FrequencySeries§
- class typed_lisa_toolkit.types.FrequencySeries[AxisT: AnyAxis][source]§
A series of numbers on a frequency grid.
Note
To construct a
FrequencySeries, use the factory functionfrequency_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
- 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)[source]§
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
- property domain: Literal['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: AxisT§
The frequencies of the series.
- 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 xp§
The underlying array namespace (numpy-like module).