##// END OF EJS Templates
use math to check for nan/inf
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@@ -1,212 +1,207 b''
1 1 """Utilities to manipulate JSON objects.
2 2 """
3 3 #-----------------------------------------------------------------------------
4 4 # Copyright (C) 2010-2011 The IPython Development Team
5 5 #
6 6 # Distributed under the terms of the BSD License. The full license is in
7 7 # the file COPYING.txt, distributed as part of this software.
8 8 #-----------------------------------------------------------------------------
9 9
10 10 #-----------------------------------------------------------------------------
11 11 # Imports
12 12 #-----------------------------------------------------------------------------
13 13 # stdlib
14 import math
14 15 import re
15 16 import sys
16 17 import types
17 18 from base64 import encodestring
18 19 from datetime import datetime
19 20
20 21 from IPython.utils import py3compat
21 22 from IPython.utils.encoding import DEFAULT_ENCODING
22 23 from IPython.utils import text
23 24 next_attr_name = '__next__' if py3compat.PY3 else 'next'
24 25
25 26 #-----------------------------------------------------------------------------
26 27 # Globals and constants
27 28 #-----------------------------------------------------------------------------
28 29
29 30 # timestamp formats
30 31 ISO8601="%Y-%m-%dT%H:%M:%S.%f"
31 32 ISO8601_PAT=re.compile(r"^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d+$")
32 33
33 # float constants
34 NAN = float('nan')
35 INF = float('inf')
36 NINF = float('-inf')
37 INFS = (INF, NINF)
38
39 34 #-----------------------------------------------------------------------------
40 35 # Classes and functions
41 36 #-----------------------------------------------------------------------------
42 37
43 38 def rekey(dikt):
44 39 """Rekey a dict that has been forced to use str keys where there should be
45 40 ints by json."""
46 41 for k in dikt.iterkeys():
47 42 if isinstance(k, basestring):
48 43 ik=fk=None
49 44 try:
50 45 ik = int(k)
51 46 except ValueError:
52 47 try:
53 48 fk = float(k)
54 49 except ValueError:
55 50 continue
56 51 if ik is not None:
57 52 nk = ik
58 53 else:
59 54 nk = fk
60 55 if nk in dikt:
61 56 raise KeyError("already have key %r"%nk)
62 57 dikt[nk] = dikt.pop(k)
63 58 return dikt
64 59
65 60
66 61 def extract_dates(obj):
67 62 """extract ISO8601 dates from unpacked JSON"""
68 63 if isinstance(obj, dict):
69 64 obj = dict(obj) # don't clobber
70 65 for k,v in obj.iteritems():
71 66 obj[k] = extract_dates(v)
72 67 elif isinstance(obj, (list, tuple)):
73 68 obj = [ extract_dates(o) for o in obj ]
74 69 elif isinstance(obj, basestring):
75 70 if ISO8601_PAT.match(obj):
76 71 obj = datetime.strptime(obj, ISO8601)
77 72 return obj
78 73
79 74 def squash_dates(obj):
80 75 """squash datetime objects into ISO8601 strings"""
81 76 if isinstance(obj, dict):
82 77 obj = dict(obj) # don't clobber
83 78 for k,v in obj.iteritems():
84 79 obj[k] = squash_dates(v)
85 80 elif isinstance(obj, (list, tuple)):
86 81 obj = [ squash_dates(o) for o in obj ]
87 82 elif isinstance(obj, datetime):
88 83 obj = obj.strftime(ISO8601)
89 84 return obj
90 85
91 86 def date_default(obj):
92 87 """default function for packing datetime objects in JSON."""
93 88 if isinstance(obj, datetime):
94 89 return obj.strftime(ISO8601)
95 90 else:
96 91 raise TypeError("%r is not JSON serializable"%obj)
97 92
98 93
99 94 # constants for identifying png/jpeg data
100 95 PNG = b'\x89PNG\r\n\x1a\n'
101 96 JPEG = b'\xff\xd8'
102 97
103 98 def encode_images(format_dict):
104 99 """b64-encodes images in a displaypub format dict
105 100
106 101 Perhaps this should be handled in json_clean itself?
107 102
108 103 Parameters
109 104 ----------
110 105
111 106 format_dict : dict
112 107 A dictionary of display data keyed by mime-type
113 108
114 109 Returns
115 110 -------
116 111
117 112 format_dict : dict
118 113 A copy of the same dictionary,
119 114 but binary image data ('image/png' or 'image/jpeg')
120 115 is base64-encoded.
121 116
122 117 """
123 118 encoded = format_dict.copy()
124 119 pngdata = format_dict.get('image/png')
125 120 if isinstance(pngdata, bytes) and pngdata[:8] == PNG:
126 121 encoded['image/png'] = encodestring(pngdata).decode('ascii')
127 122 jpegdata = format_dict.get('image/jpeg')
128 123 if isinstance(jpegdata, bytes) and jpegdata[:2] == JPEG:
129 124 encoded['image/jpeg'] = encodestring(jpegdata).decode('ascii')
130 125 return encoded
131 126
132 127
133 128 def json_clean(obj):
134 129 """Clean an object to ensure it's safe to encode in JSON.
135 130
136 131 Atomic, immutable objects are returned unmodified. Sets and tuples are
137 132 converted to lists, lists are copied and dicts are also copied.
138 133
139 134 Note: dicts whose keys could cause collisions upon encoding (such as a dict
140 135 with both the number 1 and the string '1' as keys) will cause a ValueError
141 136 to be raised.
142 137
143 138 Parameters
144 139 ----------
145 140 obj : any python object
146 141
147 142 Returns
148 143 -------
149 144 out : object
150 145
151 146 A version of the input which will not cause an encoding error when
152 147 encoded as JSON. Note that this function does not *encode* its inputs,
153 148 it simply sanitizes it so that there will be no encoding errors later.
154 149
155 150 Examples
156 151 --------
157 152 >>> json_clean(4)
158 153 4
159 154 >>> json_clean(range(10))
160 155 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
161 156 >>> sorted(json_clean(dict(x=1, y=2)).items())
162 157 [('x', 1), ('y', 2)]
163 158 >>> sorted(json_clean(dict(x=1, y=2, z=[1,2,3])).items())
164 159 [('x', 1), ('y', 2), ('z', [1, 2, 3])]
165 160 >>> json_clean(True)
166 161 True
167 162 """
168 163 # types that are 'atomic' and ok in json as-is. bool doesn't need to be
169 164 # listed explicitly because bools pass as int instances
170 165 atomic_ok = (unicode, int, types.NoneType)
171 166
172 167 # containers that we need to convert into lists
173 168 container_to_list = (tuple, set, types.GeneratorType)
174 169
175 170 if isinstance(obj, float):
176 171 # cast out-of-range floats to their reprs
177 if obj != obj or obj in INFS:
172 if math.isnan(obj) or math.isinf(obj):
178 173 return repr(obj)
179 174 return obj
180 175
181 176 if isinstance(obj, atomic_ok):
182 177 return obj
183 178
184 179 if isinstance(obj, bytes):
185 180 return obj.decode(DEFAULT_ENCODING, 'replace')
186 181
187 182 if isinstance(obj, container_to_list) or (
188 183 hasattr(obj, '__iter__') and hasattr(obj, next_attr_name)):
189 184 obj = list(obj)
190 185
191 186 if isinstance(obj, list):
192 187 return [json_clean(x) for x in obj]
193 188
194 189 if isinstance(obj, dict):
195 190 # First, validate that the dict won't lose data in conversion due to
196 191 # key collisions after stringification. This can happen with keys like
197 192 # True and 'true' or 1 and '1', which collide in JSON.
198 193 nkeys = len(obj)
199 194 nkeys_collapsed = len(set(map(str, obj)))
200 195 if nkeys != nkeys_collapsed:
201 196 raise ValueError('dict can not be safely converted to JSON: '
202 197 'key collision would lead to dropped values')
203 198 # If all OK, proceed by making the new dict that will be json-safe
204 199 out = {}
205 200 for k,v in obj.iteritems():
206 201 out[str(k)] = json_clean(v)
207 202 return out
208 203
209 204 # If we get here, we don't know how to handle the object, so we just get
210 205 # its repr and return that. This will catch lambdas, open sockets, class
211 206 # objects, and any other complicated contraption that json can't encode
212 207 return repr(obj)
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