mt_metadata.processing.fourier_coefficients

Submodules

• mt_metadata.processing.fourier_coefficients.decimation
• mt_metadata.processing.fourier_coefficients.fc
• mt_metadata.processing.fourier_coefficients.fc_channel

Classes

Decimation	Base class for all metadata objects with Pydantic validation.
FC	Base class for all metadata objects with Pydantic validation.
FCChannel	Base class for all metadata objects with Pydantic validation.

Package Contents

class mt_metadata.processing.fourier_coefficients.Decimation(**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

id: Annotated[str, Field(default='', description='Decimation level ID', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['1']})]

channels_estimated: Annotated[list[str], Field(default_factory=list, description='list of channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['[ex, hy]']})]

time_period: Annotated[mt_metadata.common.TimePeriod, Field(default_factory=TimePeriod, description='Time period over which these FCs were estimated', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['TimePeriod()']})]

channels: Annotated[mt_metadata.common.ListDict, Field(default_factory=ListDict, description='List of channels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['[ex, hy]']})]

time_series_decimation: Annotated[mt_metadata.processing.TimeSeriesDecimation, Field(default_factory=TimeSeriesDecimation, description='Time series decimation settings', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['TimeSeriesDecimation()']})]

short_time_fourier_transform: Annotated[mt_metadata.processing.ShortTimeFourierTransform, Field(default_factory=ShortTimeFourierTransform, description='Short time Fourier transform settings', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['ShortTimeFourierTransform()']})]

classmethod validate_short_time_fourier_transform(value, info)

classmethod validate_channels_estimated(value, info)

classmethod validate_channels(value, info)

validate_channels_consistency()

Ensure that channels_estimated and channels are synchronized.

• If a channel name exists in channels_estimated but not in channels, create a new FCChannel with that component name

• Ensure all channels in channels ListDict have their component names in channels_estimated

add(other)

Parameters:

other

Returns:

property decimation: mt_metadata.processing.TimeSeriesDecimation

Passthrough method to access self.time_series_decimation

property stft

update(other, match=[])

Update attribute values from another like element, skipping None

Parameters:

other (TYPE) – DESCRIPTION

Returns:

DESCRIPTION

Return type:

TYPE

has_channel(component)

Check to see if the channel already exists

Parameters:

component (string) – channel component to look for

Returns:

True if found, False if not

Return type:

boolean

channel_index(component)

get index of the channel in the channel list

get_channel(component)

Get a channel

Parameters:

component (string) – channel component to look for

Returns:

FCChannel object based on channel type

Return type:

mt_metadata.timeseries.Channel

add_channel(channel_obj)

Add a channel to the list, check if one exists if it does overwrite it

Parameters:

channel_obj (mt_metadata.transfer_functions.processing.fourier_coefficients.Channel) – channel object to add

remove_channel(channel_id)

remove a channel from the survey

Parameters:

component (string) – channel component to look for

property n_channels

update_time_period()

update time period from ch information

is_valid_for_time_series_length(n_samples_ts)

Given a time series of len n_samples_ts, checks if there are sufficient samples to STFT.

property fft_frequencies: numpy.ndarray

Returns the one-sided fft frequencies (without Nyquist)

class mt_metadata.processing.fourier_coefficients.FC(**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

decimation_levels: Annotated[list[str], Field(default_factory=list, description='List of decimation levels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['[1, 2, 3]']})]

id: Annotated[str, Field(default='', description='ID given to the FC group', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['aurora_01']})]

channels_estimated: Annotated[list[str], Field(default_factory=list, description='list of channels estimated', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [['ex', 'hy']]})]

starting_sample_rate: Annotated[float, Field(default=1.0, description='Starting sample rate of the time series used to estimate FCs.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [60]})]

method: Annotated[MethodEnum, Field(default=MethodEnum.fft, description='Fourier transform method', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['fft']})]

time_period: Annotated[mt_metadata.common.TimePeriod, Field(default_factory=TimePeriod, description='Time period of the FCs', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [TimePeriod(start='2020-01-01', end='2020-01-02')]})]

levels: Annotated[mt_metadata.common.ListDict, Field(default_factory=ListDict, description='ListDict of decimation levels and their parameters', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['ListDict containing Decimation objects']})]

classmethod validate_channels_estimated(value, info)

classmethod validate_levels(value, info)

synchronize_levels()

Ensure that decimation_levels and levels are synchronized. - Creates Decimation objects for any levels in decimation_levels that don’t exist in levels - Adds level names to decimation_levels for any existing levels not in the list

has_decimation_level(level)

Check to see if the decimation_level already exists

Parameters:

level (string) – decimation_level level to look for

Returns:

True if found, False if not

Return type:

boolean

decimation_level_index(level)

get index of the decimation_level in the decimation_level list

get_decimation_level(level)

Get a decimation_level

Parameters:

level (string) – decimation_level level to look for

Returns:

decimation_level object based on decimation_level type

Return type:

mt_metadata.timeseries.decimation_level

add_decimation_level(fc_decimation)

Add a decimation_level to the list, check if one exists if it does overwrite it

Parameters:

fc_decimation (mt_metadata.processing.fourier_coefficients.decimation_basemodel.Decimation) – decimation level object to add

remove_decimation_level(decimation_level_id)

remove a ch from the survey

Parameters:

level (string) – decimation_level level to look for

property n_decimation_levels

update_time_period()

update time period from ch information

class mt_metadata.processing.fourier_coefficients.FCChannel(**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

component: Annotated[str, Field(default='', description='Name of channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['ex']})]

frequency_max: Annotated[float, Field(default=0.0, description='Highest frequency present in the sprectrogam data.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [77.0]})]

frequency_min: Annotated[float, Field(default=0.0, description='Lowest frequency present in the sprectrogam data.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [99.0]})]

sample_rate_decimation_level: Annotated[float, Field(default=1.0, description='Sample rate of the time series that was Fourier transformed to generate the FC decimation level.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [60]})]

sample_rate_window_step: Annotated[float, Field(default=1.0, description='Sample rate of the windows.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [4]})]

units: Annotated[str, Field(default='counts', description='Units of the channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['millivolts']})]

time_period: Annotated[mt_metadata.common.TimePeriod, Field(default_factory=TimePeriod, description='Time period of the channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [TimePeriod(start='2020-01-01', end='2020-01-02')]})]

classmethod validate_units(value)