mt_metadata.transfer_functions.io.jfiles.metadata.birrp_parameters

Classes

BirrpParameters

Base class for all metadata objects with Pydantic validation.

Module Contents

class mt_metadata.transfer_functions.io.jfiles.metadata.birrp_parameters.BirrpParameters(**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

outputs: Annotated[int, Field(default=None, description='Number of output channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]

inputs: Annotated[int, Field(default=None, description='Number of input channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]

references: Annotated[int, Field(default=None, description='Number of reference channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]

tbw: Annotated[float, Field(default=0.0, description='total bandwidth of window', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.0']})]

deltat: Annotated[float, Field(default=0.0, description='sampling spacing, if negative sample rate.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['1.0']})]

nfft: Annotated[float, Field(default=0.0, description='length of time window.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['8192']})]

nsctinc: Annotated[float, Field(default=0.0, description='number by which the segment length is divided by to get next window.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.0']})]

nsctmax: Annotated[float, Field(default=0.0, description='maximum number of sections', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.0']})]

nf1: Annotated[int, Field(default=None, description='index of first frequency', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})]

nfinc: Annotated[int, Field(default=None, description='increment value of next frequency', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]

nfsect: Annotated[int, Field(default=None, description='total number of frequencies to process.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})]

uin: Annotated[float, Field(default=0.0, description='small leverage point minimum', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.00']})]

ainlin: Annotated[float, Field(default=0.0, description='bounded influence value', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['-999']})]

ainuin: Annotated[float, Field(default=0.0, description='large leverage point minimu', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.99']})]

c2threshe: Annotated[float, Field(default=0.0, description='coherencey threshold for electric channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.35']})]

nz: Annotated[int, Field(default=None, description='Use threshold for hz channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})]

c2threshe1: Annotated[float, Field(default=0.0, description='coherencey threshold for hz channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.35']})]

npcs: Annotated[int, Field(default=None, description='number of data segments used', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]

nar: Annotated[int, Field(default=None, description='order of auto-regressive prewhitening filter.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['5']})]

imode: Annotated[int, Field(default=None, description='input data file mode', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})]

jmode: Annotated[int, Field(default=None, description='input time mode', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})]

ncomp: Annotated[int, Field(default=None, description='number of components', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['5']})]