It is finally nearing the end of the project for me, as far as coding is concerned. Over the past few weeks I have spent some time on some last-ditch effort to debug with my mentors and to squeeze as much as I possibly could given the time constraint I have been under. These include but not limited to sorting out some generalisation issues that previously prevented glue PR #2167 from being usable for general FITS files, some type as well as wcs linkages issues in glue PR #2161 that cropped up after applying changes suggested in code review that have not been properly checked on my part. The pull requests started or completed for the project include but are not limited to the following list:
- glue PR #2167 for updating 1D Profile viewer to use wcsaxes for plotting and add sliders
- glue PR #2161 for updating ‘wcs_autolinking’ code to handle N-D cases using a generalised approach conforming to APE 14: Shared Python interface for World Coordinate Systems
- glue PR #2164 for adding support to NDData for astropy package
- glue PR #2131 for adding a preferred_cmap attribute to introduce a color coding scheme for different glue-solar data types (for example to distinguish the IRIS raster data cubes from its companion IRIS SJI data cubes
- glue-solar PR #15 for adding to open with “SunPy Map” GUI option
- glue-solar PR #17 for adding “Loading and Overplotting AIA and HMI files as SunPy Maps” docs as a user guide
- glue-solar PR #18 for adding “loading IRIS level 2 raster and sji data together docs” as a user guide
- glue-solar PR #23 for updating IRIS data labels with OBSIDs for filtering
- glue-solar PR #28, PR #29 as core glue-solar documentation
I have actually been using our work on the glue 1D profile tool for my current doctoral studies on studies of planetary nebulae using integral field spectroscopy (IFS) involving the handling of a large number of data cubes from some Australian telescopes (data collected by my PhD supervisor Prof. Quentin Parker). Turned out this tool made the process of investigating the different spectra, which could run up to hundreds in number per data cube or observation, as it allows me to load in my data cube only once, and then to inspect the variation across spatial dimensions to see if the signal-to-noise of a particular observation is too high, or if the opposite is true so that the spectra will then be further processed into full-optical integrated spectra with flux calibration or de-reddening as necessary.
I am grateful for Google, my mentors, other org members as well as my GSoC peers to make this a particular fun-filled and memorable project! I have learned so much from the experience that even money cannot buy in terms of programming and soft skills. I wish Google will continue this program or initiate some similar program to continue cultivating new generations of open-source software developers / development enthusiasts to further our aim to make open-source approachable and usable for all.