def match_fn(codec: ABICodec, match_values_and_abi: Collection[Tuple[str,
Any]],
data: Any) -> bool:
"""Match function used for filtering non-indexed event arguments.
Values provided through the match_values_and_abi parameter are
compared to the abi decoded log data.
"""
abi_types, all_match_values = zip(*match_values_and_abi)
decoded_values = codec.decode_abi(abi_types, HexBytes(data))
for data_value, match_values, abi_type in zip(decoded_values,
all_match_values, abi_types):
if match_values is None:
continue
normalized_data = normalize_data_values(abi_type, data_value)
for value in match_values:
if not codec.is_encodable(abi_type, value):
raise ValueError(f"Value {value} is of the wrong abi type. "
f"Expected {abi_type} typed value.")
if value == normalized_data:
break
else:
return False
return True
def get_event_data(abi_codec: ABICodec, event_abi: ABIEvent, log_entry: LogReceipt) -> EventData:
"""
Given an event ABI and a log entry for that event, return the decoded
event data
"""
if event_abi['anonymous']:
log_topics = log_entry['topics']
elif not log_entry['topics']:
raise MismatchedABI("Expected non-anonymous event to have 1 or more topics")
# type ignored b/c event_abi_to_log_topic(event_abi: Dict[str, Any])
elif event_abi_to_log_topic(event_abi) != log_entry['topics'][0]: # type: ignore
raise MismatchedABI("The event signature did not match the provided ABI")
else:
log_topics = log_entry['topics'][1:]
log_topics_abi = get_indexed_event_inputs(event_abi)
log_topic_normalized_inputs = normalize_event_input_types(log_topics_abi)
log_topic_types = get_event_abi_types_for_decoding(log_topic_normalized_inputs)
log_topic_names = get_abi_input_names(ABIEvent({'inputs': log_topics_abi}))
if len(log_topics) != len(log_topic_types):
raise LogTopicError("Expected {0} log topics. Got {1}".format(
len(log_topic_types),
len(log_topics),
))
log_data = hexstr_if_str(to_bytes, log_entry['data'])
log_data_abi = exclude_indexed_event_inputs(event_abi)
log_data_normalized_inputs = normalize_event_input_types(log_data_abi)
log_data_types = get_event_abi_types_for_decoding(log_data_normalized_inputs)
log_data_names = get_abi_input_names(ABIEvent({'inputs': log_data_abi}))
# sanity check that there are not name intersections between the topic
# names and the data argument names.
duplicate_names = set(log_topic_names).intersection(log_data_names)
if duplicate_names:
raise InvalidEventABI(
"The following argument names are duplicated "
f"between event inputs: '{', '.join(duplicate_names)}'"
)
decoded_log_data = abi_codec.decode_abi(log_data_types, log_data)
normalized_log_data = map_abi_data(
BASE_RETURN_NORMALIZERS,
log_data_types,
decoded_log_data
)
decoded_topic_data = [
abi_codec.decode_single(topic_type, topic_data)
for topic_type, topic_data
in zip(log_topic_types, log_topics)
]
normalized_topic_data = map_abi_data(
BASE_RETURN_NORMALIZERS,
log_topic_types,
decoded_topic_data
)
event_args = dict(itertools.chain(
zip(log_topic_names, normalized_topic_data),
zip(log_data_names, normalized_log_data),
))
event_data = {
'args': event_args,
'event': event_abi['name'],
'logIndex': log_entry['logIndex'],
'transactionIndex': log_entry['transactionIndex'],
'transactionHash': log_entry['transactionHash'],
'address': log_entry['address'],
'blockHash': log_entry['blockHash'],
'blockNumber': log_entry['blockNumber'],
}
return cast(EventData, AttributeDict.recursive(event_data))
