stft§

typed_lisa_toolkit.stft(frequencies: FreqAxisT, times: TimeAxisT, entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], *, sparse_indices: None = None) → STFT[Grid2DCartesian[FreqAxisT, TimeAxisT]][source]§
typed_lisa_toolkit.stft(frequencies: FAT, times: TAT, entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], *, sparse_indices: None = None) → STFT[Grid2DCartesian[Axis[FAT], Axis[TAT]]]
typed_lisa_toolkit.stft(frequencies: FreqAxisT, times: TimeAxisT, entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], *, sparse_indices: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]) → STFT[Grid2DSparse[FreqAxisT, TimeAxisT]]
typed_lisa_toolkit.stft(frequencies: FAT, times: TAT, entries: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], *, sparse_indices: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]) → STFT[Grid2DSparse[Axis[FAT], Axis[TAT]]]

Build an ShortTimeFourierTransform.

Parameters:

  • frequencies (FreqAxisT) – Either a Linspace or a 1D array of positive frequencies.

  • times (TimeAxisT) – Either a Linspace or a 1D array of time points.

  • entries (Array) – For dense grid: an array of shape (n_batch, 1, 1, 1, Nf, Nt) where Nf and Nt are the sizes of frequencies and times respectively. For sparse grid: an array of shape (n_batch, 1, 1, 1, num_sparse_points) where num_sparse_points is the number of rows in sparse_indices.

Note

See the general description of the shape convention for entries.