liger_iris_sim.sources.resample

Functions

bin_image(image, scale_in, scale_out[, fix_even])

frebin(array, new_shape[, total])

rebin(image, new_shape)

resample_convolve_cube(wave_in, cube_in, ...)

Resample and convolve an IFU cube.

resample_normalize_spectral_template(...)

liger_iris_sim.sources.resample.bin_image(image: ndarray, scale_in: tuple, scale_out: tuple, fix_even: bool = False) → ndarray[source]
liger_iris_sim.sources.resample.frebin(array, new_shape, total=True)[source]
liger_iris_sim.sources.resample.rebin(image, new_shape)[source]
liger_iris_sim.sources.resample.resample_convolve_cube(wave_in: ndarray, cube_in: ndarray, scale_in: float, wave_out: ndarray | None = None, scale_out: float | None = None, size_out: tuple[int, int] | None = None, resolution: float | None = None, psf: ndarray | None = None) → ndarray[source]

Resample and convolve an IFU cube.

Parameters:

  • wave_in (np.ndarray) – Input wavelength grid (any unit).

  • cube_in (np.ndarray) – Input IFU cube with shape (ny, nx, nw) with units power per unit area.

  • scale_in (float) – Input pixel scale (any unit).

  • wave_out (np.ndarray, optional) – Output wavelength grid. Defaults to input grid.

  • scale_out (float, optional) – Output pixel scale (any unit). Defaults to input scale.

  • size_out (tuple[int, int], optional) – Output image size as (ny, nx). Default is to match input FOV.

  • resolution (float, optional) – Spectral resolution in km/s. if None, spectral convolution is skipped.

  • psf (np.ndarray, optional) – PSF kernel for spatial convolution. If not provided, spatial convolution is skipped.

Returns:

Resampled and convolved IFU cube with shape and plate scale specified by the input arguments.

Return type:

np.ndarray

liger_iris_sim.sources.resample.resample_normalize_spectral_template(template_wave: ndarray, template_spec: ndarray, wave_out: ndarray | None) → ndarray[source]