mt_metadata.transfer_functions.io.emtfxml.metadata.data

Created on Mon Sep 6 13:53:55 2021

@author: jpeacock

Classes

TransferFunction

Deal with the complex XML format

Module Contents

class mt_metadata.transfer_functions.io.emtfxml.metadata.data.TransferFunction(**data)

Bases: mt_metadata.base.MetadataBase

Deal with the complex XML format

period: Annotated[numpy.typing.NDArray[numpy.float64] | None, Field(default_factory=lambda: np.empty((0, ), dtype=np.float64), description='periods for estimates', alias=None, json_schema_extra={'units': 'second', 'required': True, 'examples': ['0.01', '0.1', '1.0']})]

z: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 2, 2), dtype=np.complex128), description='Estimates of the impedance tensor.', json_schema_extra={'units': '[mV/km]/[nT]', 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

z_var: Annotated[numpy.typing.NDArray[numpy.float64] | None, Field(default_factory=lambda: np.empty((0, 2, 2), dtype=np.float64), description='Variance estimates for the impedance tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['0.01', '0.1', '1.0']})]

z_invsigcov: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 2, 2), dtype=np.complex128), description='Inverse of the covariance matrix for the impedance tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

z_residcov: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 2, 2), dtype=np.complex128), description='Residual covariance matrix for the impedance tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

t: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 1, 2), dtype=np.complex128), description='Estimates of the tipper tensor.', json_schema_extra={'units': '[]', 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

t_var: Annotated[numpy.typing.NDArray[numpy.float64] | None, Field(default_factory=lambda: np.empty((0, 1, 2), dtype=np.float64), description='Variance estimates for the tipper tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['0.01', '0.1', '1.0']})]

t_invsigcov: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 2, 2), dtype=np.complex128), description='Inverse of the covariance matrix for the tipper tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

t_residcov: Annotated[numpy.typing.NDArray[numpy.complex128] | None, Field(default_factory=lambda: np.empty((0, 1, 1), dtype=np.complex128), description='Residual covariance matrix for the tipper tensor.', json_schema_extra={'units': None, 'required': False, 'examples': ['1.0+0.0j', '0.5+0.5j']})]

classmethod validate_array(value, info)

Validate that the value is a numpy array or None.

initialize_arrays(n_periods)

Initialize arrays for the transfer function data.

Parameters:

n_periods (int) – number of periods

Returns:

None

Return type:

None

property array_dict: dict

property n_periods: int

read_block(block, period_index)

Read a period block which is root_dict[“data”][“period”][ii]

Parameters:

• block (dict) – read a period block

• period_index (int) – index of the period in the data

Returns:

None

Return type:

None

read_dict(root_dict)

read root_dict[“data”] This is the main data block for the transfer function data. :param root_dict: dictionary containing the transfer function data :type root_dict: dict :return: None :rtype: None

write_block(parent, index)

Write a data block

Parameters:

parent (TYPE) – DESCRIPTION

Returns:

DESCRIPTION

Return type:

TYPE

to_xml(string=False, required=True)

Write data blocks

Parameters:

parent (TYPE) – DESCRIPTION

Returns:

DESCRIPTION

Return type:

TYPE