Artikel mit Tag Python:

  • Migrating Away From Wordpress

    Since 2016, this blog was served through a Wordpress instance at the Astrophysical Institute Potsdam AIP – thanks again to our colleagues there for maintaining the platform over all these years.

    But since it now seems as if this is something that might last a long time (by Web standards), we have decided that we should leave PHP behind and look for something properly version controllable, and something that can simply live somewhere on a web server with essentially zero maintenance. Hence, we have moved the content to pelican – which has a clean Debian package, is written in Python, and does not need any active components of its own.

    As an extra bonus, the blog posts are now authored in ReStructuredText, which happens to be what DaCHS' documentation is written in, and what you can use to author metadata for DaCHS resources. If you want, you can now check out the source code for the articles (sorry, it's still subversion; one of these days I'll find something fancier than naked git but lighter than gitlab, and then I'll move GAVO's VCS to git).

    As expected, porting the theme (which I only did rather half-heartedly, so things are a bit less pretty now) and getting the figures right was what caused the bulk of the work. On the plus side, I have also greatly cleaned up categories and tags. Still, it's quite likely we messed something up. If you find anything broken here, please let us know: https://www.g-vo.org/pmwiki/About/Impressum lists the main ways through which you can reach us.

    With that: Subscribe to our Atom feed!

  • Tangible Astronomy and Movies with TOPCAT

    This March, I've put up two new VO resources (that's jargon for “table or service or whatever”) that, I think, fit quite well what I like to call tangible astronomy: things you can readily relate to what you see when you step out at night. And, since I'm a professing astronomy nerd, that's always nicely gratifying.

    The two resources are the Constellations as Polygons and the Gaia eDR3 catalogue of nearby stars (GCNS).

    Constellations

    On the constellations, you might rightfully say that's really far from science. But then they do help getting an idea where something is, and when and from where you might see something. I've hence wanted for a long time to re-publish the Davenhall Constellation Boundary Data as proper, ADQL-queriable polygons, and figuring out where the loneliest star in the sky (and Voyager 1) were finally made me do it.

    GCNS density around taurus

    Taurus in the GCNS density plot: with constellations!

    So, since early March there's the cstl.geo table on the TAP service at https://dc.g-vo.org/tap with the constallation polygons in its p column. Which, for starters, means it's trivial to overplot constallation boundaries in your favourite VO clients now, as in the plot above. To make it, I've just done a boring SELECT * FROM cstl.geo, did the background (a plain HEALPix density plot of GCNS) and, clicked Layers → Add Area Control and selected the cstl.geo table.

    If you want to identify constellations by clicking, while in the area control, choose “add central” from the Forms menu in the Form tab; that's what I did in the figure above to ensure that what we're looking at here is the Hyades and hence Taurus. Admittedly: these “centres“ are – as in the catalogue – just the means of the vertices rather than the centres of mass of the polygon (which are hard to compute). Oh, and: there is also the AreaLabel in the Forms menu, for when you need the identification more than the table highlighting (be sure to use a center anchor here).

    Note that TOPCAT's polygon plot at this point is not really geared towards large polygons (which the constellations are) right now. At the time of writing, the documentation has: “Areas specified in this way are generally intended for displaying relatively small shapes such as instrument footprints. Larger areas may also be specified, but there may be issues with use.” That you'll see at the edges of the sky plots – but keeping that in mind I'd say this is a fun and potentially very useful feature.

    What's a bit worse: You cannot turn the constellation polygons into MOCs yet, because the MOC library currently running within our database will not touch non-convex polygons. We're working on getting that fixed.

    Nearby Stars

    Similarly tangible in my book is the GCNS: nearby stars I always find romantic.

    Let's look at the 100 nearest stars, and let's add spectral types from Henry Draper (cf. my post on Annie Cannon's catalogue) as well as the constellation name:

    WITH nearest AS (
    SELECT TOP 100
      a.source_id,
      a.ra, a.dec,
      phot_g_mean_mag,
      dist_50,
      spectral
    FROM gcns.main AS a
    LEFT OUTER JOIN hdgaia.main AS b
      ON (b.source_id_dr3=a.source_id)
    ORDER BY dist_50 ASC)
    SELECT nearest.*, name
    FROM nearest
    JOIN cstl.geo AS g
      ON (1=CONTAINS(
        POINT(nearest.ra, nearest.dec),
        p))
    

    Note how I'm using CONTAINS with the polygon in the constellations table here; that's the usage I've had in mind for this table (and it's particularly handy with table uploads).

    That I have a Common Table Expression (“WITH”) here is due to SQL planner confusion (I'll post something about that real soon now): With the WITH, the machine first selects the nearest 100 rows and then does the (relatively costly) spatial match, without it, the machine (somewhat surprisingly) did the geometric match first. This particular confusion looks fixable, but for now I'd ask you for forgiveness for the hack – and the technique is often useful anyway.

    If you inspect the result, you will notice that Proxima Cen is right there, but α Cen is missing; without having properly investigated matters, I'd say it's just too bright for the current Gaia data reduction (and quite possibly even for future Gaia analysis).

    Most of the objects on that list that have made it into the HD (i.e., have a spectral type here) are K dwarfs – which is an interesting conspiracy between the limits of the HD (the late red and old white dwarfs are too weak for it) and the limits of Gaia (the few earlier stars within 6 parsec – which includes such luminaries as Sirius at a bit more than 2.5 pc – are just too bright for where Gaia data reduction is now).

    Animation

    Another fairly tangible thing in the GCNS is the space velcity, given in km/s in the three dimensions U, V, and W. That is, of course, an invitation to look for stellar streams, as, within the relatively small portion of the Milky Way the GCNS looks at, stars on similar orbits will exhibit similar space motions.

    Considering the velocity dispersion within a stellar stream will be a few km/s, let's have the database bin the data. Even though this data is small enough to conveniently handle locally, this kind of remote analysis is half of what TAP is really great at (the other half being the ability to just jump right into a new dataset). You can group by multiple things at the same time:

    SELECT
      COUNT(*) AS n,
      ROUND(uvel_50/5)*5 AS ubin,
      ROUND(vvel_50/5)*5 AS vbin,
      ROUND(wvel_50/5)*5 AS wbin
    FROM gcns.main
    GROUP BY ubin, vbin, wbin
    

    Note that this (truly) 3D histogram only represents a small minority of the GCNS objects – you need radial velocities for space motion, and these are precious even in the Gaia age.

    What really surprised me is how clumpy this distribution is – are we sure we already know all stellar streams in the solar neighbourhood? Watch for yourself (if your browser can't play webm, complain to your vendor):

    [Update (2021-04-01): Mark Taylor points out that the “flashes” you sometimes see when the grid is aligned with the viewing axes (and the general appearance) could be improved by just pulling all non-NULL UVW values out of the table and using a density plot (perhaps shading=density densemap=inferno densefunc=linear). That is quite certainly true, but it would of course defeat the purpose of having on-server aggregation. Which, again, isn't all that critical for this dataset, so doing the prettier plot actually is a valuable exercise for the reader]

    How did I make this video? Well, I started with a Cube Plot in TOPCAT as usual, configuring weighted plotting with n as its weight and played around a bit with scaling out a few outliers. And then I saved the table (to zw.vot), hit “STILTS“ in the plot window and saved the text from there to a text file, zw.sh. I had to change the ``in`` clause in the script to make it look like this:

    #!/bin/sh
    stilts plot2cube \
     xpix=887 ypix=431 \
     xlabel='ubin / km/s' ylabel='vbin / km/s' \
     zlabel='wbin / km/s' \
     xmin=-184.5 xmax=49.5 ymin=-77.6 ymax=57.6 \
     zmin=-119.1 zmax=94.1 phi=-84.27 theta=90.35 \
      psi=-62.21 \
     auxmin=1 auxmax=53.6 \
     auxvisible=true auxlabel=n \
     legend=true \
     layer=Mark \
        in=zw.vot \
        x=ubin y=vbin z=wbin weight=n \
        shading=weighted size=2 color=blue
    

    – and presto, sh zw.sh would produce the plot I just had in TOPCAT. This makes a difference because now I can animate this.

    In his documentation, Mark already has a few hints on how to build animations; here are a few more ideas on how to organise this. For instance, if, as I want here, you want to animate more than one variable, stilts tloop may become a bit unwieldy. Here's how to give the camera angles in python:

    import sys
    from astropy import table
    import numpy
    
    angles = numpy.array(
      [float(a) for a in range(0, 360)])
    table.Table([
        angles,
        40+30*numpy.cos((angles+57)*numpy.pi/180)],
      names=("psi", "theta")).write(
        sys.stdout, format="votable")
    

    – the only thing to watch out for is that the names match the names of the arguments in stilts that you want to animate (and yes, the creation of angles will make numpy afficionados shudder – but I wasn't sure if I might want to have somewhat more complex logic there).

    [Update (2021-04-01): Mark Taylor points out that all that Python could simply be replaced with a straightforward piece of stilts using the new loop table scheme in stilts, where you would simply put:

    animate=:loop:0,360,0.5
    acmd='addcol phi $1'
    acmd='addcol theta 40+30*cosDeg($1+57)'
    

    into the plot2cube command line – and you wouldn't even need the shell pipeline.]

    What's left to do is basically the shell script that TOPCAT wrote for me above. In the script below I'm using a little convenience hack to let me quickly switch between screen output and file output: I'm defining a shell variable OUTPUT, and when I un-comment the second OUTPUT, stilts renders to the screen. The other changes versus what TOPCAT gave me are de-dented (and I've deleted the theta and psi parameters from the command line, as I'm now filling them from the little python script):

    OUTPUT="omode=out out=pre-movie.png"
    #OUTPUT=omode=swing
    
    python3 camera.py |\
    stilts plot2cube \
       xpix=500 ypix=500 \
       xlabel='ubin / km/s' ylabel='vbin / km/s' \
       zlabel='wbin / km/s' \
       xmin=-184.5 xmax=49.5 ymin=-77.6 ymax=57.6 \
       zmin=-119.1 zmax=94.1 \
       auxmin=1 auxmax=53.6 \
    phi=8 \
    animate=- \
    afmt=votable \
    $OUTPUT \
       layer=Mark \
          in=zw.vot \
          x=ubin y=vbin z=wbin weight=n \
          shading=weighted size=4 color=blue
    
    # render to movie with something like
    # ffmpeg -i "pre-movie-%03d.png" -framerate 15 -pix_fmt yuv420p /stream-movie.webm
    # (the yuv420p incantation is so real-world
    # web browsers properly will not go psychedelic
    # with the colours)
    

    The comment at the end says how to make a proper movie out of the PNGs this produces, using ffmpeg (packaged with every self-respecting distribution these days) and yielding a webm. Yes, going for mpeg x264 might be a lot faster for you as it's a lot more likely to have hardware support, but everything around mpeg is so patent-infested that for the sake of your first-born's soul you probably should steer clear of it.

    Movies are fun in webm, too.

  • DaCHS 2.1: Say hello to Python 3

    DaCHS and python logos

    Today, I have released DaCHS 2.1, the first stable DaCHS running on Python 3. I have tried hard to make the major version move painless and easy, and indeed “pure DaCHS” RDs should just continue to work. But wherever there's Python in your RDs or near them, things may break, since Python 3 is different from Python 2 in some rather fundamental ways.

    Hence, the Debian package even has a new name: gavodachs2-server. Unless you install that, things will keep running as they do. I will keep fixing serious DaCHS 1 bugs for a while, so there's no immediate urgency to migrate. But unless you migrate, you will not see any new features, so one of these days you will have to migrate anyway. Why not do it today?

    Migrating to DaCHS 2

    In principle, just say apt install gavodachs2-server and hope for the best. If you have a development machine and regression tests defined, this is actually what we recommend, and we'd be very grateful to learn of any problems you may encounter.

    If you'd rather be a little more careful, Carlos Henrique Brandt has kindly updated his Docker files in order to let you spot problems before you mess up your production server. See Test Migration for a quick intro on how to do that. If you spot any problems that are not related to the Python 3 pitfalls mentioned in the howto linked below or nevow exodus, please tell me or (preferably) the dachs-support mailing list.

    A longer, more or less permanent piece elaborating possible migration pains, is in our how-to documentation: How do I go from DaCHS1 to DaCHS2?

    What's new in DaCHS2?

    I've used the opportunity of the major version change to remove a few (mis-) features that I'm rather sure nobody uses; and there are a few new features, too. Here's a rundown of the more notable changes:

    • DaCHS now produces VOTable 1.4 by default. This is particularly notable when you provide TIMESYS metadata (on which I'll report some other time).
    • When doing spatial indices, prefer the new //scs#pgs-pos-index to //scs#q3cindex. While q3c is still faster and more compact than pgsphere when just indexing points, on the longer run I'd like to shed the extra dependency (note, however, that the pgsphere index limits the cone search to a maximum radius of 90 degrees at this point).
    • Talking about Cone Search: For custom parameters, DaCHS has so far used SSA-like syntax, so you could say, for instance, vmag=12/13 (for “give me rows where vmag is between 12 and 13”). Since I don't think this was widely used, I've taken the liberty to migrate to DALI-compliant syntax, where intervals are written as they would be in VOTable PARAM values: vmag=12 13.
    • In certain situations, DaCHS tries to enable parallel queries (previously on this blog).
    • Some new ADQL user defined functions: gavo_random_normal, gavo_mocintersect, and gavo_mocunion. See the TAP capabilities for details, and note that the moc functions will fail until we put out a new pgsphere package that has support for the MOC-MOC operations.
    • dachs info (highly recommended after an import) now takes a --sample-percent option that helps when doing statistics on large tables.
    • For SSA services serving something other than spectra (in all likelihood, timeseries), you can now set a productType meta as per the upcoming SimpleDALRegExt 1.2.
    • If you have large, obscore-published SIAP tables, re-index them (dachs imp -I q) so queries over s_ra and s_dec get index support, too.
    • Since we now maintain RD state in the database, you can remove the files /var/gavo/state/updated* after upgrading.
    • When writing datalink metaMakers returning links, you can (and should, for new RDs) define the semantics in an attribute to the element rather in the LinkDef constructor.
    • Starting with this version, it's a good idea to run dachs limits after an import. This, right now, will mainly set an estimate for the number of rows in a table, but that's already relevant because the ADQL translator uses it to help the postgres query planner. It will later also update various kinds of column metadata that, or so I hope, will become relevant in VODataService 1.3.
    • forceUnique on table elements is now a no-op (and should be removed); just define a dupePolicy as before.
    • If you write bad obscore mappings, it could so far be hard to figure out the reason of the failure and, between lots of confusing error messages, to fix it. Instead, you can now run ``dachs imp //obscore recover`` in such a situation. It will re-create the obscore table and throw out all stanzas that fail; after that, you can fix the obscore declarations that were thrown out one by one.
    • If you run DaCHS behind a reverse proxy that terminates https, you can now set [web]adaptProtocol in /etc/gavo.rc to False. This will make that setup work for form-based services, too.
    • If you have custom OAI set name (i.e., anything but local and ivo_managed in the sets attribute of publish elements), you now have to declare them in [ivoa]validOAISets.
    • Removed things: the docform renderer (use form instead), the soap renderer (well, it's not actually removed, it's just that the code it depends on doesn't exist on python3 any more), sortKey on services (use the defaultSortKey property), //scs#q3cpositions (port the table to have ra and dec and one of the SCS index mixins), the (m)img.jpeg renderers (if you were devious enough to use these, let me know), and quite a few even more exotic things.

    Some Breaking Changes

    Python 3 was released in 2008, not long after DaCHS' inception, but since quite a few of the libraries it uses to do its job haven't been available for Python 3, we have been reluctant to make the jump over the past then years (and actually, the stability of the python2 platform was a very welcome thing).

    Indeed, the most critical of our dependencies, twisted, only became properly usable with python3 in, roughly, 2017. Indeed, large parts of DaCHS weren't even using twisted directly, but rather a nice add-on to it called nevow. Significant parts of nevow bled through to DaCHS operators; for instance, the render functions or the entire HTML templating.

    Nevow, unfortunately, fell out of fashion, and so nobody stepped forward to port it. And when I started porting it myself I realised that I'm mainly using the relatively harmless parts of nevow, and hence after a while I figured that I could replace the entire dependency by something like a 1000 lines in DaCHS, which, given significant aches when porting the whole of nevow, seemed like a good deal.

    The net effect is that if you built code on top of nevow – most likely in the form of a custom renderer – that will break now, and porting will probably be rather involved (having ported ~5 custom renderers, I think I can tell). If this concerns you, have a look at the README in gavo.formal (and then complain because it's mainly notes to myself at this point). I feel a bit bad about having to break things that are not totally unreasonable in this drastic way and thus offer any help I can give to port legacy DaCHS code.

    Outside of these custom renderers, there should just be a single visible change: If you have used n:data="some_key" in nevow templates to pull data from dictionaries, that won't work any longer. Use n:data="key some_key" n:render="str" instead. And it turns out that this very construct was used in the default root template, which you may have derived from. So – see if you have /var/gavo/web/templates/root.html and if so, whether there is <ul n:data="chunk" in there. If you have that, change it to <ul n:data="key chunk".

    Update (2020-11-19): Two only loosely related problems have surfaced during updates. In particular if you are updating on rather old installations, you may want to look at the points on Invalid script type preIndex and function spoint_in already exists in our list of common problems.

  • DaCHS 1.4 is out

    Dachs logo with "version 1.4" superposed

    Since the Groningen Interop is over, it's time for a DaCHS release, and so, roughly half a year after the release of DaCHS 1.3, today I've pushed DaCHS 1.4 into our Debian repository.

    As usual, you should upgrade as soon as you find time to do so, because upgrades become more difficult if they span large version gaps; and one of these days you will need some new feature or run into one of the odd bugs. Upgrading is a good opportunity to also get your DaCHS ready for buster by adding the repos mentioned there.

    The list of new features is rather short this time around. Here are some noteworthy ones:

    • There's now an XML grammar that can be used when you have to parse smallish snippets of XML as, for instance, in VOEvent.
    • You can now use TABLESAMPLE(1) after a table specification in DaCHS' ADQL to tell the database engine to just use 1% of a table for a query. While this isn't a precise way to sample tables, it's great when developing queries.
    • Also among new features I'd like to see in ADQL and have therefore put into DaCHS is GENERATE_SERIES(a,b), which is what is known as table-generating function in SQL . If you know SDSS CasJobs, you'll have seen lots of those already. GENERATE_SERIES, however, is really plain: it just spits out a table with a column with integers between a and b. For an example of why one might what to have that, check out the poster I'm linking to in my ADASS report.
    • If you have an updating data descriptor (usually, because you keep feeding data into a data collection), DaCHS will no longer automatically re-make its dependencies (like, say, views). That's because that's not necessary in general, and it's a pain if every update on an obscore-published table tears down and rebuilds the obscore view. For the rare cases when you do need to rebuild dependencies, there's now a remakeOnDataChange attribute on data.
    • At the interop, I've mentioned a few use cases for knowing which server software you're talking to, and I've said that people should set their server headers to informative values. DaCHS does that now.

    To conclude on a low note: This is probably going to be the last release of DaCHS for python 2. Even though we will have to shed a dependency or two that simply will not be ported to python 3, and even though I'm rather unhappy with a few properties of the python 3 port of twisted, there's probably no way to escape this, given that Debian is purging out python 2 packages quickly already.

    So, when we meet again for the next release, you'll probably be looking at DaCHS 2.0, and where you have custom code in your RDs, it's rather likely that you'll see a minor amount of breakage. I promise I'll do everything I can to make the migration easy for deployers, but I can't do higher magic, so: If there's ever been a time to add regression tests to your RDs, it's now.

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