mt_metadata.transfer_functions.io.jfiles.metadata

Submodules

• mt_metadata.transfer_functions.io.jfiles.metadata.birrp_angles
• mt_metadata.transfer_functions.io.jfiles.metadata.birrp_block
• mt_metadata.transfer_functions.io.jfiles.metadata.birrp_parameters
• mt_metadata.transfer_functions.io.jfiles.metadata.header

Classes

BirrpAngles	Base class for all metadata objects with Pydantic validation.
BirrpBlock	Base class for all metadata objects with Pydantic validation.
BirrpParameters	Base class for all metadata objects with Pydantic validation.
Header	A partial location class that only includes the latitude, longitude, and elevation.

Package Contents

class mt_metadata.transfer_functions.io.jfiles.metadata.BirrpAngles(**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

theta1: Annotated[float, Field(default=0.0, description='rotation angle for block x', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})]

theta2: Annotated[float, Field(default=0.0, description='rotation angle for block y', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['90']})]

phi: Annotated[float, Field(default=0.0, description='rotation angle for block', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})]

class mt_metadata.transfer_functions.io.jfiles.metadata.BirrpBlock(**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

filnam: Annotated[str, Field(default='', description='File name of data block', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['hx.dat']})]

nskip: Annotated[int, Field(default=None, description='number of points to skip', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})]

nread: Annotated[int, Field(default=None, description='number of points to read', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10000']})]

ncomp: Annotated[int, Field(default=0, description='number of components in file', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})]

indices: Annotated[list[int] | int | str, Field(default_factory=list, description='index values to use', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['[1, 2]']})]

classmethod validate_indices(value)

Ensure indices is a list of integers or a single integer.

class mt_metadata.transfer_functions.io.jfiles.metadata.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']})]

class mt_metadata.transfer_functions.io.jfiles.metadata.Header(**data)

Bases: mt_metadata.common.BasicLocation

A partial location class that only includes the latitude, longitude, and elevation. This is used to avoid circular imports.

title: Annotated[str, Field(default='', description='title of file', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['BIRRP Version 5 basic mode output']})]

station: Annotated[str, Field(default='', description='station name', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['mt001']})]

azimuth: Annotated[float, Field(default=0.0, description='rotation of full impedance tensor', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})]

birrp_parameters: Annotated[mt_metadata.transfer_functions.io.jfiles.metadata.BirrpParameters, Field(default_factory=BirrpParameters, description='BIRRP parameters', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['BirrpParameters(...)']})]

data_blocks: Annotated[list[mt_metadata.transfer_functions.io.jfiles.metadata.BirrpBlock], Field(default_factory=list, description='BIRRP data blocks', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['BirrpBlock(...)']})]

angles: Annotated[list[mt_metadata.transfer_functions.io.jfiles.metadata.BirrpAngles], Field(default_factory=list, description='BIRRP angles', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['BirrpAngles(...)']})]

read_header(j_lines)

Parsing the header lines of a j-file to extract processing information.

Parameters:

j_lines (str) – The lines of the j-file as a string.

read_metadata(j_lines)

Read in the metadata of the station, or information of station logistics like: lat, lon, elevation

Parameters:

• j_lines (str) – The lines of the j-file as a string.

• nan's (Not really needed for a birrp output since all values are)