kwcoco.util.util_json module

kwcoco.util.util_json.ensure_json_serializable(dict_, normalize_containers=False, verbose=0)

Attempt to convert common types (e.g. numpy) into something json complient

Convert numpy and tuples into lists

Parameters:normalize_containers (bool, default=False) – if True, normalizes dict containers to be standard python structures.

Example

>>> data = ub.ddict(lambda: int)
>>> data['foo'] = ub.ddict(lambda: int)
>>> data['bar'] = np.array([1, 2, 3])
>>> data['foo']['a'] = 1
>>> result = ensure_json_serializable(data, normalize_containers=True)
>>> assert type(result) is dict

kwcoco.util.util_json.find_json_unserializable(data, quickcheck=False)

Recurse through json datastructure and find any component that causes a serialization error. Record the location of these errors in the datastructure as we recurse through the call tree.

Parameters:

• data (object) – data that should be json serializable
• quickcheck (bool) – if True, check the entire datastructure assuming its ok before doing the python-based recursive logic.

Returns:

list of “bad part” dictionaries containing items

’value’ - the value that caused the serialization error ‘loc’ - which contains a list of key/indexes that can be used

to lookup the location of the unserializable value. If the “loc” is a list, then it indicates a rare case where a key in a dictionary is causing the serialization error.

Return type:

List[Dict]

Example

>>> from kwcoco.util.util_json import *  # NOQA
>>> part = ub.ddict(lambda: int)
>>> part['foo'] = ub.ddict(lambda: int)
>>> part['bar'] = np.array([1, 2, 3])
>>> part['foo']['a'] = 1
>>> # Create a dictionary with two unserializable parts
>>> data = [1, 2, {'nest1': [2, part]}, {frozenset({'badkey'}): 3, 2: 4}]
>>> parts = list(find_json_unserializable(data))
>>> print('parts = {}'.format(ub.repr2(parts, nl=1)))
>>> # Check expected structure of bad parts
>>> assert len(parts) == 2
>>> part = parts[0]
>>> assert list(part['loc']) == [2, 'nest1', 1, 'bar']
>>> # We can use the "loc" to find the bad value
>>> for part in parts:
>>>     # "loc" is a list of directions containing which keys/indexes
>>>     # to traverse at each descent into the data structure.
>>>     directions = part['loc']
>>>     curr = data
>>>     special_flag = False
>>>     for key in directions:
>>>         if isinstance(key, list):
>>>             # special case for bad keys
>>>             special_flag = True
>>>             break
>>>         else:
>>>             # normal case for bad values
>>>             curr = curr[key]
>>>     if special_flag:
>>>         assert part['data'] in curr.keys()
>>>         assert part['data'] is key[1]
>>>     else:
>>>         assert part['data'] is curr