ButlerLogRecords

class lsst.daf.butler.ButlerLogRecords(*, __root__: List[ButlerLogRecord])

Bases: BaseModel

Class representing a collection of ButlerLogRecord.

Attributes Summary

log_format

Methods Summary

append(value)
clear()
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.
extend(records)
from_file(filename)	Read records from file.
from_orm(obj)
from_raw(serialized)	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)
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
pop([index])
reverse()
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
set_log_format(format)	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

log_format

Methods Documentation

append(value: Union[LogRecord, ButlerLogRecord]) → None

clear() → None

classmethod construct(_fields_set: Optional[SetStr] = 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

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

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: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = 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.

extend(records: Iterable[Union[LogRecord, ButlerLogRecord]]) → None

classmethod from_file(filename: str) → ButlerLogRecords

Read records from file.

Parameters:

filenamestr

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]) → ButlerLogRecords

Parse raw serialized form and return records.

Parameters:

serializedbytes 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[ButlerLogRecord]) → ButlerLogRecords

Create collection from iterable.

Parameters:

recordsiterable of ButlerLogRecord

The records to seed this class with.

classmethod from_stream(stream: IO) → ButlerLogRecords

Read records from I/O stream.

Parameters:

streamtyping.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[LogRecord, ButlerLogRecord]) → None

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = 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 parse_file(path: Union[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: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

pop(index: int = -1) → 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: Any) → unicode

set_log_format(format: Optional[str]) → Optional[str]

Set the log format string for these records.

Parameters:

formatstr, 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_formatstr, optional

The previous log format.

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