ButlerLogRecord¶

class lsst.daf.butler.logging.ButlerLogRecord(*, name: str, asctime: datetime, message: str, levelno: int, levelname: str, filename: str, pathname: str, lineno: int, funcName: str | None = None, process: int, processName: str, exc_info: str | None = None, MDC: dict[str, str])¶

Bases: _BaseModelCompat

A model representing a logging.LogRecord.

A LogRecord always uses the current time in its record when recreated and that makes it impossible to use it as a serialization format. Instead have a local representation of a LogRecord that matches Butler needs.

Attributes Summary

model_fields

Methods Summary

`construct`([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
`copy`(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
`dict`(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
`format`([log_format])	Format this record.
`from_orm`(obj)
`from_record`(record)	Create a new instance from a `LogRecord`.
`json`(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`model_construct`([_fields_set])
`model_copy`(*[, update, deep])
`model_dump`(*[, mode, include, exclude, ...])
`model_dump_json`(*[, indent, include, ...])
`model_rebuild`(*[, force, raise_errors, ...])
`model_validate`(obj, *[, strict, ...])
`model_validate_json`(json_data, *[, strict, ...])
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)

Attributes Documentation

model_fields = {'MDC': ModelField(name='MDC', type=Mapping[str, str], required=True), 'asctime': ModelField(name='asctime', type=datetime, required=True), 'exc_info': ModelField(name='exc_info', type=Optional[str], required=False, default=None), 'filename': ModelField(name='filename', type=str, required=True), 'funcName': ModelField(name='funcName', type=Optional[str], required=False, default=None), 'levelname': ModelField(name='levelname', type=str, required=True), 'levelno': ModelField(name='levelno', type=int, required=True), 'lineno': ModelField(name='lineno', type=int, required=True), 'message': ModelField(name='message', type=str, required=True), 'name': ModelField(name='name', type=str, required=True), 'pathname': ModelField(name='pathname', type=str, required=True), 'process': ModelField(name='process', type=int, required=True), 'processName': ModelField(name='processName', type=str, required=True)}¶

Methods Documentation

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → 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

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(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

format(log_format: str | None = None) → str¶

Format this record.

Parameters:

log_formatstr, optional: The format string to use. This string follows the standard f-style use for formatting log messages. If None the class default will be used.

Returns:

textstr: The formatted log message.

classmethod from_orm(obj: Any) → Model¶

classmethod from_record(record: LogRecord) → ButlerLogRecord¶

Create a new instance from a LogRecord.

Parameters:

recordlogging.LogRecord: The record from which to extract the relevant information.

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

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().

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self¶

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Self¶

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, exclude: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]¶

model_dump_json(*, indent: int | None = None, include: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, exclude: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str¶

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None¶

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Self¶

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Self¶

classmethod parse_file(path: str | Path, *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: str | bytes, *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

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: Any) → unicode¶

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

classmethod validate(value: Any) → Model¶

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ButlerLogRecord¶