mt_metadata.transfer_functions.io.jfiles.metadata ================================================= .. py:module:: mt_metadata.transfer_functions.io.jfiles.metadata Submodules ---------- .. toctree:: :maxdepth: 1 /source/api/mt_metadata/transfer_functions/io/jfiles/metadata/birrp_angles/index /source/api/mt_metadata/transfer_functions/io/jfiles/metadata/birrp_block/index /source/api/mt_metadata/transfer_functions/io/jfiles/metadata/birrp_parameters/index /source/api/mt_metadata/transfer_functions/io/jfiles/metadata/header/index Classes ------- .. autoapisummary:: mt_metadata.transfer_functions.io.jfiles.metadata.BirrpAngles mt_metadata.transfer_functions.io.jfiles.metadata.BirrpBlock mt_metadata.transfer_functions.io.jfiles.metadata.BirrpParameters mt_metadata.transfer_functions.io.jfiles.metadata.Header Package Contents ---------------- .. py:class:: BirrpAngles(**data) Bases: :py:obj:`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. .. attribute:: _skip_equals Private attribute listing fields to skip in equality comparisons :type: list[str] .. attribute:: _fields Private attribute caching field information :type: dict[str, Any] .. rubric:: 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 .. py:attribute:: theta1 :type: Annotated[float, Field(default=0.0, description='rotation angle for block x', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})] .. py:attribute:: theta2 :type: Annotated[float, Field(default=0.0, description='rotation angle for block y', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['90']})] .. py:attribute:: phi :type: Annotated[float, Field(default=0.0, description='rotation angle for block', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})] .. py:class:: BirrpBlock(**data) Bases: :py:obj:`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. .. attribute:: _skip_equals Private attribute listing fields to skip in equality comparisons :type: list[str] .. attribute:: _fields Private attribute caching field information :type: dict[str, Any] .. rubric:: 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 .. py:attribute:: filnam :type: Annotated[str, Field(default='', description='File name of data block', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['hx.dat']})] .. py:attribute:: nskip :type: Annotated[int, Field(default=None, description='number of points to skip', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})] .. py:attribute:: nread :type: Annotated[int, Field(default=None, description='number of points to read', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10000']})] .. py:attribute:: ncomp :type: Annotated[int, Field(default=0, description='number of components in file', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})] .. py:attribute:: indices :type: 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]']})] .. py:method:: validate_indices(value) :classmethod: Ensure indices is a list of integers or a single integer. .. py:class:: BirrpParameters(**data) Bases: :py:obj:`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. .. attribute:: _skip_equals Private attribute listing fields to skip in equality comparisons :type: list[str] .. attribute:: _fields Private attribute caching field information :type: dict[str, Any] .. rubric:: 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 .. py:attribute:: outputs :type: Annotated[int, Field(default=None, description='Number of output channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})] .. py:attribute:: inputs :type: Annotated[int, Field(default=None, description='Number of input channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})] .. py:attribute:: references :type: Annotated[int, Field(default=None, description='Number of reference channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})] .. py:attribute:: tbw :type: Annotated[float, Field(default=0.0, description='total bandwidth of window', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.0']})] .. py:attribute:: deltat :type: 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']})] .. py:attribute:: nfft :type: Annotated[float, Field(default=0.0, description='length of time window.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['8192']})] .. py:attribute:: nsctinc :type: 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']})] .. py:attribute:: nsctmax :type: Annotated[float, Field(default=0.0, description='maximum number of sections', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.0']})] .. py:attribute:: nf1 :type: Annotated[int, Field(default=None, description='index of first frequency', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})] .. py:attribute:: nfinc :type: Annotated[int, Field(default=None, description='increment value of next frequency', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})] .. py:attribute:: nfsect :type: Annotated[int, Field(default=None, description='total number of frequencies to process.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4']})] .. py:attribute:: uin :type: Annotated[float, Field(default=0.0, description='small leverage point minimum', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.00']})] .. py:attribute:: ainlin :type: Annotated[float, Field(default=0.0, description='bounded influence value', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['-999']})] .. py:attribute:: ainuin :type: Annotated[float, Field(default=0.0, description='large leverage point minimu', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.99']})] .. py:attribute:: c2threshe :type: Annotated[float, Field(default=0.0, description='coherencey threshold for electric channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.35']})] .. py:attribute:: nz :type: Annotated[int, Field(default=None, description='Use threshold for hz channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})] .. py:attribute:: c2threshe1 :type: Annotated[float, Field(default=0.0, description='coherencey threshold for hz channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.35']})] .. py:attribute:: npcs :type: Annotated[int, Field(default=None, description='number of data segments used', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})] .. py:attribute:: nar :type: Annotated[int, Field(default=None, description='order of auto-regressive prewhitening filter.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['5']})] .. py:attribute:: imode :type: Annotated[int, Field(default=None, description='input data file mode', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})] .. py:attribute:: jmode :type: Annotated[int, Field(default=None, description='input time mode', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})] .. py:attribute:: ncomp :type: Annotated[int, Field(default=None, description='number of components', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['5']})] .. py:class:: Header(**data) Bases: :py:obj:`mt_metadata.common.BasicLocation` A partial location class that only includes the latitude, longitude, and elevation. This is used to avoid circular imports. .. py:attribute:: title :type: Annotated[str, Field(default='', description='title of file', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['BIRRP Version 5 basic mode output']})] .. py:attribute:: station :type: Annotated[str, Field(default='', description='station name', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['mt001']})] .. py:attribute:: azimuth :type: Annotated[float, Field(default=0.0, description='rotation of full impedance tensor', alias=None, json_schema_extra={'units': 'degrees', 'required': True, 'examples': ['0']})] .. py:attribute:: birrp_parameters :type: 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(...)']})] .. py:attribute:: data_blocks :type: 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(...)']})] .. py:attribute:: angles :type: 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(...)']})] .. py:method:: read_header(j_lines) Parsing the header lines of a j-file to extract processing information. :param j_lines: The lines of the j-file as a string. :type j_lines: str .. py:method:: read_metadata(j_lines) Read in the metadata of the station, or information of station logistics like: lat, lon, elevation :param j_lines: The lines of the j-file as a string. :type j_lines: str :param Not really needed for a birrp output since all values are nan's: