A common task with data cubes is continuum identification and subtraction. For line-rich cubes where the continuum is difficult to identify, you should use statcont. For single-line cubes, the process is much easier.
First, the simplest case is when you have a single line that makes up a small fraction of the total observed band, e.g., a narrow line. In this case, you can use a simple median approximation for the continuum.:
>>> med = cube.median(axis=0) >>> med_sub_cube = cube - med
The second part of this task may complain that the cube is too big. If it
does, you can still do the above operation by first setting
cube.allow_huge_operations=True, but be warned that this can be expensive.
For a more complicated case, you may want to mask out the line-containing channels. This can be done using a spectral boolean mask.:
>>> from astropy import units as u >>> import numpy as np >>> spectral_axis = cube.with_spectral_unit(u.km/u.s).spectral_axis >>> good_channels = (spectral_axis < 25*u.km/u.s) | (spectral_axis > 45*u.km/u.s) >>> masked_cube = cube.with_mask(good_channels[:, np.newaxis, np.newaxis]) >>> med = masked_cube.median(axis=0) >>> med_sub_cube = cube - med
good_channels is a simple 1D numpy boolean array that is
for all channels below 25 km/s and above 45 km/s, and is
False for all
channels in the range 25-45 km/s. The indexing trick
np.newaxis, np.newaxis] (or equivalently,
good_channels[:, None, None])
is just a way to tell the cube which axes to project along. In more
recent versions of
spectral-cube, the indexing trick is not necessary.
The median in this case is computed only over the specified line-free channels.
Any operation can be used to compute the continuum, such as the
percentile, but for most use cases, the
median is fine.