ShortTimeFourierTransform

class typed_lisa_toolkit.types.ShortTimeFourierTransform[GridT: Grid2D]

Short-time Fourier transform time-frequency representation.

Note

To construct an ShortTimeFourierTransform, use the factory function stft().

classmethod make(*, times: Axis[Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]] | Linspace, ModuleType], frequencies: Axis[Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]] | Linspace, ModuleType], entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]) → Self

Create a time-frequency representation from time and frequency grids and entries.

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: Grid2D[A0, A1], *, known_slices: tuple[slice, ...] | None = None) → STFT[Grid2DSparse[A0, A1]]

get_embedded(embedding_grid: Grid2D[A0, A1], *, known_slices: tuple[slice, ...] | None = None) → STFT[Grid2DCartesian[A0, A1]]

Return the representation embedded in a new 2D grid.

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

Return the entries of the representation.

get_subset(*, time_interval: tuple[float, float] | None = None, freq_interval: tuple[float, float] | None = None, slices: tuple[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()

property domain: Literal['time-frequency']

The physical domain of the representation.

property f_max: float

The maximum frequency of the series.

property f_min: float

The minimum frequency of the series.

property frequencies

The frequency grid of the time-frequency representation.

property kind: str

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

The time grid of the time-frequency representation.

property xp

The underlying array namespace (numpy-like module).