0]], dtype=int64) 

56 """ 

57 arrays_dict = {} 

58 if matrix.format in ('csc', 'csr', 'bsr'): 

59 arrays_dict.update(indices=matrix.indices, indptr=matrix.indptr) 

60 elif matrix.format == 'dia': 

61 arrays_dict.update(offsets=matrix.offsets) 

62 elif matrix.format == 'coo': 

63 arrays_dict.update(row=matrix.row, col=matrix.col) 

64 else: 

65 msg = f'Save is not implemented for sparse matrix of format {matrix.format}.' 

66 raise NotImplementedError(msg) 

67 arrays_dict.update( 

68 format=matrix.format.encode('ascii'), 

69 shape=matrix.shape, 

70 data=matrix.data 

71 ) 

72 if isinstance(matrix, sp.sparse.sparray): 

73 arrays_dict.update(_is_array=True) 

74 if compressed: 

75 np.savez_compressed(file, **arrays_dict) 

76 else: 

77 np.savez(file, **arrays_dict) 

78 

79 

80def load_npz(file): 

81 """ Load a sparse array/matrix from a file using ``.npz`` format. 

82 

83 Parameters 

84 ---------- 

85 file : str or file-like object 

86 Either the file name (string) or an open file (file-like object) 

87 where the data will be loaded. 

88 

89 Returns 

90 ------- 

91 result : csc_array, csr_array, bsr_array, dia_array or coo_array 

92 A sparse array/matrix containing the loaded data. 

93 

94 Raises 

95 ------ 

96 OSError 

97 If the input file does not exist or cannot be read. 

98 

99 See Also 

100 -------- 

101 scipy.sparse.save_npz: Save a sparse array/matrix to a file using ``.npz`` format. 

102 numpy.load: Load several arrays from a ``.npz`` archive. 

103 

104 Examples 

105 -------- 

106 Store sparse array/matrix to disk, and load it again: 

107 

108 >>> import numpy as np 

109 >>> import scipy as sp 

110 >>> sparse_array = sp.sparse.csc_array([[0, 0, 3], [4, 0, 0]]) 

111 >>> sparse_array 

112 <2x3 sparse array of type '<class 'numpy.int64'>' 

113 with 2 stored elements in Compressed Sparse Column format> 

114 >>> sparse_array.toarray() 

115 array([[0, 0, 3], 

116 [4, 0, 0]], dtype=int64) 

117 

118 >>> sp.sparse.save_npz('/tmp/sparse_array.npz', sparse_array) 

119 >>> sparse_array = sp.sparse.load_npz('/tmp/sparse_array.npz') 

120 

121 >>> sparse_array 

122 <2x3 sparse array of type '<class 'numpy.int64'>' 

123 with 2 stored elements in Compressed Sparse Column format> 

124 >>> sparse_array.toarray() 

125 array([[0, 0, 3], 

126 [4, 0, 0]], dtype=int64) 

127 

128 In this example we force the result to be csr_array from csr_matrix 

129 >>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]]) 

130 >>> sp.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix) 

131 >>> tmp = sp.sparse.load_npz('/tmp/sparse_matrix.npz') 

132 >>> sparse_array = sp.sparse.csr_array(tmp) 

133 """ 

134 with np.load(file, **PICKLE_KWARGS) as loaded: 

135 sparse_format = loaded.get('format') 

136 if sparse_format is None: 

137 raise ValueError(f'The file {file} does not contain ' 

138 f'a sparse array or matrix.') 

139 sparse_format = sparse_format.item() 

140 

141 if not isinstance(sparse_format, str): 

142 # Play safe with Python 2 vs 3 backward compatibility; 

143 # files saved with SciPy < 1.0.0 may contain unicode or bytes. 

144 sparse_format = sparse_format.decode('ascii') 

145 

146 if loaded.get('_is_array'): 

147 sparse_type = sparse_format + '_array' 

148 else: 

149 sparse_type = sparse_format + '_matrix' 

150 

151 try: 

152 cls = getattr(sp.sparse, f'{sparse_type}') 

153 except AttributeError as e: 

154 raise ValueError(f'Unknown format "{sparse_type}"') from e 

155 

156 if sparse_format in ('csc', 'csr', 'bsr'): 

157 return cls((loaded['data'], loaded['indices'], loaded['indptr']), 

158 shape=loaded['shape']) 

159 elif sparse_format == 'dia': 

160 return cls((loaded['data'], loaded['offsets']), 

161 shape=loaded['shape']) 

162 elif sparse_format == 'coo': 

163 return cls((loaded['data'], (loaded['row'], loaded['col'])), 

164 shape=loaded['shape']) 

165 else: 

166 raise NotImplementedError(f'Load is not implemented for ' 

167 f'sparse matrix of format {sparse_format}.')