mt_metadata.transfer_functions.io.emtfxml.metadata.run

Classes

Run	Base class for all metadata objects with Pydantic validation.

Module Contents

class mt_metadata.transfer_functions.io.emtfxml.metadata.run.Run(**data)

Bases: mt_metadata.base.MetadataBase

Base class for all metadata objects with Pydantic validation.

MetadataBase extends DotNotationBaseModel (which inherits from Pydantic’s BaseModel) to provide automatic validation according to metadata standards. It adds functionality beyond dictionaries, supporting JSON, XML, pandas Series, and other formats for metadata interchange.

_skip_equals

Private attribute listing fields to skip in equality comparisons

Type:

list[str]

_fields

Private attribute caching field information

Type:

dict[str, Any]

Notes

• All field assignments are validated automatically via Pydantic

• None values are converted to appropriate defaults (empty string or 0.0)

• Supports nested attribute access via dot notation

• Thread-safe for read operations after initialization

errors: Annotated[str | None, Field(default=None, description='Any field errors', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ['moose ate cables']})]

run: Annotated[str, Field(default='', description='Run name', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['mt001a']})]

sampling_rate: Annotated[float | None, Field(default=None, description='Sample rate of the run', alias=None, json_schema_extra={'units': 'samples per second', 'required': False, 'examples': ['1']})]

start: Annotated[mt_metadata.common.mttime.MTime | str | float | int | numpy.datetime64 | pandas.Timestamp, Field(default_factory=lambda: MTime(time_stamp=None), description='Date time when the data collection started', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2020-01-01T12:00:00']})]

end: Annotated[mt_metadata.common.mttime.MTime | str | float | int | numpy.datetime64 | pandas.Timestamp, Field(default_factory=lambda: MTime(time_stamp=None), description='Date time when the data collection ended', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2020-05-01T12:00:00']})]

comments: Annotated[mt_metadata.common.Comment | str | None, Field(default_factory=Comment, description='Comments about the run', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ["Comment(text='This is a comment')"]})]

instrument: Annotated[mt_metadata.transfer_functions.io.emtfxml.metadata.Instrument, Field(default_factory=Instrument, description='Instrument used for the run', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ["Instrument(name='MT Sensor', type='magnetometer')"]})]

magnetometer: Annotated[list[mt_metadata.transfer_functions.io.emtfxml.metadata.Magnetometer], Field(default_factory=list, description='List of magnetometers used in the run', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ["Magnetometer(name='Magnetometer 1', type='fluxgate')"]})]

dipole: Annotated[list[mt_metadata.transfer_functions.io.emtfxml.metadata.Dipole], Field(default_factory=list, description='List of dipoles used in the run', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ["Dipole(name='Dipole 1', type='fluxgate')"]})]

classmethod validate_start(field_value)

classmethod validate_comments(field_value)

read_dict(input_dict)

Field notes are odd so have a special reader to do it piece by painstaking piece.

Parameters:

input_dict (dict) – input dictionary containing run data

Returns:

None

Return type:

None

to_xml(string=False, required=True)

Parameters:

• string (TYPE, optional) – DESCRIPTION, defaults to True

• required (TYPE, optional) – DESCRIPTION, defaults to False

Returns:

DESCRIPTION

Return type:

TYPE