TSData

class typed_lisa_toolkit.types.TSData

Multi-channel time series data container.

Note

To construct a TSData, use the factory function tsdata().

classmethod from_dict(data_dict: Mapping[str, AnyReps], /, **kwargs: Any) → Self

Create a new instance from a dictionary of channel names to representations.

Warning

This is an expert-level API. Most users should use the top-level factory functions in typed_lisa_toolkit.

classmethod from_entries(*, times: Axis[Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]] | Linspace, ModuleType], entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], channels: tuple[str, ...], name: str | None = None) → Self

Construct from raw time-domain entries and explicit channel names.

Warning

This method is considered an expert-level API; for most users, prefer to construct a TSData with the factory function tsdata().

classmethod load(file_path: str | Path, *, legacy: bool = False)

Load the data from an HDF5 file (Deprecated).

Warning

This method is deprecated and will be removed in 0.8.0; use load_data() instead.

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

get_embedded(embedding_grid: Grid1D[Axis[Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]] | Linspace, ModuleType]], *, known_slices: tuple[slice, ...] | None = None)

Return data embedded on a new 1D grid.

get_frequencies()

Return the frequencies grid matching the time grid.

get_kernel() → Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]

Return kernel entries in conventional shape.

get_subset(*, interval: tuple[float, float] | None = None, slice: slice | None = None) → Self

Return the subset as a new instance.

get_zero_padded(pad_time: tuple[float, float], tapering: Tapering | None = None) → TSData

Return the zero-padded data.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pick(channels: str | tuple[str, ...]) → Self

Return a new instance containing only the specified channels.

save(file_path: str | Path)

Save the data to an HDF5 file.

The data are saved in the following structure:

• The root level contains the attribute type with the class name.

• Each channel is saved as a group.

• The group contains two datasets:

  • grid for the grid.

  • entries for the entries.

• For TimedFSData, there will be a dataset times at the root level containing the time grid.

set_name(name: str | None) → Self

Set the name of the data container.

The name is only used for labeling the data container in plots.

Note

This method returns self to allow for fluent method chaining.

to_fsdata(*, keep_times: Literal[False], tapering: tapering.Tapering | None = None) → FSData

to_fsdata(*, keep_times: Literal[True] = True, tapering: tapering.Tapering | None = None) → TimedFSData

Return the frequency series data (Deprecated).

Warning

This method is deprecated and will be removed in 0.8.0; use time2freq() instead.

Returns:

The frequency series data. If keep_times is True, the time grid is kept.

Return type:

FSData | TimedFSData

values() → an object providing a view on D's values

property channel_names: tuple[str, ...]

Return the channel names.

property domain: Literal['time', 'frequency', 'time-frequency']

Physical domain shared by all channels.

property dt: float

Return the time step.

property grid

Return the grid.

property kind: None

Semantic kind of the data.

property t_end: float

Return the end time.

property t_start: float

Return the start time.

property times

Return the times.

property xp

The underlying array namespace (numpy-like module).