Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.11/site-packages/ipykernel/jsonutil.py: 95%

1"""Utilities to manipulate JSON objects.""" 

2 

3# Copyright (c) IPython Development Team. 

4# Distributed under the terms of the Modified BSD License. 

5 

6import math 

7import numbers 

8import re 

9import types 

10from binascii import b2a_base64 

11from datetime import date, datetime 

12 

13from jupyter_client._version import version_info as jupyter_client_version 

14 

15next_attr_name = "__next__" 

16 

17# ----------------------------------------------------------------------------- 

18# Globals and constants 

19# ----------------------------------------------------------------------------- 

20 

21# timestamp formats 

22ISO8601 = "%Y-%m-%dT%H:%M:%S.%f" 

23ISO8601_PAT = re.compile( 

24 r"^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})(\.\d{1,6})?Z?([\+\-]\d{2}:?\d{2})?$" 

25) 

26 

27# holy crap, strptime is not threadsafe. 

28# Calling it once at import seems to help. 

29datetime.strptime("2000-01-01", "%Y-%m-%d") 

30 

31# ----------------------------------------------------------------------------- 

32# Classes and functions 

33# ----------------------------------------------------------------------------- 

34 

35 

36# constants for identifying png/jpeg data 

37PNG = b"\x89PNG\r\n\x1a\n" 

38# front of PNG base64-encoded 

39PNG64 = b"iVBORw0KG" 

40JPEG = b"\xff\xd8" 

41# front of JPEG base64-encoded 

42JPEG64 = b"/9" 

43# constants for identifying gif data 

44GIF_64 = b"R0lGODdh" 

45GIF89_64 = b"R0lGODlh" 

46# front of PDF base64-encoded 

47PDF64 = b"JVBER" 

48 

49JUPYTER_CLIENT_MAJOR_VERSION = jupyter_client_version[0] 

50 

51 

52def encode_images(format_dict): 

53 """b64-encodes images in a displaypub format dict 

54 

55 Perhaps this should be handled in json_clean itself? 

56 

57 Parameters 

58 ---------- 

59 format_dict : dict 

60 A dictionary of display data keyed by mime-type 

61 

62 Returns 

63 ------- 

64 format_dict : dict 

65 A copy of the same dictionary, 

66 but binary image data ('image/png', 'image/jpeg' or 'application/pdf') 

67 is base64-encoded. 

68 

69 """ 

70 

71 # no need for handling of ambiguous bytestrings on Python 3, 

72 # where bytes objects always represent binary data and thus 

73 # base64-encoded. 

74 return format_dict 

75 

76 

77def json_clean(obj): # pragma: no cover 

78 """Deprecated, this is a no-op for jupyter-client>=7. 

79 

80 Clean an object to ensure it's safe to encode in JSON. 

81 

82 Atomic, immutable objects are returned unmodified. Sets and tuples are 

83 converted to lists, lists are copied and dicts are also copied. 

84 

85 Note: dicts whose keys could cause collisions upon encoding (such as a dict 

86 with both the number 1 and the string '1' as keys) will cause a ValueError 

87 to be raised. 

88 

89 Parameters 

90 ---------- 

91 obj : any python object 

92 

93 Returns 

94 ------- 

95 out : object 

96 A version of the input which will not cause an encoding error when 

97 encoded as JSON. Note that this function does not *encode* its inputs, 

98 it simply sanitizes it so that there will be no encoding errors later. 

99 

100 """ 

101 if int(JUPYTER_CLIENT_MAJOR_VERSION) >= 7: 

102 return obj 

103 

104 # types that are 'atomic' and ok in json as-is. 

105 atomic_ok = (str, type(None)) 

106 

107 # containers that we need to convert into lists 

108 container_to_list = (tuple, set, types.GeneratorType) 

109 

110 # Since bools are a subtype of Integrals, which are a subtype of Reals, 

111 # we have to check them in that order. 

112 

113 if isinstance(obj, bool): 

114 return obj 

115 

116 if isinstance(obj, numbers.Integral): 

117 # cast int to int, in case subclasses override __str__ (e.g. boost enum, #4598) 

118 return int(obj) 

119 

120 if isinstance(obj, numbers.Real): 

121 # cast out-of-range floats to their reprs 

122 if math.isnan(obj) or math.isinf(obj): 

123 return repr(obj) 

124 return float(obj) 

125 

126 if isinstance(obj, atomic_ok): 

127 return obj 

128 

129 if isinstance(obj, bytes): 

130 # unanmbiguous binary data is base64-encoded 

131 # (this probably should have happened upstream) 

132 return b2a_base64(obj).decode("ascii") 

133 

134 if isinstance(obj, container_to_list) or ( 

135 hasattr(obj, "__iter__") and hasattr(obj, next_attr_name) 

136 ): 

137 obj = list(obj) 

138 

139 if isinstance(obj, list): 

140 return [json_clean(x) for x in obj] 

141 

142 if isinstance(obj, dict): 

143 # First, validate that the dict won't lose data in conversion due to 

144 # key collisions after stringification. This can happen with keys like 

145 # True and 'true' or 1 and '1', which collide in JSON. 

146 nkeys = len(obj) 

147 nkeys_collapsed = len(set(map(str, obj))) 

148 if nkeys != nkeys_collapsed: 

149 msg = ( 

150 "dict cannot be safely converted to JSON: " 

151 "key collision would lead to dropped values" 

152 ) 

153 raise ValueError(msg) 

154 # If all OK, proceed by making the new dict that will be json-safe 

155 out = {} 

156 for k, v in obj.items(): 

157 out[str(k)] = json_clean(v) 

158 return out 

159 if isinstance(obj, (datetime, date)): 

160 return obj.strftime(ISO8601) 

161 

162 # we don't understand it, it's probably an unserializable object 

163 raise ValueError("Can't clean for JSON: %r" % obj)