Additional notes and script for the ALMA array combinations for the Data Reduction Workshop (29-oct-2024). This link will be available on https://github.com/teuben/qac2024 - please reload/pull this regularly for timely updates.
Original link: https://science.nrao.edu/facilities/alma/facilities/alma/naasc-workshops/nrao-dr-umd24
Link to presentation slides: https://drive.google.com/drive/folders/170W_JlYYYmVs9W5B79Q5Du2V5l7qCf-t
- 10:30 - 10:40 Welcome coffee & event logistics
- 10:40 - 11:40 ALMA Overview and Interferometry Basics (Eltha Teng)
- 11:40 - 12:00 Science Talk (Keaton Donaghue): "Enshrouded in Dust: Using ALMA to Probe the Embedded Super Star Clusters in NGC 253"
- 12:00 - 13:00 Lunch (provided for in-person attendees)
- 13:00 - 13:30 Verifying data, scripts, and using CASA and CARTA
- 13:30 - 14:30 Tutorial: continuum imaging and self-calibration with CASA (Eltha Teng)
- 14:30 - 14:50 Short break
- 14:50 - 16:20 Tutorial: imaging array combination with feathering and advanced methods (Peter Teuben)
- 16:20 - 16:45 Tutorial: data visualization with CARTA (Eltha & Peter)
- 16:45 - 17:00 Wrap-up & survey: https://naasc.typeform.com/to/r6wNKWpk#workshop=umd
An explanation of files you might see in the qac2024 directory is here
See https://casa.nrao.edu/casa_obtaining.shtml how to obtain casa
Some example links:
- https://casa.nrao.edu/download/distro/casa/release/rhel/casa-6.6.5-31-py3.10.el8.tar.xz
- https://alma-dl.mtk.nao.ac.jp/ftp/casa/distro/casa/release/rhel/casa-6.5.6-22-py3.8.el8.tar.xz
Although 6.6.5 is available, current examples have only been verified for 6.5.6 (Peter)
Download, extract, and run. Here's an example for linux:
tar xf casa-6.5.6-22-py3.8.el8.tar.xz
casa-6.6.5-31-py3.10.el8/bin/casa
if you an error about missing data, create this directory, and start casa again. This is likely to happen if you're never run CASA.
mkdir $HOME/.casa/data
For Ubuntu Linux (this include Windows/WSL2), carta is now available as a standard package
sudo apt install carta
otherwise head over to https://cartavis.org to download your favorite version of CARTA.
wget https://github.com/CARTAvis/carta/releases/latest/download/carta.AppImage.x86_64.tgz
Some of the demos need you to install a plugin for CASA, called analysis script. I normally put them in the ~/.casa directory, e.g.
cd ~/.casa
wget ftp://ftp.cv.nrao.edu/pub/casaguides/analysis_scripts.tar
tar xf analysis_scripts.tar
and add the plugin to your ~/.casa/startup.py file:
import sys
sys.path.append("/home/teuben/.casa/analysis_scripts")
import analysisUtils as au
for a more portable version, one can use
import sys, os
sys.path.append("%s/.casa/analysis_scripts" % os.environ["HOME"])
import analysisUtils as au
casa-6.6.5-31-py3.10.el8/bin/casa
CASA <1> exportfits('demo/M100_combine12+7_CO_cube.image','M100-demo.fits')
CASA <2> exit
$ ds9 M100-demo.fits
$ carta M100-demo.fits
# for very large cubes, conversion can be useful to speed up CARTA
$ fits2idia [-o M100-demo.hdf5] M100-demo.fits
$ carta M100-demo.fits
For large computations your laptop may not efficiently be able to display the data. Of course you can copy them to your laptop, but with CARTA , much like jupyter notebooks, there's now a way to remotely compute and locally display using your browser. You will need to use an ssh port forwarding technique for this.
remote% carta M100_demo.fits --no_browser
# copy and paste the URL with the token on your local machine
xdg-open http://129.2.14.56:3002/?token=4fff9b36-1925-4fbb-928f-a609a114a35f
if this doesn't work, perhaps the port is being blocked, and you might be able to use ssh port forwarding, e.g.
xxx% PORT=3333 && ssh -L ${PORT}:localhost:${PORT} <user>@<server> carta --host=localhost --port=${PORT} --no_browser
Compare this to a similar way how remote jupyter notebooks can be run:
# login on the remote
local% ssh user@remote
# set up your python environment on the remote, YMMV
remote% source anaconda3/python_start.sh
# start up a notebook without local browser, but pick a free port number
# watch the URL to be loaded later
remote% jupyter notebook --no-browser --port=8086
# set up port forwarding between laptop and remote, this will leave an open shell on remote
local% ssh -L 8086:localhost:8086 user@remote
local%
#
local% xdg-open http://localhost:8086/tree?token=blablablabla
Python's Global Interpreter Lock (GIL) can pay havoc on casa's runtime. The 7m imaging script took 3 mins on my laptop, where if using 1 processorm it ran in 1 min. The 12m imaging not as fast a speedup: 9.5 mins to 6 mins.`
Here is how you can force casa to run with 1 processor
OMP_NUM_THREADS=1 casa-6.6.5-31-py3.10.el8/bin/casa
- https://almascience.nrao.edu/alma-data/science-verification (TW Hyd is #1 on the list, M100 is #4 on the list)
- https://casaguides.nrao.edu/index.php?title=TWHydraBand7
- https://casaguides.nrao.edu/index.php/M100_Band3
- https://casaguides.nrao.edu/index.php/M100_Band3_Combine
- https://casaguides.nrao.edu/index.php/M100_Band3_SingleDish
Note currently the Combine and SingleDish casaguides point to casa version 6.5.4 (Fall 2024), even though the latest version is 6.6.5
For TW-Hya, we need the calibrated measurement set (twhya_calibrated.ms).
1. https://bulk.cv.nrao.edu/almadata/sciver/TWHya/TWHYA_BAND7_CalibratedData.tgz 5.7 GB
2. https://bulk.cv.nrao.edu/almadata/sciver/TWHya/TWHYA_BAND7_ReferenceImages.tgz 8.5 MB
-
TW-Hya data: https://bulk.cv.nrao.edu/almadata/public/ALMA_firstlooks/twhya_firstlook.tgz
-
Extract the needed files:
tar -xvzf twhya_firstlook.tgz
tar -xvf twhya_calibrated.ms.tar
- Download the text files:
1. https://github.com/teuben/qac2024/blob/main/imaging_tutorial.py
2. https://github.com/teuben/qac2024/blob/main/selfcal_tutorial.py
For M100 We will need 12m, 7m, and TP data.
-
M100 12m data: http://almascience.org/almadata/sciver/M100Band3_12m
-
https://bulk.cv.nrao.edu/almadata/sciver/M100Band3_12m/M100_Band3_12m_CalibratedData.tgz 15GB ***
-
https://bulk.cv.nrao.edu/almadata/sciver/M100Band3_12m/M100_Band3_12m_Imaging.py
M100_Band3_12m_CalibratedData.ms M100_12m_CO_demo.mask M100_Band3_12m_Imaging.py (should produce M100_Band3_12m_ReferenceImages.tgz = 297M)
-
-
M100 7m & TP data: http://almascience.org/almadata/sciver/M100Band3ACA
https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_7m_CalibratedData.tgz - 9.1G *** https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_7m_Imaging.py - still python2 https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_TP_CalibratedData_5.1.tgz - 13G --no-- https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_DataComb_ReferenceImages_5.1.tgz - 393M https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_ACA_ReferenceImages_5.1.tgz - 24M -
M100 Combo Tutorial: https://bulk.cv.nrao.edu/almadata/public/combo_tutorial
M100_combine12+7_CO_cube.image.tgz script.py
As mentioned, the tgz files dump their (calibrated) data in subdirectories. To avoid confusion I decided to use symlinks to these places, instead of moving them. So we would have:
ln -s M100_Band3_12m_CalibratedData/M100_Band3_12m_CalibratedData.ms/
ln -s M100_Band3_7m_CalibratedData/M100_Band3_7m_CalibratedData.ms
ln -s M100_Band3_ACA_ReferenceImages_5.1/M100_TP_CO_cube.spw3.image.bl
The data for M100 are huge! We have compiled a dataset with trimmed down data for the 12m and 7m with just 70 x 5km/s channels to combine the data much quicker.
To be confirmed if these still work, but this will be the link:
wget https://www.astro.umd.edu/~teuben/QAC/qac_bench5.tar.gz
tar zxf qac_bench5.tar.gz
OMP_NUM_THREADS=1 casa-6.6.5-31-py3.10.el8/bin/casa
execfile("qac.py")
%time execfile("M100_Band3_7m_Imaging_trimmed.py")
this took 1 minute on my laptop, and 3 minutes if the OMP setting was ignored and the code ran across all my cores.
- CASA: https://casa.nrao.edu/casa_obtaining.shtml
- Analysis Scripts: ftp://ftp.cv.nrao.edu/pub/casaguides/analysis_scripts.tar
- CARTA: https://cartavis.org/
- DataComb: https://github.com/teuben/DataComb
- QAC: https://github.com/teuben/QAC
- Phangs: https://github.com/akleroy/phangs_imaging_scripts
- CASA-Guides-Script-Extractor: https://github.com/CasaGuides/CASA-Guides-Script-Extractor
- Recent related workshops of note:
- Ekers and Rots 1979 - Short Spacing Synthesis from a Primary Beam Scanned Interferometer
- Vogel et al. 1984 - Interaction of the outflow and quiescent gas in Orion : HCO+ aperture synthesis maps.
- Braun and Walterbos 1985 - A solution to the short spacing problem in radio interferometry.
- Jorsater and van Moorsel 1995 - High Resolution Neutral Hydrogen Observations of the Barred Spiral Galaxy NGC 1365
- Kurono, Morita, Kamazaki 2009 - A Study of Combining Technique of Single-Dish and Interferometer Data: Imaging Simulations and Analysis
- Koda et al. 2011 = CO(J = 1-0) Imaging of M51 with CARMA and the Nobeyama 45 m Telescope
- Cotton 2017 - Fourier Plane Image Combination by Feathering
- Hoffman & Kepley 2018 - Correcting ALMA 12-m Array Data for Missing Short Spacings Using the Green Bank Telescope
- Koda et al. 2019 - Total Power Map to Visibilities (TP2VIS): Joint Deconvolution of ALMA 12m, 7m, and Total Power Array Data
- Rau et al. 2019 -A Joint Deconvolution Algorithm to Combine Single-dish and Interferometer Data for Wideband Multiterm and Mosaic Imaging
- Mason 2020 - Imaging Spatially Extended Objects with Interferometers: Mosaicking and the Short Spacing Correction
- Miyoshi et al. 2024 - An Independent Hybrid Imaging of Sgr A∗ from the Data in EHT 2017 Observations
