Posts with the Tag pyVO:

  • Towards Data Discovery in pyVO

    2022-01-10 Markus Demleitner

    When I struggled with ways to properly integrate TAP services – which may have hundreds or thousands of different resources in one service – into the VO Registry without breaking what we already had, I realised that there are really two fundamentally different modes of using the VO Registry. In Discovering Data Collections's abstract I wrote:

    the Registry must support both VO-wide discovery of services by type ("service enumeration") and discovery by data collection ("data discovery").

    To illustrate the difference in a non-TAP case, suppose I have archived images of lensed quasars from Telescopes A, B, and C. All these image collections are resources in their own right and should be separately findable when people look for “resources with data from Telescope A“ or perhaps “images obtained between 2011-01-01 and 2011-12-31”.

    However, when a machine wants to find all images at a certain position, publishing the three resources through three different services would mean that that machine has to do three requests where one would work just as well. That is very relevant when you think about how the VO will evolve: At this point there are 342 SIAP services in the VO, and when you read this, that number may have grown further. Adding one service per collection will simply not scale when we want to keep the possibility of all-VO searches. Since I claim that is a very desirably thing, we need to enable collective services covering multiple subordinate resources.

    So, while in the first (“data discovery”) case one wants to query (or at least discover) the three resources separately, in the second case they should be ignored, and only a collective “images of lensed quasars” service should be queried.

    The technical solution to this requirement was creating “auxliary capabilities” as discribed in the endorsed note on discoving data collections cited above. But these of course need client support; VO clients up to now by and large do service enumeration, as that has been what we started with in the VO Registry. Client support would, roughly, mean that clients would present their users with data collections, and then offer the various ways to to access them.

    There are quite a number of technicalities involved in why that's not terribly straightforward for the “big” clients like TOPCAT and Aladin (though Aladin's discovery tree already comes rather close).

    Now that quite a number of people use pyVO interactively in jupyter notebooks, extending pyVO's registry interface to do data discovery in addition to the conventional service enumeration becomes an attractive target to have data discovery in practice.

    I have hence created pyVO PR #289. I think some the rough edges will need to be smoothed out before it can be merged, but meanwhile I'd be grateful if you could try it out already. To facilitate that, I have prepared a jupyter notebook that shows the basic ideas.

    To run it while the PR is not merged, you need to install the forked pyVO. In order to not clobber your main installation, you can install astropy using your package manager and then do the following (assuming your shell is bash or something suitably similar):

    virtualenv --system-site-packages try-discoverdata
. try-discoverdata/bin/activate
cd try-discoverdata
git clone https://github.com/msdemlei/pyvo
cd pyvo
git checkout add-discoverdata
python3 setup.py develop
ipython3 notebook

    That should open a browser window in which you can open the notebook (you probably want to download it into the pyvo checkout in order to make the notebook selector see it). Enjoy!

  • LAMOST5 meets Datalink

    2019-12-11 Markus Demleitner

    One of the busiest spectral survey instruments operated right now is the Large Sky Area Multi-Object Fiber Spectrograph Telescope (LAMOST). And its data in the VO, more or less: DR2 and DR3 have been brought into the VO by our Czech colleagues, but since they currently lack resources to update their services to the latest releases, they have kindly given me their DaCHS resource descriptor, and so I had a head start for publishing DR5 in Heidelberg.

    With some minor updates, here it is now: Over nine million medium-resolution spectra covering large parts of the northen sky – the spatial coverage is like this:

    Coverage Healpix map

    There's lots of fun to be had with this; of course, there's an SSA service, so when you point Aladin or Splat at some part of the covered sky and look for spectra, chances are you'll see LAMOST spectra, and when working on some of our tutorials (this one, for example), it happened that LAMOST actually had what I was looking for when writing them.

    But I'd like to use the opportunity to mention two other modes of accessing the data.

    Stacked spectra

    Tablesample and TOPCAT's Plot Table activation action

    Say you'd like to look at spectra of M stars and would like to have some sample from across the sky, fire up TOPCAT, point its TAP client the GAVO DC TAP service (http://dc.g-vo.org/tap) and run something like:

    select
  ssa_pubDID, accref, raj2000, dej2000, ssa_targsubclass
from lamost5.data tablesample(1)
where
  ssa_targsubclass like 'M%'

    This is using the TABLESAMPLE modifier in the from clause, which isn't standard ADQL yet. As mentioned in the DaCHS 1.4 announcement, DaCHS has a prototype implementation of what's been discussed on the IVOA's DAL mailing list: pick a part of a table rather than the full one. It takes a percentage as an argument, and tells the server to choose about this percentage of the table's records using a reasonable and fast heuristic. Note that this won't give you perfect statistical sampling, but if it's not “good enough” for some purpose, I'd like to learn about that purpose.

    Drawing a proper statistical sample, on the other hand, would take minutes on the GAVO database server – with tablesample, I had the roughly 6000 spectra the above query returns essentially instantaneously, and from eyeballing a sky plot of them, I'd say their distribution is close enough to that of the full DR5. So: tablesample is your friend.

    For a quick look at the spectra themselves, in TOPCAT click Views/Activation Actions, check “Plot Table” and make sure TOPCAT proposes the accref column as “Table Location” (if you don't see these items, update your TOPCAT – it's worth it). Now click on a row or perhaps a dot on a plot and behold an M spectrum.

  • Small Telescopes, Large Surveys

    2019-03-13 Markus Demleitner
    Image: Blink comparator and survey camera

    Plate technology at Bamberg observatory: a blink comparator with one plate mounted, and a survey camera that was once used at Boyden Station, an astronomer outpost in 60ies South Africa.

    I'm currently at the workshop “Large surveys with small telescopes: past, present, and future” (or Astroplate III for short) in Bamberg, where people are discussing using and re-using the rich heritage of historical observations (hence the “plate” part) as well growing that heritage in the age of large CCDs, fast computers and large disks.

    Using and re-using is of course what the Virtual Observatory is about, and we've been keeping fairly large plate collections in our data center for quite a while (among them the Archives of Landessternwarte Königstuhl or the Palomar-Leiden Trojan surveys, and there is the WFPDB TAP-accessibly). Therefore, people from GAVO Heidelberg have been to all past astroplate conferences.

    For this one, I brought a brand-new tutorial on plate scans in the VO, which, I hope, also works as a general introduction to image discovery in the VO using SIAP, Datalink, and Obscore. If you're doing image stuff now and then, please have a quick look at the thing – I am particularly grateful for hints on what to improve or perhaps particularly obvious use cases for the material discussed.

    Such VO proselytising aside, the conference is discussing the wide variety of creative, low-cost data collectors out there as well as computer-aided re-analysis extracting new knowledge from decades-old data. If I had to choose a single come-to-think-of-it moment, it would be Norbert Zacharias' observation that if you have a well-behaved object and you'd like to know where it was in 1900, it's now more accurate to extrapolate Gaia astrometry to the epoch of observation than to measure it on the plate itself. Which is saying a lot about the amazing feat of engineering that Gaia is.

    This is not, however, an argument for dumping the old data. Usually, it is exactly what is not so well-behaved (like those) that's interesting – both in terms of astrometry and in terms of photometry (for which there's a lot more unruly behaviour in the first place). To figure out how objects don't behave well, and, for objects disguising as well-behaved only on time scales of the (say) Gaia mission, which these are, the key is “old” data. The freshness of which we're discussing this week.

  • Gaia DR2: A light version and light curves

    2018-05-07 Markus Demleitner
    screenshot: topcat and matplotlib

    Topcat is doing datalink, and our little python script has plotted a two-color time series of RMC 18 (or so I think).

    If anyone ever writes a history of the VO, the second data release of Gaia on April 25, 2018 will probably mark its coming-of-age – at least if you, like me, consider the Registry the central element of the VO. It was spectacular to view the spike of tens of Registry queries per second right around 12:00 CEST, the moment the various TAP services handing out the data made it public (with great aplomb, of course).

    In GAVO's Data Center we also carry Gaia DR2 data. Our host institute, the Zentrum für Astronomie in Heidelberg, also has a dedicated Gaia server. This gives relieves us from having to be a true mirror of the upstream data release. And since the source catalog has lots and lots of columns that most users will not be using most of the time, we figured a “light” version of the source catalog might fill an interesting ecological niche: Behold gaia.dr2light on the GAVO DC TAP service, containing essentially just the basic astrometric parameters and the diagonal of the covariance matrix.

    That has two advantages: Result sets with SELECT * are a lot less unwieldy (but: just don't do this with Gaia DR2), and, more importantly, a lighter table puts less load on the server. You see, conventional databases read entire rows when processing data, and having just 30% of the columns means we will be 3 times faster on I/O-bound tasks (assuming the same hardware, of course). Hence, and contrary to several other DR2-carrying sites, you can perform full sequential scans before timing out on our TAP service on gaia.dr2light. If, on the other hand, you need to do debugging or full-covariance-matrix error calculations: The full DR2 gaia_source table is available in many places in the VO. Just use the Registry.

    Photometry via TAP

    A piece of Gaia DR2 that's not available in this form anywhere else is the lightcurves; that's per-transit photometry in the G, BP, and RP band for about 0.5 million objects that the reduction system classified as variable. ESAC publishes these through datalink from within their gaia_source table, and what you get back is a VOTable that has the photometry in the three bands interleaved.

    I figured it might be useful if that data were available in a TAP-queriable table with lightcurves in the database. And that's how gaia.dr2epochflux came into being. In there, you have three triples of arrays: the epochs (g_transit_time, bp_obs_time, and rp_obs_time), the fluxes (g_transit_flux, bp_flux, and rp_flux), and their errors (you can probably guess their names). So, to retrieve G lightcurves where available together with a gaia_source query of your liking, you could write something like:

    SELECT g.*, g_transit_time, g_transit_flux
FROM gaia.dr2light AS g
LEFT OUTER JOIN gaia.dr2epochflux
USING (source_id)
WHERE ...whatever...

    – the LEFT OUTER JOIN arranges things such that the g_transit_time and g_transit_flux columns simply are NULL when there are no lightcurves; with a normal (“inner”) join, rows without lightcurves would not be returned in such a query.

    To give you an idea of what you can do with this, suppose you would like to discover new variable blue supergiants in the Gaia data (who knows – you might discover the precursor of the next nearby supernova!). You could start with establishing color cuts and train your favourite machine learning device on light curves of variable blue supergiants. Here's how to get (and, for simplicity, plot) time series of stars classified as blue supergiants by Simbad for which Gaia DR2 lightcurves are available, using pyvo and a little async trick:

    from matplotlib import pyplot as plt
import pyvo

def main():
  simbad = pyvo.dal.TAPService(
    "http://simbad.u-strasbg.fr:80/simbad/sim-tap")
  gavodc = pyvo.dal.TAPService("http://dc.g-vo.org/tap")

  # Get blue supergiants from Simbad
  simjob = simbad.submit_job("""
    select main_id, ra, dec
    from basic
    where otype='BlueSG*'""")
  simjob.run()

  # Get lightcurves from Gaia
  try:
    simjob.wait()
    time_series = gavodc.run_sync("""
      SELECT b.*, bp_obs_time, bp_flux, rp_obs_time, rp_flux
      FROM (SELECT
         main_id, source_id, g.ra, g.dec
         FROM
        gaia.dr2light as g
         JOIN TAP_UPLOAD.t1 AS tc
         ON (0.002>DISTANCE(tc.ra, tc.dec, g.ra, g.dec))
      OFFSET 0) AS b
      JOIN gaia.dr2epochflux
      USING (source_id)
      """,
      uploads={"t1": simjob.result_uri})
  finally:
    simjob.delete()

  # Now plot one after the other
  for row in time_series.table:
    plt.plot(row["bp_obs_time"], row["bp_flux"])
    plt.plot(row["rp_obs_time"], row["rp_flux"])
    plt.show(block=False)
    raw_input("{}; press return for next...".format(row["main_id"]))
    plt.cla()

if __name__=="__main__":
  main()

    If you bother to read the code, you'll notice that we transfer the Simbad result directly to the GAVO data center without first downloading it. That's fairly boring in this case, where the table is small. But if you have a narrow pipe for one reason or another and some 105 rows, passing around async result URLs is a useful trick.

    In this particular case the whole thing returns just four stars, so perhaps that's not a terribly useful target for your learning machine. But this piece of code should get you started to where there's more data.

    You should read the column descriptions and footnotes in the query results (or from the reference URL) – this tells you how to interpret the times and how to make magnitudes from the fluxes if you must. You probably can't hear it any more, but just in case: If you can, process fluxes rather than magnitudes from Gaia, because the errors are painful to interpret in magnitudes when the fluxes are small (try it!).

    Note how the photometry data is stored in arrays in the database, and that VOTables can just transport these. The bad news is that support for manipulating arrays in ADQL is pretty much zero at this point; this means that, when you have trained your ML device, you'll probably have to still download lots and lots of light curves rather than write some elegant ADQL to do the filtering server-side. However, I'd be highly interested to work out how some tastefully chosen user defined functions might enable offloading at least a good deal of that analysis to the database. So – if you know what you'd like to do, by all means let me know. Perhaps there's something I can do for you.

    Incidentally, I'll talk a bit more about ADQL arrays in a blog post coming up in a few weeks (I think). Don't miss it, subscribe to our feed).

    SSAP and Obscore

    If you're fed up with bleeding-edge tech, the light curves are also available through good old SSAP and Obscore. To use that, just get Splat (or another SSA client, preferably with a bit of time series support). Look for a Gaia DR2 time series service (you may have to update the service list before you find it), enter (in keeping with our LBV theme) S Dor as position and hit “Lookup” followed by “Send Query”. Just click on any result to just view the time series – and then apply Splat's rich tool set to it.

    Update (8.5.2018): Clusters

    Here's another quick application – how about looking for variable stars in clusters? This piece of ADQL should get you started:

    SELECT TOP 100
  source_id, ra, dec, parallax, g.pmra, g.pmdec,
  m.name, m.pmra AS c_pmra, m.pmde AS c_pmde,
  m.e_pm AS c_e_pm,
  1/dist AS cluster_parallax
FROM
  gaia.dr2epochflux
  JOIN gaia.dr2light AS g USING (source_id)
  JOIN mwsc.main AS m
  ON (1=CONTAINS(
    POINT(g.ra, g.dec),
    CIRCLE(m.raj2000, m.dej2000, rcluster)))
WHERE IN_UNIT(pmdec, 'deg/yr') BETWEEN m.pmde-m.e_pm*3 AND m.pmde+m.e_pm*3

    – yes, you'll want to constrain pmra, too, and the distance, and properly deal with error and all. But you get simple lightcurves for free. Just add them in the SELECT clause!

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