Custom Query (241 matches)

To implement spectrum baseline estimate (and removing) by a polynomial running fit.

It is similar to a running median (or running mean), but, for any channel, a polynomial fit is performed over a narrow range of channels centred on the channel itself. The baseline estimate is then the fit evaluated in the position of the channel. Both range width and polynomial degree should be set by the user. The polynomial degree default value could be 2 (quadratic fit) which I usually found very effective in other applications.

I implemented this method for other applications and found it very effective with complex baselines, which otherwise would require high degree polynomials if fit over the whole range with poor performances.

For small selections the user usually applies, e.g. just selecting one IF, the selection mechanism is a little bit of overkill.

Change the scantable and selector to handle state settings directly through arguments, e.g.

s = scantable('xyz')
s.set_selection(ifs=1)
sel = selector()
sel.set_ifs(1)
s.set_selection(sel)
#instead of
# and
sel = selector(ifs=1)
#instead of
sel = selector()
sel.set_ifs(1)

The old system should be maintained though for handling complex selections.

We need a way to get hold of the (spectral) coordinate information in python, to support calculation outside of scantable.