1 / 3

Flagging data

Flagging data. Overview. Whether it be spurs, overtly bad data, or some other reason, there are two main ways to flag data for exclusion Channel level flags (via HIFI.MASK) Dataset level flags (via ROWFLAGS)

sharis
Download Presentation

Flagging data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Flagging data

  2. Overview • Whether it be spurs, overtly bad data, or some other reason, there are two main ways to flag data for exclusion • Channel level flags (via HIFI.MASK) • Dataset level flags (via ROWFLAGS) • Currently we do not have user friendly tools to let you flag data, but it is relatively straightforward via scripts, and one can (in some cases) use the SpectrumExplorer • As with other scripts shown in this workshop, the general idea is to extract the data you want to manipulate, deal with it, then re-insert into the obs context.

  3. Tool dependent flag response • Currently, HIPE tools respond differently to different flags. • Deconvolution of SSCAN, for example, handles row flags and channel flags. • RemoveFlaggedPixels is a task that lets users quickly remove flagged data via setting to NaN or interpolation • HIPE help search on ‘rowflags’ will help you parse what flags correspond to.

More Related