TFNoiseModel

class typed_lisa_toolkit.types.TFNoiseModel

Time-frequency Gaussian noise model.

This model is a Gaussian noise model suitable for non-stationary noise. The covariance matrix is diagonal in the chosen time-frequency representation. The model allows correlations between TDI channels. In other words, we have a 3x3 symmetric matrix at each location in the time-frequency plane.

Note

To construct a TFNoiseModel, use the noise_model() factory function.

Attention

This class is considered experimental. If you are interested in using it, please reach out to the developers to discuss your use case and how we can best support it.

get_scalar_product(left: WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]], right: WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]]) → Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]

Return the scalar product.

inner(left: WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]], right: WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]]) → Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]

Alias for get_scalar_product().

whiten(_data: WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]]) → WDMData[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]] | ProjectedWaveform[WilsonDaubechiesMeyer[Grid2D[Axis[Linspace, ModuleType], Axis[Linspace, ModuleType]]]]

Whiten the data according to the noise model.