rubix.telescope.lsf package

rubix.telescope.lsf package#

Submodules#

rubix.telescope.lsf.lsf module#

Mainly reimplmented from SimSpin: kateharborne/SimSpin

rubix.telescope.lsf.lsf.apply_lsf(datacube: Float[Array, 'n1 n2 wave_bins'], lsf_sigma: float, wave_resolution: float, extend_factor: int = 12) → Float[Array, 'n1 n2 wave_bins'][source]#

Apply the Line Spread Function (LSF) to a datacube.

This function first flattens the datacube, applies the LSF to the spectra, and then reshapes the datacube back to the original shape.

Parameters:

  • datacube (ndarray) – The input datacube to apply the LSF to.

  • lsf_sigma (float) – The sigma of the LSF. Currently a Gaussian kernel.

  • wave_resolution (float) – The wave resolution of the spectra inside the datacube.

  • extend_factor (int) – The factor to extend the kernel by.

Returns:

convolved – The convolved datacube.

Return type:

ndarray

rubix.telescope.lsf.lsf.apply_lsf_spectra(spectra: Float[Array, 'n_spectra wave_bins'], lsf_sigma: float, wave_resolution: float, extend_factor: int = 12) → Float[Array, 'n_spectra wave_bins'][source]#

Apply the Line Spread Function (LSF) to multiple spectra.

This function applies the LSF to multiple spectra in parallel using JAX’s vmap. Currently only supports a Gaussian kernel and fixed wave resolution across all spectra and wavelenghts.

Parameters:

  • spectra (ndarray) – The input spectra to apply the LSF to.

  • lsf_sigma (float) – The sigma of the LSF. Currently a Gaussian kernel.

  • wave_resolution (float) – The wave resolution of the spectra.

  • extend_factor (int) – The factor to extend the kernel by.

Returns:

convolved – The convolved spectra.

Return type:

ndarray

rubix.telescope.lsf.lsf.gaussian1d(x: Float[Array, 'n_x'], sigma: float) → Float[Array, 'n_x'][source]#

Module contents#

Contents