Posts with the Tag pyVO:

  • A Proposal for Persistent TAP Uploads

    From its beginning, the IVOA's Table Access Protocol TAP has let users upload their own tables into the services' databases, which is an important element of TAP's power (cf. our upload crossmatch use case for a minimal example). But these uploads only exist for the duration of the request. Having more persistent user-uploaded tables, however, has quite a few interesting applications.

    Inspired by Pat Dowler's 2018 Interop talk on youcat I have therefore written a simple implementation for persistent tables in GAVO's server package DaCHS. This post discusses what is implemented, what is clearly still missing, and how you can play with it.

    If all you care about is using this from Python, you can jump directly to a Jupyter notebook showing off the features; it by and large explains the same things as this blogpost, but using Python instead of curl and TOPCAT. Since pyVO does not know about the proposed extensions, the code necessarily is still a bit clunky in places, but if something like this will become more standard, working with persistent uploads will look a lot less like black art.

    Before I dive in: This is certainly not what will eventually become a standard in every detail. Do not do large implementations against what is discussed here unless you are prepared to throw away significant parts of what you write.

    Creating and Deleting Uploads

    Where Pat's 2018 proposal re-used the VOSI tables endpoint that every TAP service has, I have provisionally created a sibling resource user_tables – and I found that usual VOSI tables and the persistent uploads share virtually no server-side code, so for now this seems a smart thing to do. Let's see what client implementors think about it.

    What this means is that for a service with a base URL of http://dc.g-vo.org/tap[1], you would talk to (children of) http://dc.g-vo.org/tap/user_tables to operate the persistent tables.

    As with Pat's proposal, to create a persistent table, you do an http PUT to a suitably named child of user_tables:

    $ curl -o tmp.vot https://docs.g-vo.org/upload_for_regressiontest.vot
    $ curl -H "content-type: application/x-votable+xml" -T tmp.vot \
      http://dc.g-vo.org/tap/user_tables/my_upload
    Query this table as tap_user.my_upload
    

    The actual upload at this point returns a reasonably informative plain-text string, which feels a bit ad-hoc. Better ideas are welcome, in particular after careful research of the rules for 30x responses to PUT requests.

    Trying to create tables with names that will not work as ADQL regular table identifiers will fail with a DALI-style error. Try something like:

    $ curl -H "content-type: application/x-votable+xml" -T tmp.vot
      http://dc.g-vo.org/tap/user_tables/join
    ... <INFO name="QUERY_STATUS" value="ERROR">'join' cannot be used as an
      upload table name (which must be regular ADQL identifiers, in
      particular not ADQL reserved words).</INFO> ...
    

    After a successful upload, you can query the VOTable's content as tap_user.my_upload:

    A TOPCAT screenshot with a query 'select avg("3.6mag") as blue, avg("5.8mag") as red from tap_user.my_upload' that has a few red warnings, and a result window showing values for blue and red.

    TOPCAT (which is what painted these pixels) does not find the table metadata for tap_user tables (yet), as I do not include them in the “public“ VOSI tables. This is why you see the reddish syntax complaints here.

    I happen to believe there are many good reasons for why the volatile and quickly-changing user table metadata should not be mixed up with the public VOSI tables, which can be several 10s of megabytes (in the case of VizieR). You do not want to have to re-read that (or discard caches) just because of a table upload.

    If you have the table URL of a persistent upload, however, you inspect its metadata by GET-ting the table URL:

    $ curl http://dc.g-vo.org/tap/user_tables/my_upload | xmlstarlet fo
    <vtm:table [...]>
      <name>tap_user.my_upload</name>
      <column>
        <name>"_r"</name>
        <description>Distance from center (RAJ2000=274.465528, DEJ2000=-15.903352)</description>
        <unit>arcmin</unit>
        <ucd>pos.angDistance</ucd>
        <dataType xsi:type="vs:VOTableType">float</dataType>
        <flag>nullable</flag>
      </column>
      ...
    

    – this is a response as from VOSI tables for a single table. Once you are authenticated (see below), you can also retrieve a full list of tables from user_tables itself as a VOSI tableset. Enabling that for anonymous uploads did not seem wise to me.

    When done, you can remove the persistent table, which again follows Pat's proposal:

    $ curl -X DELETE http://dc.g-vo.org/tap/user_tables/my_upload
    Dropped user table my_upload
    

    And again, the text/plain response seems somewhat ad hoc, but in this case it is somewhat harder to imagine something less awkward than in the upload case.

    If you do not delete yourself, the server will garbage-collect the upload at some point. On my server, that's after seven days. DaCHS operators can configure that grace period on their services with the [ivoa]userTableDays setting.

    Authenticated Use

    Of course, as long as you do not authenticate, anyone can drop or overwrite your uploads. That may be acceptable in some situations, in particular given that anonymous users cannot browse their uploaded tables. But obviously, all this is intended to be used by authenticated users. DaCHS at this point can only do HTTP basic authentication with locally created accounts. If you want one in Heidelberg, let me know (and otherwise push for some sort of federated VO-wide authentication, but please do not push me).

    To just play around, you can use uptest as both username and password on my service. For instance:

      $ curl -H "content-type: application/x-votable+xml" -T tmp.vot \
      --user uptest:uptest \
      http://dc.g-vo.org/tap/user_tables/privtab
    Query this table as tap_user.privtab
    

    In recent TOPCATs, you would enter the credentials once you hit the Log In/Out button in the TAP client window. Then you can query your own private copy of the uploaded table:

    A TOPCAT screenshot with a query 'select avg("3.6mag") as blue, avg("5.8mag") as red from tap_user.my_upload' that has a few red warnings, and a result window showing values for blue and red; there is now a prominent Log In/Out-button showing we are logged in.

    There is a second way to create persistent tables (that would also work for anonymous): run a query and prepend it with CREATE TABLE. For instance:

    A TOPCAT screenshot with a query 'create table tap_user.smallgaia AS SELECT * FROM gaia.dr3lite TABLESAMPLE(0.001)'. Again, TOPCAT flags the create as an error, and there is a dialog "Table contained no rows".

    The “error message” about the empty table here is to be expected; since this is a TAP query, it stands to reason that some sort of table should come back for a successful request. Sending the entire newly created table back without solicitation seems a waste of resources, and so for now I am returning a “stub” VOTable without rows.

    As an authenticated user, you can also retrieve a full tableset for what user-uploaded tables you have:

    $ curl --user uptest:uptest http://dc.g-vo.org/tap/user_tables | xmlstarlet fo
    <vtm:tableset ...>
      <schema>
        <name>tap_user</name>
        <description>A schema containing users' uploads. ...  </description>
        <table>
          <name>tap_user.privtab</name>
          <column>
            <name>"_r"</name>
            <description>Distance from center (RAJ2000=274.465528, DEJ2000=-15.903352)</description>
            <unit>arcmin</unit>
            <ucd>pos.angDistance</ucd>
            <dataType xsi:type="vs:VOTableType">float</dataType>
            <flag>nullable</flag>
          </column>
          ...
        </table>
        <table>
          <name>tap_user.my_upload</name>
          <column>
            <name>"_r"</name>
            <description>Distance from center (RAJ2000=274.465528, DEJ2000=-15.903352)</description>
            <unit>arcmin</unit>
            <ucd>pos.angDistance</ucd>
            <dataType xsi:type="vs:VOTableType">float</dataType>
            <flag>nullable</flag>
          </column>
          ...
        </table>
      </schema>
    </vtm:tableset>
    

    Open Questions

    Apart from the obvious question whether any of this will gain community traction, there are a few obvious open points:

    1. Indexing. For tables of non-trivial sizes, one would like to give users an interface to say something like “create an index over ra and dec interpreted as spherical coordinates and cluster the table according to it”. Because this kind of thing can change runtimes by many orders of magnitude, enabling it is not just some optional embellishment.

      On the other hand, what I just wrote already suggests that even expressing the users' requests in a sufficiently flexible cross-platform way is going to be hard. Also, indexing can be a fairly slow operation, which means it will probably need some sort of UWS interface.

    2. Other people's tables. It is conceivable that people might want to share their persistent tables with other users. If we want to enable that, one would need some interface on which to define who should be able to read (write?) what table, some other interface on which users can find what tables have been shared with them, and finally some way to let query writers reference these tables (tap_user.<username>.<tablename> seems tricky since with federated auth, user names may be just about anything).

      Given all this, for now I doubt that this is a use case sufficiently important to make all the tough nuts delay a first version of user uploads.

    3. Deferring destruction. Right now, you can delete your table early, but you cannot tell my server that you would like to keep it for longer. I suppose POST-ing to a destruction child of the table resource in UWS style would be straightforward enough. But I'd rather wait whether the other lacunae require a completely different pattern before I will touch this; for now, I don't believe many persistent tables will remain in use beyond a few hours after their creation.

    4. Scaling. Right now, I am not streaming the upload, and several other implementation details limit the size of realistic user tables. Making things more robust (and perhaps scalable) hence will certainly be an issue. Until then I hope that the sort of table that worked for in-request uploads will be fine for persistent uploads, too.

    Implemented in DaCHS

    If you run a DaCHS-based data centre, you can let your users play with the stuff I have shown here already. Just upgrade to the 2.10.2 beta (you will need to enable the beta repo for that to happen) and then type the magic words:

    dachs imp //tap_user
    

    It is my intention that users cannot create tables in your DaCHS database server unless you say these words. And once you say dachs drop --system //tap_user, you are safe from their huge tables again. I would consider any other behaviour a bug – of which there are probably still quite a few. Which is why I am particularly grateful to all DaCHS operators that try persistent uploads now.

    [1]As already said in the notebook, if http bothers you, you can write https, too; but then it's much harder to watch what's going on using ngrep or friends.
  • Learn To Use The VO

    Thumbnails of the first 60 pages of the lecture notes, grayish goo with occasional colour spots thrown in.

    The first 60 pages of the lecture notes as they currently are. I give you a modern textbook would probably look a bit more colorful from this distance, but perhaps this will still do.

    About ten years ago, I had planned to write something I tentatively called VadeVOcum: A guide for people wanting to use the Virtual Observatory somewhat more creatively than just following and slightly adapting tutorials and use cases. If you will, I had planned to write a textbook on the VO.

    For all the usual reasons, that project never went far. Meanwhile, however, GAVO's courses on ADQL and on pyVO grew and matured. When, some time in 2021, I was asked whether I could give a semester-long course “on the VO”, I figured that would be a good opportunity to finally make the pyVO course publishable and complement the two short courses with enough framing that some coherent story would emerge, close enough to the VO textbook I had in mind in about 2012.

    Teaching Virtual Observatory Matters

    The result was a course I taught at Universität Heidelberg in the past summer semester together with Hendrik Heinl and Joachim Wambsganss. I have now published the lecture notes, which I hope are textbooky enough that they work for self-study, too. But of course I would be honoured if the material were used as a basis of similar courses in other places. To make this simpler, the sources are available on Codeberg without relevant legal restrictions (i.e., under CC0).

    The course currently comprises thirteen “lectures”. These are designed so I can present them within something like 90 minutes, leaving a bit of space for questions, contingencies, and the side tracks. You can build the slides for each of these lectures separately (see the .pres files in the source repository), which makes the PDF to work while teaching less cumbersome. In addition to that main trail, there are seven “side tracks”, which cover more fundamental or more general topics.

    In practice, I sprinkled in the side tracks when I had some time left. For instance, I showed the VOTable side track at the ends of the ADQL 2 and ADQL 3 lectures; but that really had no didactic reason, it was just about filling time. It seemed the students did not mind the topic switches to much. Still, I wonder if I should not bring at least some of the side tracks, like those on UCDs, identifiers, and vocabularies, into the main trail, as it would be unfortunate if their content fell through the cracks.

    Here is a commented table of contents:

    • Introduction: What is the VO and why should you care? (including a first demo)
    • Simple Protocols and their clients (which is about SIAP, SSAP, and SCS, as well as about TOPCAT and Aladin)
    • TAP and ADQL (that's typically three lectures going from the first SELECT to complex joins involving subqueries)
    • Interlude: HEALPix, MOC, HiPS (this would probably be where a few of the other side tracks might land, too)
    • pyVO Basics (using XService objects and a bit of SAMP, mainly along an image discovery task)
    • pyVO and TAP (which is developed around a multi-catalogue SED building case)
    • pyVO and the Registry (which, in contrast to the rest of the course, is employing Jupyter notebooks because much of the Registry API makes sense mainly in interactive use)
    • Datalink (giving a few pyVO examples for doing interesting things with the protocol)
    • Higher SAMP Magic (also introducing a bit of object oriented programming, this is mainly about tool building)
    • At the Limit: VO-Wide TAP Queries (cross-server TAP queries with query building, feature sensing and all that jazz; I admit this is fairly scary and, well, at the limit of what you'd want to show publicly)
    • Odds and Ends (other pyVO topics that don't warrant a full section)
    • Side Track: Terminology (client, server, dataset, data collection, oh my; I had expected this to grow more than it actually did)
    • Side Track: Architecture (a deeper look at why we bother with standards)
    • Side Track: Standards (a very brief overview of what standards the IVOA has produced, with a view of guiding users away from the ones they should not bother with – and perhaps towards those they may want to read after all)
    • Side Track: UCDs (including hints on how to figure out which would denote a concept one is interested in)
    • Side Track: Vocabularies (I had some doubts whether that is too much detail, but while updating the course I realised that vocabularies are now really user-visible in several places)
    • Side Track: VOTable (with the intention of giving people enough confidence to perform emergency surgery on VOTables)
    • Side Track: IVOA Identifiers (trying to explain the various ivo:// URIs users might see).

    Pitfalls: Technical, Intellectual, and Spiritual

    The course was accompanied by lab work, again 90 minutes a week. There are a few dozen exercises embedded in the course, and in the lab sessions we worked on some suitable subset of those. With the particular students I had and the lack of grading pressure, the fact that solutions for most of the exercises come with the lecture notes did not turn out to be a problem.

    The plan was that the students would explain their solutions and, more importantly, the places they got stuck in to their peers. This worked reasonably well in the ADQL part, somewhat less for the side tracks, and regrettably a lot less well in the pyVO part of the course. I cannot say I have clear lessons to be learned from that yet.

    A piece of trouble for the student-generated parts I had not expected was that the projector only interoperated with rather few of the machines the students brought. Coupling computers and projectors was occasionally difficult even in the age of universal VGA. These days, even in the unlikely event one has an adapter for the connectors on the students' computers, there is no telling what part of a computer screen will end up on the wall, which distortions and artefacts will be present and how much the whole thing will flicker.

    Oh, and better forget about trying to fix things by lowering the resolution or the refresh rate or whatever: I have not had one instance during the course in which any plausible action on the side of the computer improved the projected image. Welcome to the world of digital video signals. Next time around, I think I will bring a demonstration computer and figure out a way in which the students can quickly transfer their work there.

    Talking about unexpected technical hurdles: I am employing PDF-attached source code quite extensively in the course, and it turned out that quite a few PDF clients in use no longer do something reasonable with that. With pdf.js, I see why that would be, and it's one extra reason to want to avoid it. But even desktop readers behaved erratically, including some Windows PDF reader that had the .py extension on some sort of blacklist and refused to store the attached files on grounds that they may “damage the computer”. Ah well. I was tempted to have a side track on version control with git when writing the course. This experience is probably an encouragement to follow through with that and at least for the pyVO part to tell students to pull the files out of a checkout of the course's source code.

    Against the outline in the lecture as given, I have now promoted the former HEALPix side track to an interlude session, going between ADQL and pyVO. It logically fits there, and it was rather popular with the students. I have also moved the SAMP magic lecture to a later spot in the course; while I am still convinced it is a cool use case, and giving students a chance to get to like classes is worthwhile, too, it seems to be too much tool building to have much appeal to the average participant.

    Expectably, when doing live VO work I regularly had interesting embarrassments. For instance, in the pyvo-tap lecture, where we do something like primitive SEDs from three catalogues (SDSS, 2MASS and WISE), the optical part of the SEDs was suddenly gone in the lecture and I really wondered what I had broken. After poking at things for longer than I should have, I eventually promised to debug after class and report next time, only to notice right after the lecture that I had, to make some now-forgotten point, changed the search position – and had simply left the SDSS footprint.

    But I believe that was actually a good thing, because showing actual errors (it does not hurt if they are inadvertent) and at least brief attempts to understand them (and, possibly later, explain how one actually understood them) is a valuable part of any sort of (IT-related) education. Far too few people routinely attempt to understand what a computer is trying to tell them when it shows a message – at their peril.

    Reruns, House Calls, TV Shows

    Of course, there is a lot more one could say about the VO, even when mainly addressing users (as opposed to adopters). An obvious addition will be a lecture on the global dataset discovery API I have recently discussed here, and I plan to write it when the corresponding code will be in a pyVO release. I am also tempted to have something on stilts, perhaps in a side track. For instance, with a view to students going on to do tool development, in particular stilts' validators would deserve a few words.

    That said, and although I still did quite a bit of editing based on my experiences while teaching, I believe the material is by and large sound and up-to-date now. As I said: everyone is welcome to the material for tinkering and adoption. Hendrik and I are also open to give standalone courses on ADQL (about a day) or pyVO (two to three days) at astronomical institutes in Germany or elsewhere in not-too remote Europe as long as you house (one of) us. The complete course could be a 10-days block, but I don't think I can be booked with that[1].

    Another option would be a remote-teaching version of the course. Hendrik and I have discussed whether we have the inclination and the resources to make that happen, and if you believe something like that might fit into your curriculum, please also drop us a note.

    And of course we welcome all sorts of bug reports and pull requests on codeberg, first and foremost from people using the material to spread the VO gospel.

    [1]Well… let me hedge that I don't think I'd find a no in myself if the course took place on the Canary Islands…
  • Watch Sphinx Doctests

    No astronomy at all here; please move on if tooling for improving tooling bores you.

    While giving a lecture on pyVO, I am churning out quite a few pull requests against pyVO at the moment. I am also normally also fairly religious about running unit tests before doing a commit. But then PyVO unit tests became really, really slow a while ago when pytesting of the examples in the documentation was turned on, and so I started relying on the github continuous integration, which feels fairly wasteful – and also makes all kinds of minor idiocies public that I would have caught locally with a test suite that finishes within a minute or so.

    Regrettably, tooling for inspecting how doctests with sphinx and pytest run is not really great: All the code from one documentation file translates into a single test, and when that runs for five minutes, it's anyone's guess where the time is spent. After a bit of poking and asking around, it seemed to me that there indeed is no “doctest profiler” (if you will), at least not for pytest-executable doctests embedded in sphinx-processable ReStructuredText.

    Well, I thought, let's write a quick one. Originally, I had wanted to use the docutils parser for robustness, but once I tried to pull in the sphinx extensions and got lost in their modules I decided a simple, RE-based parser has to be enough.

    And here it is, my my quick-and-dirty doctest profiler: watch-doctests.py. Just put it into your path, make it executable, and you can do something like this:

    pyvo/docs/dal > watch-doctests.py index.rst | head -30
    ---0.00---------------
    
    import pyvo as vo
    ---0.94---------------
    
    service = vo.dal.SIAService("http://dc.zah.uni-heidelberg.de/lswscans/res/positions/siap/siap.xml")
    ---0.94---------------
    
    print(service.description)
    Scans of plates kept at Landessternwarte Heidelberg-Königstuhl. They
    were obtained at location, at the German-Spanish Astronomical Center
    (Calar Alto Observatory), Spain, and at La Silla, Chile. The plates
    cover a time span between 1880 and 1999.
    
    Specifically, HDAP is essentially complete for the plates taken with
    the Bruce telescope, the Walz reflector, and Wolf's Doppelastrograph
    at both the original location in Heidelberg and its later home on
    Königstuhl.
    ---1.02---------------
    
    import pyvo as vo
    ---1.02---------------
    
    from astropy.coordinates import SkyCoord
    ---1.02---------------
    
    from astropy.units import Quantity
    

    – so, you pass in the ReStructuredText with the embedded sphinx/pytest doctests, and then the thing extracts every line to be executed in the doctests (it ignores the outputs, so it will not actually check any assertions), prints the runtime so far in a separator and then runs the code through Python as usual: note that no automatic repr() of any non-None results – that the REPL does – happens. This is for profiling, not for test development.

    The quick hack helped me speed up the dal and registry doctests by sizeable factors, for instance because I am now avoiding downloads of large datasets, and I am using faster queries where I can.

    So, that's nice. But unless someone asks, I will distribute the code here only and in this ad-hoc fashion (probably with a link in the pyVO hackers' docs). I still believe there must be something a lot less hacky that does about the same thing somewhere out there…

  • DASCH is now in the VO

    Black dots on a white-ish background.  In the middle, some diffuse greyish stuff around a relatively large black dot.

    This frame would show comet 2P/Encke during its proximity to Earth in 1941 – if it went deep enough. But never mind practicalities: If you want to learn about matching ephemeris against the DASCH plate collection (or, really, any sort of obscore-like table), read on.

    For about a century – that is, into the 1980s –, being an observational astronomer meant taking photographic plates and doing tricks with them (unless you were a radio astronomer or one of the very few astronomers peeking beyond radio and optical in those days, of course). This actually is somewhat fortunate for archivists, because unlike many of the early CCD observations that by now are lost with our ability to read the tapes they were stored on, the plates are still there.

    Why Bother?

    However, to make them usable, the plates need to be digitised. In the GAVO data centre, we keep the results of several scan campaigns large and small, such as HDAP, the various data collections joined in the historical photographic plate image archive HPPA, or the delightfully quirky Münster Flare Plates.

    I personally care a lot about these data collections. This is partly because they are indispensible for understanding the history of astronomy. But more importantly, they are the next best thing we have to a time machine; if we have a way of knowing how the sky looked like seventy years ago, it is these plate collections. Having such a time machine is important for all kinds of scientific efforts, including figuring out whether there are aliens (i.e., 2016ApJ...822L..34S) on Tabby's Star.

    Somewhat to my chagrin, the cited paper 2016ApJ...822L..34S did not use the VO to obtain the plate images but went straight to DASCH's web interface. DASCH, in case you have not heard of it before, is probably the most ambitious project concerned with plate digitisation at the moment – or perhaps: “was”, because they just finished scanning the core part of Harvard's plate collections, which was their primary goal.

    I can understand why Bradley Schaefer, the paper's author, did not bother with a VO search In 2016. For starters, working with halfway homogeneous data from instruments you are somewhat familiar saves a substantial amount of work and thought, in particular if you are, in addition, up against the usual lack of machine-readable metadata. Also, at that time DASCH probably had about as many digitised plates as all the VO's contemporary plate collections taken together.

    DASCH: The Harvard Plates

    Given such stats, I have always wanted to have at least the metadata from DASCH's plates in the VO. Thanks to a recent update to DASCH's publication system, this is now a reality. Since 2024-04-29, I am publishing the metadata of the DASCH plates via Obscore and and SIAP2.

    Followup (2024-05-03)

    This is now DASCH news, and one of my two main contacts on the DASCH side, Peter Williams, has written an insightful post on this, too. Let me use this opportunity to thank him for the delightful cooperation, and extend these thanks to Ben Sabath, who is primarily responsible for the update to the DASCH publication system I mentioned above.

    Matching plates are returned as datalink documents, pointing to a preview, photos of the plate and its jacket, and links to the science data, once downsampled by a factor of 16, once in the original size (example). For now, #this points to the downsampled version, as Amazon charges DASCH about three cents per full-scale plate at the moment, and that can quickly add up by accident (there's nothing wrong with consciously downloading full-scale FITS-es if you need them, of course).

    This is a bit fishy in that the size of the image in the obscore/SIAP2 fields s_xel1 and s_xel2 refers to the unscaled image, and thus I should be returning the full-scale image as datalink #this. I hope I will not cause much confusion with this design.

    In case you look at the links in the datalink documents, let me include a disclaimer: Although they point into the GAVO data centre, the data is served courtesy of the DASCH project. The links only go to us because we need to sign links for you. I mention this because you can save the datalink documents and the links within them; the URLs you are redirected to from there, however, will expire fast. Just do not look at them.

    Plates in Global Discovery

    So – what can you do with DASCH in the VO that you could not do before?

    Most importantly, you will discover DASCH in registry interfaces and its datasets in global queries (in particular the global dataset queries I have discussed a few weeks ago). For instance, DASCH is now in Aladin's discovery tree:

    A screen shot with many selected points, highlighted in green, on the right side.  On the left side, an tree display with many branches folded in.  On a folded-out branch, there is “DASCH SIAP2“ highlighted.  On the right side, there is a large rectangle overplotted in red.

    You can now find DASCH in Aladin and do the usual “in view“ searches. However, currently this yields many matches that are, in practical terms, spurious, as they come from extremely wide-angle instruments. The red rectangle is the footprint of one of these images; note that the view here is a full two pi sky. We will probably do something about this “noise“.

    The addition of DASCH to the VO has a strong effect in some use cases. For instance, at the end of the GAVO plates tutorial, we do an all-VO obscore query that, at the time of the last update of the tutorial in 2019, yielded 4067 datasets (of course, including modern and/or non-optical observations) potentially showing some strongly lensed quasar. With DASCH – and, admittedly, a few more collections that came into the VO since 2019 –, that number is now 10'489; the range of observation dates grew from MJD 12550…52000 to MJD 9800…58600, with the mean decreasing from 51'909 to 30'603. That the mean observation date moves that much back in time is a certain sign that a major part of the expansion is due to DASCH (well, and certainly to APPLAUSE, too).

    Followup (2024-05-03)

    As discussed in my DASCH update, I have taken out the large-coverage plates from my obscore table, which changes the stats (but not the conclusions) quite a bit. They is now 10'098 plates and mean observation date 36'396

    TAP, Uploads, and pyVO on DASCH

    But this is not just about bringing astronomical heritage to the VO. It is also about exposing DASCH through the powerful ADQL/TAP interface. As an example of how this may be useful, consider the comet P2/Encke, which, according to JPL's Small-Body Database was relatively close to Earth (about half an AU) in May 1941. It would have had about 14.5 mag at that point and hence was safely within reach of several of the instruments archived in DASCH. Perhaps we can find serendipitous or even targeted observations of the comet in the collection?

    The plan to find that out is: compute an ephemeris (we are lazy and use an external service, Miriade ephemcc) and then for each day see whether there are DASCH observations in the vicinity of the sky location obtained in this way.

    As usual, it's never that easy because the call to the ephemeris webservice (paste the link into TOPCAT to have a look) returns cursed sexagesimal coordinates. We need to fix them before doing anything serious with the table, and while we are at it, we also repair the date, which is simpler to consume if it is MJD to begin with. Getting the ephemeris thus takes quite a few lines:

    from astropy import table
    from astropy import units as u
    from astropy.coordinates import SkyCoord
    from astropy.time import Time
    
    ephem = table.Table.read(
      "https://vo.imcce.fr/webservices/miriade/ephemcc.php?-from=vespa"
      "&-name=c:p/encke&-ep=1941-04-01&-nbd=90&-step=1d&-observer=500"
      &-mime=votable")
    
    parsed = SkyCoord(ephem["ra"], ephem["dec"], unit=(u.hourangle, u.deg))
    ephem["ra"] = parsed.ra.degree
    ephem["dec"] = parsed.dec.degree
    
    parsed = Time(ephem["epoch"])
    ephem["epoch"] = parsed.mjd
    

    Compared to that, the actual matching against DASCH is almost trivial if you are somewhat familiar with crossmatching in ADQL and the Obscore schema:

    svc = pyvo.dal.TAPService("http://dc.g-vo.org/tap")
    res = svc.run_sync("""
        SELECT *
        FROM
            dasch.plates
            JOIN tap_upload.orbit
            ON (1=CONTAINS(POINT(ra, dec), s_region))
        WHERE
            t_min<epoch
            AND t_max>epoch""",
        uploads={"orbit": ephem})
    

    Followup (2024-05-03)

    You would probably query the dasch.narrow_plates table in actual operations; querying dasch.plates is probably more for people interested in the history of astronomy or DASCH itself.

    Inspect the query for a moment: This is a normal upload join, except we are constructing an ADQL POINT on the fly to be able to see whether we are in the spatial region covered by a DASCH dataset (given in obscore's s_region column). We could have put the temporal condition into the join's ON; but I think the intention is somewhat clearer with the WHERE constraint, and the database engine will probably go through identical motions for both queries – the beauty of having a query planner in the loop is that you do not need to think about such details most of the time.

    Actually, in this case there is one last complication: As said above, we have put a datalink service between you and the downloads to discourage accidental large downloads. We hence use pyVO's (suboptimally documented) datalink interface (iter_datalinks):

    with pyvo.samp.connection() as conn:
        for dl in res.iter_datalinks():
            link = next(dl.bysemantics("#preview-image"))
            pyvo.samp.send_image_to(
                conn,
                link.access_url,
                client_name="Aladin")
    

    Among the artefacts available we pick the scaled jpegs in this fragment (#preview-image), since these are almost free even on the Amazon cloud. Change that #preview-image to #this in the to get scaled calibrated FITS-es, which are still fairly small. This would, for instance, let you overplot the ephemeris in Aladin, which you cannot do with the jpegs as they lack astrometric calibration (for now). But even with #preview-image, we can use Aladin as a glorified image viewer by SAMP-sending the images there, which is why we do the minor magic with functions from pyvo.samp.

    If you want to try this yourself or mangle the program to do something else that requires querying against a reasonable number positions in time and space, just get encke.py and hack away. Make sure to start Aladin before running the program so it has something to send the images to.

    Disclaimers

    This is a contrived example, and it is likely that this particular use case is astronomically wrong in several ways. Let me enumerate a few things that would need looking into before this approaches proper science:

    • We compute the ephemeris for the center of the Earth. At half an AU distance, the resulting parallax will not shift the position enough to hide a plate we should know about, but at least for anything closer, you should try to do a bit better; admittedly, for a resource like DASCH – that contains plates from observatories all over the place – you will have to compromise.
    • The ephemeris is probably wrong; comet's orbits change over time, and I have no idea if the ephemeris service actually uses 2P/Encke's 1941 orbit to compute the positions.
    • The coordinate metadata may be wrong. Ephemcc's documentation says something that sounds a lot as if they were sometimes returning RA and Dec for the equator of the time rather than for J2000 (i.e., ICRS for all intents and purposes), but of course our obscore coverages are for the ICRS. Regrettably, the VOTable returned by the service does not contain a COOSYS element yet, and so there is no easy way to tell.
    • If you look at the table with DASCH matches, you will see they all were observed with an extremely wide-angle instrument sporting an aperture of a mere three inches. Even at the whopping exposure times (two hours), there is probably no way you would see a diffuse object of 14th mag on a plate with a 1940s-era photographic emulsion with that kind of optics (well: feel free to prove me wrong).
    • It would of course be a huge waste of bandwidth to pull the entire plates if we already had a good idea of where we would expect the comet (i.e., had a reliable ephemeris). Hence, a cutout service that would let you retrieve more or less exactly the pixels you would like to use for your research and not the cruft around it would be a nifty supplement. It's in the works, and I'd say you can almost hold your breath. The cutout will simply appear as a SODA service in the datalink documents. See 2020ASPC..522..295D for how you would operate such a service.
  • Global Dataset Discovery in PyVO

    A Tkinter user interface with inputs for Space, Spectrum, and Time, a checkbox marked "inclusive", and buttons Run, Stop, Broadcast, Save, and Quit.

    Admittedly somewhat old-style: As part of teaching global dataset discovery to pyVO, I have also come up with a Tkinter GUI for it. See A UI for more on this.

    One of the more exciting promises of the Virtual Observatory was global dataset discovery: You say “Give me all spectra of object X that there are“, and the computer relates that request to all the services that might have applicable data. Once the results come in, they are merged into some uniformly browsable form.

    In the early VO, there were a few applications that let you do this; I fondly remember VODesktop. As the VO grew and diversified, however, this became harder and harder, partly because there were more and more services, partly because there were more protocols through which to publish data. Thus, for all I can see, there is, at this point, no software that can actually query all services plausibly serving, say, images or spectra in the VO.

    I have to say that writing such a thing is not for the faint-hearted, either. I probably wouldn't have tackled it myself unless the pyVO maintainers had made it an effective precondition for cleaning up the pyVO Servicetype constraint.

    But they did, and hence as a model I finally wrote some code to do all-VO image searches using all of SIA1, SIA2, and obscore, i.e., the two major versions of the Simple Image Access Protocol plus Obscore tables published through TAP services. I actually have already reported in Tucson on some preparatory work I did last summer and named a few problems:

    • There are too many services to query on a regular basis, but filtering them would require them to declare their coverage; far too many still don't.
    • With the current way of registering obscore tables, there is no way to know their coverage.
    • One dataset may be availble through up to three protocols on a single host.
    • SIA1 does not even let you constrain time and spectrum.

    Some of these problems I can work around, others I can try to fix. Read on to find out how I fared so far.

    The pyVO API

    Currently, the development happens in pyVO PR #470. While it is still a PR, let me point you to temporary pyVO docs on the proposed pyvo.discover module – of course, all of this is for review and probably not in the shape it will remain in[1].

    Followup (2024-11-28)

    With the recent release of pyVO 1.6, what is described here is actually available in the release (or by checking out the main branch of the repository).

    To quote from there, the basic usage would be something like:

    from pyvo import discover
    from astropy import units as u
    from astropy import time
    
    datasets, log = discover.images_globally(
      space=(339.49, 3.1, 0.1),
      spectrum=650*u.nm,
      time=(time.Time('1995-01-01'), time.Time('1995-12-31')))
    

    At this point, only a cone is supported as a space constraint, and only a single point in spectrum. It would certainly be desirable to be more flexible with the space constraint, but given the capabilities of the various protocols, that is hard to do. Actually, even with the plain cone Obscore (i.e., ironically, the most powerful of the discovery protocols covered here) currently results in an implementation that makes me unhappy: ugly, slow, and wrong. This is requires a longer discussion; see Appendix: Optionality Considered Harmful.

    datasets at this point is a list of, conceputally, Obscore records. Technically, the list contains instances of a custom class ImageFound, which have attributes named after the Obscore columns. In case you have doubts about the Semantics of any column, the Obscore specification is there to help. And yes, you can argue we should create a single astropy table from that list. You are probably right.

    PyVO adds an extra column over the mandatory obscore set, origin_service. This contains the IVOA identifier (IVOID) of the service at which the dataset was found. You have probably seen IVOIDs before: they are URIs with a scheme of ivo:. What you may not know: these things actually resolve, specifically to registry resource records. You can do this resolution in a web browser: Just prepend https://dc.g-vo.org/I/ to an IVOID and paste the result into the address bar. For instance, my Obscore table has the IVOID ivo://org.gavo.dc/__system__/obscore/obscore; the link below the IVOID leads you to an information page, which happens to be the resource's Registry record formatted with a bit of XSLT. A somewhat more readable but less informative rendering is available when you prepend https://dc.g-vo.org/LP/ (“landing page”).

    The second value returned from discover.images_globally is a list of strings with information on how the global discovery progressed. For now, this is not intended to be machine-readable. Humans can figure out which resources were skipped because other services already cover their data, which services yielded how many records, and which services failed, for instance:

    Skipping ivo://org.gavo.dc/lswscans/res/positions/siap because it is served by ivo://org.gavo.dc/__system__/obscore/obscore
    Skipping ivo://org.gavo.dc/rosat/q/im because it is served by ivo://org.gavo.dc/__system__/obscore/obscore
    Obscore GAVO Data Center Obscore Table: 2 records
    SIA2 The VO @ ASTRON SIAP Version 2 Service: 0 records
    SIA2 ivo://au.csiro/casda/sia2 skipped: ReadTimeout: HTTPSConnectionPool(host=&apos;casda.csiro.au&apos;, port=443): Read timed out. (read timeout=20)
    SIA2 CADC Image Search (SIA): 0 records
    SIA2 European HST Archive SIAP service: 0 records
    ...
    

    (On the skipping, see Relationships below). I consider this crucial provenance, as that lets you assess later what you may have missed. When you save the results, be sure to save these, too.

    A feature that will presumably (see Inclusivity for the reasons for this expectation) be important at least for a few years is that you can pass the result of a Registry query, and pyVO will try to find services suitable for image discovery on that set of resources.

    A relatively straightforward use case for that is global obscore discovery. This would look like this:

    from pyvo import discover
    from pyvo import registry
    from astropy import units as u
    from astropy import time
    
    def say(discoverer, s):
            print(s)
    
    datasets, log = discover.images_globally(
      space=(274.6880, -13.7920, 1),
      time=(time.Time('1995-01-01'), time.Time('1995-12-31')),
      services=registry.search(registry.Datamodel("obscore")),
      watcher=say)
    

    The watcher thing lets you, well, watch the progress of the discovery; it receives an instance of the discoverer -- this is so you can abort a discoverer's activities from within some UI -- and the human-readable string to display or process in some other way.

    A UI

    To get an idea whether this API might one day work for the average astronomer, I have written a Tkinter-based GUI to global image discovery as it is now: tkdiscover (only available from github at this point). This is what a session with it might look like:

    Lots of TOPCAT windows with various graphs and tables, an x-ray image of the sky with overplotted points, and a play gray window offering the specification of space, spectrum, and time constraints.

    The actual UI is in the top right: A plain window in which you can configure a global discovery query by straightfoward serialisations of discover.images_globally's arguments:

    • Space (currently, a cone in RA, Dec, and search radius, separated by whitespace of commas)
    • Spectrum (currently, a single point as a wavelength in metres)
    • Time (currently, either a single point in time – which probably is rarely useful – or an interval, to be entered as civil DALI dates
    • Inclusivity.

    When you run this, this basically calls discover.images_globally and lets you know how it is progressing. You can click Broadcast (which sends the current result to all VOTable clients on the SAMP bus) or Save at any time and inspect how discovery is progressing. I predict you will want to do that, because querying dozens of services will take time.

    There is also a Stop button that aborts the dataset search (you will still have the records already found). Note that the Stop button will not interrupt running network operations, because the network library underneath pyVO, requests, is not designed for being interrupted. Hence, be patient when you hit stop; this may take as long as the configured timeout (currently is 20 seconds) if the service hangs or has to do a lot of work. You can see that tkdiscover has noticed your stop request because the service counter will show a leading zero.

    Service counter? Oh, that's what is at the bottom right of the window. Once service discovery is done, that contains three numbers: The number of services to query, the number of services queried already, and the number of services that failed.

    The table contains the obscore records described above, and the log lines are in the discovery_log INFO. I will give you that this is extremely unreadable in particular in TOPCAT, which normalises the line separators to plain whitespace. Perhaps some other representation of these log lines would be preferable: A PARAM with a char[][] (but VOTable still is terrible with arrays of variable-length strings)? Or a separate table with char[*] entries?

    Inclusivity

    I have promised above I'd explain the “Inclusive” part in both the pyVO API and the Tk UI. Well, this is a bit of a sad story.

    All-VO-queries take time. Thus, in pyVO we try to only query services that we expect serve data of interest. How do we arrive at expectations like that? Well, quite a few records in the Registry by now declare their coverage in space and time (cf. my 2018 post for details).

    The trouble is: Most still don't. The checkmark at inclusive decides whether or not to query these “undecidable” services. Which makes a huge difference in runtime and effort. With the pre-configured constraints in the current prototype (X-Ray images a degree around 274.6880, -13.7920 from the year 1995), we currently discover three services (of which only one actually needs to be queried) when inclusive is off. When it is on, pyVO will query a whopping 323 services (today).

    The inclusivity crisis is particularly bad with Obscore tables because of their broken registration pattern; I can say that so bluntly because I am the author of the standard at fault, TAPRegExt. I am preparing a note with a longer explanation and proposals for fixing matters – <cough> follow me on github –, but in all brevity: Obscore data is discovered using something like a flag on TAP services. That is bad because the TAP services usually have entriely different metadata from their Obscore table; think, in particular, of the physical coverage that is relevant here.

    It will be quite a bit of effort to get the data providers to do the Registry work required to improve this situation. Until that is done, you will miss Obscore tables when you don't check inclusive (or override automatic resource selection as above) – and if you do check inclusive, your discovery runs will take something like a quarter of an hour.

    Relationships

    In general, the sheer number of services to query is the Achilles' heel in the whole plan. There is nothing wrong with having a machine query 20 services, but querying 200 is starting to become an effort.

    With multi-data collection services like Obscore (or collective SIA2 services), getting down to a few dozen services globally for a well-constrained search is actually not unrealistic; once all resources properly declare their coverage, it is not very likely that more than 20 institutions worldwide will have data in a credibly small region of space, time, and spectrum. If all these run collective services and properly declare the datasets to be served by them, that's our 20-services global query right there.

    However, pyVO has to know when data contained in a resource is actually queriable by a collective service. Fortunately, this problem has already been addressed in the 2019 endorsed note on Discovering Data Collections Within Services: Basically, the individual resource declares an IsServedBy relationship to the collective service. PyVO global discovery already looks at these. That is how it could figure out these two things in the sample log given above:

    Skipping ivo://org.gavo.dc/lswscans/res/positions/siap because it is served by ivo://org.gavo.dc/__system__/obscore/obscore
    Skipping ivo://org.gavo.dc/rosat/q/im because it is served by ivo://org.gavo.dc/__system__/obscore/obscore
    

    But of course the individual services have to declare these relationships. Surprisingly many already do, as you can observe yourself when you run:

    select ivoid, related_id from
    rr.relationship
    natural join rr.capability
    where
    standard_id like 'ivo://ivoa.net/std/sia%'
    and relationship_type='isservedby'
    

    on your favourite RegTAP endpoint (if you have no preferences, use mine: http://dc.g-vo.org/tap). If you have collective services and run individual SIA services, too, please run that query, see if you are in there, and if not, please declare the necessary relationships. In case you are unsure as to what to do, feel free to contact me.

    Future Directions

    At this point, this is a rather rough prototype that needs a lot of fleshing out. I am posting this in part to invite the more adventurous to try (and break) global discovery and develop further ideas.

    Some extensions I am already envisaging include:

    • Write a similar module for spectra based on SSAP and Obscore. That would then probably also work for time series and similar 1D data.

    • Do all the Registry work I was just talking about.

    • Allow interval-valued spectral constraints. That's pretty straightforward; if you are looking for some place to contribute code, this is what I'd point you to.

    • Track overflow conditions. That should also be simple, probably just a matter of perusing the pyVO docs or source code and then conditionally produce a log entry.

    • Make an obscore s_region out of the SIA1 WCS information. This should also be easy – perhaps someone already has code for that that's tested around the poles and across the stitching line? Contributions are welcome.

    • Allow more complex geometries to define the spatial region of interest. To keep SIA1 viable in that scenario it would be conceivable to compute a bounding box for SIA1 POS/SIZE and do “exact” matching locally on the coarser SIA1 result.

    • Enable multi-position or multi-interval constraints. This pretty certainly would exclude SIA1, and, realistically, I'd probably only enable Obscore services with TAP uploads with this. With those constraints, it would be rather straightforward.

    • Add SODA support: It would be cool if my ImageFound had a way to say “retrieve data for my RoI only”. This would use SODA and datalink to do server-side cutouts where available and do the cut-out locally otherwise. If this sounds like rocket science: No, the standards for that are actually in place, and pyVO also has the necessary support code. But still the plumbing is somewhat tricky, partly also because pyVO's datalink API still is a bit clunky.

    • Going async? Right now, we civilly query one service after the other, waiting for each result before proceeding to the next service. This is rather in line with how pyVO is written so far.

      However, on the network side for many years asynchronous programming has been a very successful paradigm – for instance, our DaCHS package has been based on an async framework from the start, and Python itself has growing in-language support for async, too.

      Async allows you to you fire off a network request and forget about it until the results come back (yes, it's the principle of async TAP, too). That would let people run many queries in parallel, which in turn would result in dramatically reduced waiting times, while we can rather easily ensure that a single client will not overflow any server. Still, it would be handing a fairly powerful tool into possibly unexperienced hands… Well: for now there is no need to decide on this, as pyVO would need rather substantial upgrades to support async.

    Appendix: Optionality Considered Harmful

    The trouble with obscore and cones is a good illustration of the traps of attempting to fix problems by adding optional features. I currently translate the cone constraint on Obscore using:

    "(distance(s_ra, s_dec, {}, {}) < {}".format(
      self.center[0], self.center[1], self.radius)
    +" or 1=intersects(circle({}, {}, {}), s_region))".format(
      self.center[0], self.center[1], self.radius))
    

    which is all of ugly, presumably slow, and wrong.

    To appreciate what is going on, you need to know that Obscore has two ways to define the spatial coverage of an observation. You can give its “center” (s_ra, s_dec) and something like a rough radius (s_fov), or you can give some sort of geometry (e.g., a polygon: s_region). When the standard was written, the authors wanted to enable Obscore services even on databases that do not know about spherical geometry, and hence s_region is considered rather optional. In consequence, it is missing in many services. And even the s_ra, s_dec, s_fov combo is not mandatory non-null, so you are perfectly entitled to only give s_region.

    That is why there are the two conditions or-ed together (ugly) in the code fragment above. 1=intersects(circle(.), s_region) is the correct part; this is basically how the cone is interpreted in SIA1, too. But because s_region may be NULL even when s_ra and s_dec are given, we also need to do a test based on the center position and the field of view. That rather likely makes things slower, possibly quite a bit.

    Even worse, the distance-based condition actually is wrong. What I really ought to take into account is s_fov and then do something like distance(.) < {self.radius}+s_fov, that is, the dataset position need only be closer than the cone radius plus the dataset's FoV (“intersects”). But that would again produce a lot of false negatives because s_fov may be NULL, too, and often is, after which the whole condition would be false.

    On top of that, it is virtually impossible that such an expression would be evaluated using an index, and hence with this code in place, we would likely be seqscanning the entire obscore table almost every time – which really hurts when you have about 85 Million records in your Obscore table (as I do).

    The standard could immediately have sanitised all this by saying: when you have s_ra and s_dec, you must also give a non-empty s_fov and s_region. This is a classic case for where a MUST would have been necessary to produce something that is usable without jumping through hoops. See my post on Requirements and Validators on this blog for a longer exposition on this whole matter.

    I'm not sure if there is a better solution than the current “if the operators didn't bother with s_region, the dataset's FoV will be ignored“. If you have good ideas, by all means let me know.

    Followup (2024-11-28)

    I've given a talk at the Malta interop giving another view on this matter: VO at the limit.

    [1]

    If you want to try this (in particular without clobbering your “normal” pyVO), do something like this:

    virtualenv --system-site-packages global-datasets
    . global-datasets/bin/activate
    cd global-datasets
    git clone https://github.com/msdemlei/pyvo
    cd pyvo
    git checkout global-datasets
    pip install .
    

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