UniformTimeSeries

class typed_lisa_toolkit.types.UniformTimeSeries

A time series on a uniform time grid.

Note

To construct a UniformTimeSeries, use the factory function time_series().

add(other: Self, slice: tuple[slice, ...], *, inplace: bool = False) → Self

Add another series on a sub-grid with known slice.

This method adds another series on a sub-grid of the current series with a known slice, which is used to select the entries of the current series to be added with.

If inplace is True, the current series is modified in place and returned (equivalent to calling iadd()). Otherwise, a new series is returned with the result of the addition. Default is False.

See also

iadd()

create_like(entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]])

Create a new instance with the same grid as the current one.

get_embedded(embedding_grid: Grid1D, *, known_slices: tuple[slice, ...] | None = None)

Return the series embedded in a new 1D grid.

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

Return the entries of the representation.

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

Return the subset as a new instance.

iadd(other: Self, slice: tuple[slice | EllipsisType, ...]) → Self

Add another series on a sub-grid with known slice in place.

See also

add()

rfft(*, tapering: Tapering | None = None)

Fast Fourier transform of the series (Deprecated).

Note

Unlike the inverse transform FrequencySeries.irfft(), this method does not allow taking a frequency grid as input. Time series are considered as primary representations for DATA, in the sense that they are the most directly related to what we measure.

Warning

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

property domain: Literal['time']

The physical domain of the representation.

property dt: float

The time step. Alias for resolution.

property kind: None

The semantic kind of the representation.

property n_batches: int | None

Return the number of batches.

property n_channels: int | None

Return the number of channels.

property n_features: int | None

Return the number of features.

property n_harmonics: int | None

Return the number of harmonics.

property t_end: float

The end time of the series.

property t_start: float

The onset time of the series.

property times: AxisT

The times of the series.

property xp

The underlying array namespace (numpy-like module).