Adam Ginsburg's blog

• MOVIES!

  Making movies is surprisingly difficult. No matter what language you use, apparently true movie files can only be made by stringing together images, i.e. there is no native movie-producing feature. Gnuplot can do some simple animations but to do anything sophisticated you need to start delving into variables, and for that I switch to a real language. So, I returned to python. As usual, it took no more than a few hours of coding and learning to come up with something. But it bothers me that it took that long: I still think python is most deficient in its failure to create a default column-text reader like 'readcol' in IDL. I can't complain that much, though: I wrote my own in about 5 minutes. Anyway, the key is to use the .set_xdata and .set_ydata functions of a plot to update a canvas. I still don't have nearly as high a plotting speed as I'd like, but it works alright if I don't display to screen. Probably a different backend would be more effective but I don't like to mess with backends. I use `` savefig(filename,dpi=50) `` to reduce the image quality so that it's easier for the animator to handle. ImageMagick's convert can be used to stitch any kind of image into a movie given that you've installed an mpeg2 encoder (fink gave me mpeg2vidcodec).

  The command is very simple: convert -size 300x300 *.png movie.mpg

  I had to use a smaller image size because a series of 1000x12kb files somehow chomped ~6-8 GB of RAM and swap space.

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• my scipy install...

  As far as I was able to reconstruct, my scipy install looked like this when it went well: mkdir scipy-bincp ../scipy-svn/site.cfg .export PATH=/Users/adam/repos/scipy.git/scipy-bin:$PATHln -s /usr/bin/g++-4.0 scipy-bin/g++-4.0ln -s /usr/bin/g++-4.0 scipy-bin/c++export CC=/usr/bin/gcc-4.0 ln -s /usr/bin/gcc-4.0 scipy-bin/ln -s /usr/bin/gcc-4.0 scipy-bin/gccln -s /usr/local/bin/gfortran-4.0 scipy-bin/gfortran-4.0ln -s /usr/local/bin/gfortran-4.0 scipy-bin/gfortranln -s /usr/local/bin/g95 scipy-bin/g95ln -s /usr/local/bin/i686-apple-darwin8-gfortran-4.2 scipy-bin/python2.7 setup.py buildpython2.7 setup.py install However, site.cfg included pointers to AMD and UMFPACK that were installed via the incredibly complicated series of steps listed here: http://blog.hyperjeff.net/?p=160 AG

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• My starred reader articles

  Just posting up a few papers I found interesting. Peng Wang and Tom Abel's paper on outflow feedback in clusters A somewhat less interesting follow-up to the previous HARP mapping of the Serpens cloud core Neal Evans' review of low mass star formation observations the MNRAS paper on supermassive stars in the LMC Identification of molecular clouds from the FCRAO OGS

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• NaN-friendly convolution

  NaN-friendly convolution is important for, e.g., masked data sets in which you want to interpolate across the masked region.

  Astropy has gained this functionality with pull request 155: https://github.com/astropy/astropy/pull/155 but this is a "direct" convolution parallel to IDL's 'convol' routine.

  My FFT-based version now works in N dimensions and is a little cleaner: http://code.google.com/p/agpy/source/browse/trunk/AG_fft_tools/convolve_nd.py

  I'm still working on writing unit tests, and I'm really not sure what the "correct" behavior at the edges is for the different cases... right now, it seems counterintuitive to me, but the code is doing what I expect it to.

  Also, Boxcar kernels always result in shifts for me... they're never supposed to. This is a bug.

  Currently, other links to these codes: http://stackoverflow.com/questions/1100100/fft-based-2d-convolution-and-correlation-in-python/8454010#8454010

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• Neat new things....

  1. sptool is a quick way to compare standards to stellar spectra. Nice, I'd been looking for a tool like that. 2. GNU screen captions are useful especially when working in a screen-within-a-screen environment (who does that, really?) 3. finally got SPLAT to work... turns out I just hadn't reduced my damned data 4. kill -STOP and kill -CONT are really useful ways to pause programs that are sucking up resources if you want to resume them later. Haven't tried this on "real" code yet.

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• New python software

  IDLsave was released on my birthday. It does what you'd expect - read IDL save files into Python. APLpyhas a make_RGB_cube function now that takes care of all the internals of making an RGB image.... haven't tried it yet but I need to because it is awesome.

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• Next Generation Astronomy

  A brief commentary on a recent article Next Generation Astronomy: I'm probably one of the folks who will be working on making that future happen, in the sense of being the intermediary between the end-user and the instrumentalist. Scientists are still needed to build instruments, but those who build the instruments don't necessarily have the time or expertise to build the software pipelines that generate high-quality data. At the same time, the STARLINK folks do a much better job than I do, and I already spend about half of my time comparing different archival data sets... so I think his crystal ball is overall very accurate.

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• Observing 10/20

  I don't have a better place to post this one, so here it is:

  

  My automated fitter (Gaussfitting Cube Collapser) has come a long way. I now adaptively choose to fit 1, 2, or 3 Gaussian components to output to a data cube. The purpose of that code is primarily to find a two-dimensional way to display information about the 3D structure, specifically about the presence/absence of outflows. Outflows will inevitably be confused with multiple velocity components, but they are also likely to be convolved with them.

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• Observing @ GBT: Signs of good & bad data

  So far, all of the observations for the H₂CO densitometry project have been performed at the Green Bank Telescope. During a 10-day long observing trip here, I've learned a lot about diagnosing bad data.

  

  This first image shows TSYS vs Airmass for good data. The high outliers are just sources with continuum in them - the continuum is the source of the extra signal, not atmosphere.  The receiver temperature is a nice 20.6 K, and you get about 5 K extra per airmass, suggesting a zenith optical depth of 0.018 assuming a round atmospheric temperature of 300K.

  

  In the same style plot, there is a set of observations with low system temperatures: that stuff is good. There is also a set with clearly rising system temperatures, even at constant elevation. These data are bad. During this observation, the "blowers" that are meant to keep dew off of the receivers failed. Dew buildup on the receiver covers lead to higher optical depths and therefore system temperatures.

  

  Finally, this data set was totally useless. Ku-band is not particularly sensitive to water in the atmosphere... but it's still not a good idea to observe during a rain storm.  Note that the fitted receiver temperature TREC is nonsensical.

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• Observing run summary

  3 nights of worse than grade 4 weather (tau ~.4, i.e. the atmosphere is nearly opaque) have given me a few interesting results. 1. Non-detections of CS 5-4 in a few bright BGPS cores. My best guess is that the pointing was off, though, because I definitely got down to a noise level around 50mK. 2. Lots of CO 2-1 mapping, 13CO/C18O too. That means I can now use this plot: http://dustem.astro.umd.edu/pdrt/co3221web.html to try to measure the incident flux. 3. I can't do homework at the summit. Maybe it's just that I can't do stellar structures homework, but I really am not processing this stuff well. Damn. 4. Running at altitude uphill is really, really hard. All the running I've been doing the past N months has not even come close to preparing me. Need to correct that... 5. "I am inappropriate." I probably violate a bunch of those rules... the important ones... 6. gotta get back to the mountains. Really. Seriously. OMFG. 7. ARGH those papers need writing! Lots of it! For the next observing run, need the following: 1. Pre-prepared A3 rasters 2. Larger selection of summit music 3. Better sleep schedule preparation 4. More caffeinated drinks per night 5. Cloud cannons.

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