ButlerLogRecords¶

class lsst.daf.butler.ButlerLogRecords¶

Bases: pydantic.main.BaseModel

Class representing a collection of ButlerLogRecord.

Attributes Summary

`copy`	Duplicate a model, optionally choose which fields to include, exclude and change.
`dict`	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
`json`	Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`log_format`

Methods Summary

`append`(value, …)
`clear`()
`construct`(_fields_set, None] = None, **values)	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
`extend`(records, …)
`from_file`(filename)	Read records from file.
`from_orm`(obj)
`from_raw`(serialized, bytes])	Parse raw serialized form and return records.
`from_records`(records)	Create collection from iterable.
`from_stream`(stream)	Read records from I/O stream.
`insert`(index, value, …)
`parse_file`(path, pathlib.Path], *, …)
`parse_obj`(obj)
`parse_raw`(b, bytes], *, content_type, …)
`pop`(index)
`reverse`()
`schema`(by_alias, ref_template)
`schema_json`(*, by_alias, ref_template, …)
`set_log_format`(format, None])	Set the log format string for these records.
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)

Attributes Documentation

copy¶

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:	include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to `True` to make a deep copy of the model
Returns:	new model instance

dict¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

log_format¶

Methods Documentation

append(value: Union[logging.LogRecord, lsst.daf.butler.core.logging.ButlerLogRecord]) → None¶

clear() → None¶

classmethod construct(_fields_set: Optional[SetStr, None] = None, **values) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = 'allow' was set since it adds all passed values

extend(records: Iterable[Union[logging.LogRecord, lsst.daf.butler.core.logging.ButlerLogRecord]]) → None¶

classmethod from_file(filename: str) → lsst.daf.butler.core.logging.ButlerLogRecords¶

Read records from file.

Parameters:	filename : `str` Name of file containing the JSON records.

Notes

Works with one-record-per-line format JSON files and a direct serialization of the Pydantic model.

classmethod from_orm(obj: Any) → Model¶

classmethod from_raw(serialized: Union[str, bytes]) → lsst.daf.butler.core.logging.ButlerLogRecords¶

Parse raw serialized form and return records.

Parameters:	serialized : `bytes` or `str` Either the serialized JSON of the model created using `.json()` or a streaming format of one JSON `ButlerLogRecord` per line. This can also support a zero-length string.

classmethod from_records(records: Iterable[lsst.daf.butler.core.logging.ButlerLogRecord]) → lsst.daf.butler.core.logging.ButlerLogRecords¶

Create collection from iterable.

Parameters:	records : iterable of `ButlerLogRecord` The records to seed this class with.

classmethod from_stream(stream: IO) → lsst.daf.butler.core.logging.ButlerLogRecords¶

Read records from I/O stream.

Parameters:	stream : `typing.IO` Stream from which to read JSON records.

Notes

Works with one-record-per-line format JSON files and a direct serialization of the Pydantic model.

insert(index: int, value: Union[logging.LogRecord, lsst.daf.butler.core.logging.ButlerLogRecord]) → None¶

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model¶

pop(index: int = -1) → lsst.daf.butler.core.logging.ButlerLogRecord¶

reverse() → None¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs) → unicode¶

set_log_format(format: Optional[str, None]) → Optional[str, None]¶

Set the log format string for these records.

Parameters:	format : `str`, optional The new format string to use for converting this collection of records into a string. If `None` the default format will be used.
Returns:	old_format : `str`, optional The previous log format.

classmethod update_forward_refs(**localns) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

Navigation

ButlerLogRecords¶