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163 lines
4.9 KiB
163 lines
4.9 KiB
"""Utilities to manipulate JSON objects."""
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# Copyright (c) IPython Development Team.
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# Distributed under the terms of the Modified BSD License.
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import math
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import numbers
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import re
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import types
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from binascii import b2a_base64
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from datetime import datetime
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from jupyter_client._version import version_info as jupyter_client_version
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next_attr_name = "__next__"
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# -----------------------------------------------------------------------------
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# Globals and constants
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# -----------------------------------------------------------------------------
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# timestamp formats
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ISO8601 = "%Y-%m-%dT%H:%M:%S.%f"
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ISO8601_PAT = re.compile(
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r"^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})(\.\d{1,6})?Z?([\+\-]\d{2}:?\d{2})?$"
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)
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# holy crap, strptime is not threadsafe.
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# Calling it once at import seems to help.
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datetime.strptime("1", "%d")
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# -----------------------------------------------------------------------------
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# Classes and functions
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# -----------------------------------------------------------------------------
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# constants for identifying png/jpeg data
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PNG = b"\x89PNG\r\n\x1a\n"
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# front of PNG base64-encoded
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PNG64 = b"iVBORw0KG"
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JPEG = b"\xff\xd8"
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# front of JPEG base64-encoded
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JPEG64 = b"/9"
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# constants for identifying gif data
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GIF_64 = b"R0lGODdh"
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GIF89_64 = b"R0lGODlh"
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# front of PDF base64-encoded
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PDF64 = b"JVBER"
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JUPYTER_CLIENT_MAJOR_VERSION = jupyter_client_version[0]
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def encode_images(format_dict):
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"""b64-encodes images in a displaypub format dict
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Perhaps this should be handled in json_clean itself?
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Parameters
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----------
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format_dict : dict
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A dictionary of display data keyed by mime-type
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Returns
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-------
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format_dict : dict
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A copy of the same dictionary,
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but binary image data ('image/png', 'image/jpeg' or 'application/pdf')
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is base64-encoded.
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"""
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# no need for handling of ambiguous bytestrings on Python 3,
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# where bytes objects always represent binary data and thus
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# base64-encoded.
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return format_dict
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def json_clean(obj):
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"""Deprecated, this is a no-op for jupyter-client>=7.
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Clean an object to ensure it's safe to encode in JSON.
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Atomic, immutable objects are returned unmodified. Sets and tuples are
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converted to lists, lists are copied and dicts are also copied.
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Note: dicts whose keys could cause collisions upon encoding (such as a dict
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with both the number 1 and the string '1' as keys) will cause a ValueError
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to be raised.
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Parameters
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----------
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obj : any python object
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Returns
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-------
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out : object
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A version of the input which will not cause an encoding error when
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encoded as JSON. Note that this function does not *encode* its inputs,
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it simply sanitizes it so that there will be no encoding errors later.
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"""
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if int(JUPYTER_CLIENT_MAJOR_VERSION) >= 7:
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return obj
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# types that are 'atomic' and ok in json as-is.
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atomic_ok = (str, type(None))
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# containers that we need to convert into lists
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container_to_list = (tuple, set, types.GeneratorType)
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# Since bools are a subtype of Integrals, which are a subtype of Reals,
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# we have to check them in that order.
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if isinstance(obj, bool):
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return obj
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if isinstance(obj, numbers.Integral):
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# cast int to int, in case subclasses override __str__ (e.g. boost enum, #4598)
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return int(obj)
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if isinstance(obj, numbers.Real):
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# cast out-of-range floats to their reprs
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if math.isnan(obj) or math.isinf(obj):
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return repr(obj)
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return float(obj)
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if isinstance(obj, atomic_ok):
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return obj
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if isinstance(obj, bytes):
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# unanmbiguous binary data is base64-encoded
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# (this probably should have happened upstream)
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return b2a_base64(obj).decode("ascii")
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if isinstance(obj, container_to_list) or (
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hasattr(obj, "__iter__") and hasattr(obj, next_attr_name)
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):
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obj = list(obj)
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if isinstance(obj, list):
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return [json_clean(x) for x in obj]
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if isinstance(obj, dict):
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# First, validate that the dict won't lose data in conversion due to
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# key collisions after stringification. This can happen with keys like
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# True and 'true' or 1 and '1', which collide in JSON.
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nkeys = len(obj)
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nkeys_collapsed = len(set(map(str, obj)))
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if nkeys != nkeys_collapsed:
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raise ValueError(
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"dict cannot be safely converted to JSON: "
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"key collision would lead to dropped values"
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)
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# If all OK, proceed by making the new dict that will be json-safe
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out = {}
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for k, v in obj.items():
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out[str(k)] = json_clean(v)
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return out
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if isinstance(obj, datetime):
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return obj.strftime(ISO8601)
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# we don't understand it, it's probably an unserializable object
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raise ValueError("Can't clean for JSON: %r" % obj)
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