WilsonDaubechiesMeyer§
- class typed_lisa_toolkit.types.WilsonDaubechiesMeyer[GridT: Grid2D][source]§
Wilson-Daubechies-Meyer time-frequency representation.
Note
To construct a
WilsonDaubechiesMeyer, use the factory functionwdm().This represents data using an evenly-spaced 2D grid in the time-frequency plane with shape (Nf+1, Nt). Each “pixel” has size ΔT ΔF = 1/2. The times range approximately from 0 to the final observation time, while the frequencies range from 0 to the Nyquist limit (half the sampling rate).
Attention
The frequency grid has size Nf+1, not Nf. This is because correctly inverting WDM transforms requires some information from the Nyquist band m=Nf. This data layout contains redundant information but is simpler to interpret and to work with. It follows the convention of wdm-transform.
- 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: Grid2D, *, known_slices: tuple[slice, ...] | None = None)[source]§
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
- property ND: int§
Total number of data points in the time-frequency plane.
- property Nf: int§
Number of frequency points.
- property Nt: int§
Number of time bins.
Note
Throughout this codebase, a grid of N points is considered to have N-1 bins, since the first point is the start of the first bin and the last point is the end of the last bin.
- property dF: float§
Frequency resolution (ΔF) of the time-frequency grid.
- property dT: float§
Time resolution (ΔT) of the time-frequency grid.
- property df: float§
Frequency resolution of a FrequencySeries corresponding to this WDM.
Smaller than the wavelet frequency bin
dF.
- property domain: Literal['time-frequency']§
The physical domain of the representation.
- property dt: float§
Alias for
sample_interval.
- 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 fs: float§
Sampling rate 1/Δt for the time series equivalent to this WDM.
- 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 nyquist: float§
Nyquist frequency (half the sampling rate).
- property sample_interval: float§
Time resolution of a TimeSeries corresponding to this WDM.
Smaller than the wavelet time bin
dT.
- 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).