Introduction to CASA and Basic Data Reduction Overview Crystal Brogan ALMA CASA Subsystem Scientist...

Introduction to CASA and Basic Data

Reduction Overview

Crystal BroganALMA CASA Subsystem Scientist

(NRAO/NAASC)

With lots of help from Steve Myers (Project Scientist)

http://www.cv.nrao.edu/~cbrogan/CASATutorial_Santiago.pdf



CASA Tutorial – Santiago Jan. 26, 20092

Beta Release StatusCASA Status:

in Beta Release since October 2007

Roughly biannual “patches”

register at my.nrao.edu , no password for download

Current Release: Beta Release Patch 2.3.1

Next Release @June ‘09


Getting User SupportFirst stop: Read the Manual!

CASA Home: http://casa.nrao.edu

Cookbook, and “help” within CASA

CASA Helpdesk & Support Cadres

“Helpdesk” under my.nrao.edu (will migrate to more user friendly helpdesk in future)

submit Issues


Other Information

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Some ALMA User software demos: almasimmos OT, and splatalogue:http://www.cv.nrao.edu/~jstoke/Demo_Videos/

The Eleventh Synthesis Imaging Workshop:Calibration, Imaging, Mosaicing, Millimeter Interferometry etc.:http://www.aoc.nrao.edu/events/synthesis/2008/lectures08.html


CASA DocumentationCASA Analysis cookbook (draft):

http://casa.nrao.edu/Doc/Draft/Cookbook/casa_cookbook.pdf

CASA User Reference Manual: http://casa.nrao.edu/docs/casaref/CasaRef.html

Python: http://python.org/doc (e.g., see Tutorial for novices)

IPython: http://ipython.scipy.org/moin/Documentation

matplotlib: http://matplotlib.sourceforge.net/


Tutorial Contents

I - Interface

II - Tasks & Tools

III - Data in CASA

IV - Data Processing Workflow

V - Example Use Cases


I - InterfaceDocumentation

IPython & Python

Crashes & Recovery

CASA help

CASA task interface

Functionality


I - CASA InterfaceIPython

shell accessautoparenthesis (autocall)command historysession logging (ipython.log, casapy.log)numbered input/outputhistory/searching

Pythonsee standard manuals, Cookbook Appendix D


Python Pointersto run a .py script:execfile(‘<scriptname>’)

example: execfile(‘ngc5921_demo.py’)

indentation matters!be careful when doing cut-and-paste to Python

cut a few (4-6) lines at a time

variables are global when using task interface!tasknames are objects (not variables)


Part II - Tasks & Tools

overview of tasks and tools in CASA

introduction to the task interface

how to get help on tasks and tools

how tools underly the tasks

how to run tasks asynchronously


Tasks and tools in CASA

Tasks - high-level functionality

function call or parameter handling interface

these are what you should use in tutorial

Tools - complete functionality

tool.method calls, used by tasks

sometimes shown in tutorial scripts

CASA Tutorial -- 7 October 2008

Key TasksTask list:

may be different in test and release versions

asap_init to load single-dish tasks and tools


Task Executiontwo ways to invoke:

call from Python as functions with arguments

taskname( arg1=val1, arg2=val2, ... )

unspecified parameters will be defaulted (globals not used)

use standard tasking interface

use global variables for task parameters

see Chapter 1.3 in Cookbook


Task Interfacestandard tasking interface

use parameters set as global Python variables

set <param> = <value> (e.g. vis = ‘ngc5921.demo.ms’ )

parameter manipulation commandsusing inp , default , saveinputs , tget

execute<taskname> or go ( e.g. clean() )

return valuessome tasks return Python dictionaries, e.g. myval=imval()


Task Interfaceexamine task parameters with inp :


Expandable Parameters

boldface parameter are expandable


Parameter Checkingsanity checks of parameters in inp :

erroneousvalues in red


Help on TasksIn-line help <taskname> command:


Tools in CASACASA Toolkit underneath tasks

core AIPS++ code (mostly in C++)

tools are functions

call from casapy as <tool>.<method>()

default tool objects are pre-constructed

e.g. imager (im) , calibrater (cb), ms (ms) , etc. (see toolhelp)


CASA Tool Listlist of default tools from toolhelp :

tools described in the CASA User Reference Manual:

http://casa.nrao.edu/docs/casaref/CasaRef.html


Asynchronous Tasksrun tasks in background

set parameter async=True

runs as separate process

use taskmanager tool (tm) to monitor (Cookbook Chapter 1.3.2)

messages will appear in logger in the asynchronous order

useful to avoid known problems (e.g. exportfits / importfits)

cannot retrieve return values from async tasks


III - Data in CASA

data in CASA are stored in tables

interferometry data in Measurement Sets (MS)

also tables for images and calibration tables

formats and structures defined in CASA memos

accessed through table and ms tools

row and column structure is logical not physical


The Measurement Set

the MS is a directory on disk

the MAIN table in table.* files

also contains sub-tables

e.g. FIELD, SOURCE, ANTENNA, etc.

sub-tables are sub-directories

to copy must cp -rf to get contents

WARNING: moving a MS can break cal-table dependencies


Example MSExample: ls ngc5921.usecase.ms

ls ngc5921.usecase.ms/FIELD


MAIN Table ContentsExample using task browsetable:


More MS Detailsoften will refer to “rows” & “columns”

rows = integrations in spectral windows

columns = time, u, v, w, DATA, weights, etc.

• there are “scratch columns”• created when needed - can bloat the MS

• but (usually) less space than scratch copy of MS!

• CORRECTED_DATA , MODEL_DATA, IMAGING_WEIGHTS


Data Selection• standard MS selection syntax

• see Chapter 2.5 of CASA Cookbook

• syntax detailed in CASA Memo

• http://casa.nrao.edu/Memos/msselection/index.html


Selection Example• standard selection parameters

• e.g. for task gaincal:

• field and spw common standard selections

• expandable selectdata with other selections as sub-parameters


Selection Syntax• see Chapter 2.5 of Cookbook

• field - string with source name or field ID

• can use ‘*’ as wildcard, first checks for name, then ID

• example: field = ‘1331+305’ ; field = ‘3C*’ ; field = ‘0,1,4~5’

• spw - string with specwindow ID plus channels

• use ‘:’ as separator of spw from optional channelization

• use ‘^’ as separator of channels from step/width

• example: spw = ‘0~2’ ; spw = ‘1:10~30’ ; spw = ‘2~5:5~54^5’


Selection Syntax

• see Chapter 2.5 of Cookbook

• antenna - string with antenna name or ID

• first check for name, then ID (beware VLA name 1-27, ID 0-26)

• example: antenna = ‘1~5,11’ ; antenna = ‘VA*’

• timerange - string with date/time range

• specify ‘T0~T1’ , missing parts of T1 default to T0, can give ‘T0+dT’

• example: timerange = ‘2007/10/16/01:00:00~06:30:00’


IV - Reduction Philosophy

• data is imported into MS• keep original DATA inviolate

• calibration is carried in cal-tables• will discuss cal-table philosophy later

• imaging creates images from MS• feed back model images for self-calibration


Data Philosophy• Original data kept intact by default

• DATA (and WEIGHT) columns

• do not change in original MS

• user can use toolkit to mess with these also

• MS “scratch columns” are used• used for adjusted data and model visibilities

• BETA: user must keep track ; Post-Beta: hide table and MS state


CASA Workflow Chart

• workflow:• import

• examine / flag

• calibrate

• image

• analyze


Workflow Steps• follow rough workflow path:

• import data (from UVFITS or archive formats)

• examine data and edit out bad visibilities

• calibrate data (through cal-tables)

• image data (various deconvolution methods)

• image and uv analysis (e.g. moments)

• data and image visualization (whenever needed)


Import Data

• see Chapter 2 of Cookbook• task importuvfits

• UVFITS data (e.g. from AIPS)

• task importvla

• VLA “export” format (e.g. from archive)

• task importasdm

• ALMA data format (also EVLA eventually)


Data Examination• see Chapter 3 of Cookbook

• task listobs

• list summary of MS

• task plotxy

• x-y line plots

• task browsetable (casabrowser outside casapy)

• Qt browser for tables (e.g. MS and images)

• casaviewer - view raster images of MS


Flagging• see Chapter 3 of Cookbook

• task plotxy

• draw boxes around points and flag

• casaviewer ( task viewer )

• draw boxes on raster and flag (modes similar to AIPS TVFLG)

• task flagdata - manual flagging, clipping, etc.

• task flagmanager - backup & restore flags


Task plotxy• use plotxy for simple data plots &

flagging

before (left) and after (right) editing and calibration


Task plotxy• interactive plotting and flagging

• averaging• timebin, width (time & channel averaging)

• iteration • iteration = ‘antenna’ , ‘field’ , ‘time’ , ‘spw’ (or combinations)

• GUI controls• buttons for MarkRegion , Flag , Unflag , Locate , Next , Quit

• plot options• use subplot = yxn to set multi-panels (e.g. subplot=311)

• controls for fontsize , markersize , other options


Example inputs• set up to plot amp vs. time


Editing Jupiter• isolate blocks of bad data and remove

single bad scan on Jupiter both spw , all antennas


MS viewing• the casaviewer can handle MS (e.g.

TVFLG)


Calibration

• see Chapter 4 of Cookbook• prior calibration

• solving

• calibration table manipulation

• calibration table examination

• application to data


Calibration• workflow:

• input dataset

• solving => tables

• incremental

• accumulation

• application


Calibration Tables• calibration tables are exchanged between the calibration

tasks:


Calibration Tasks

• prior calibration: setjy

• solvers: gaincal, bandpass, polcal, blcal, fringecal

• manipulation: plotcal, accum, smoothcal

• application: applycal, split

• other: uvcontsub, uvmodelfit


Parameterizations• prior calibration:

• gaincurve: VLA-only for now

• opacity: single tau value (for now)

• gaincal: • gaintype=’G’ (solution per solint time slot)

• gaintype=’GSPLINE’

• bandpass: • bandtype=’B’ (solution per channel)

• bandtype=’BPOLY’

• No time-dependence yet


Calibration Processing• setjy (or ft)

• specify a model image (for selfcal or resolved cals)

• Except for uv-range does not yet handle planets, VLA calibrator models are available

• smoothcal

• Mean, median smoothing of cal tables

• accum

• calibration re-sampling

• Incremental/cumulative calibration


Imaging• see Chapter 5 of Cookbook

• task clean

• single-field cleaning, variety of algorithms

• mosaicing using uv-gridder (uv-plane mosaicing on single image)

• interactive and non-interactive operation

• task widefield - widefield imaging prototype

• w-projection and faceting

• task feather - combine single-dish and uvMS


Interactive CLEAN• clean has interactive

mode

• interactive=True

• uses the viewer

• can change cycles during cleaning

• draw boxes and polygon regions for masking


Cube Channelization• When using mode=‘channel’

• select spectral windows of the data, e.g. spw=‘0,1’

• the width is then set in units of first data spw channel width


Imaging spectral cubes

• Other cube considerations... • Averaging of data channel in imaging is

always ‘mfs’ style…i.e gridded with different (u,v)

• If a mode=’velocity’ cube is chosen, then data will be gridded into LSRK bins

• If simple data averaging is needed split is recommended


Mosaicing in clean• use imagermode=’mosaic’

• deconvolution on “Sault” (constant noise) image• User can set make PB corrected image at end

• Heterogeneous now available if multiple antenna diameters are present

• primary beam coverage x Sky in image plane• Convolution with aperture cross-correlation function in uv-domain

• Advantages of doing mosaicing in uv• Faster and apply correct beam transform for each baseline (e.g.

different dish size, change of pb with frequency, beam errors etc.)

• Parameter “ftmachine” sets this• Possibilities are 'ft' or 'mosaic’

• For poorly sampled mosaics, ‘ft’ is better


More clean• Mosaicing parameters continued:

• mosweight: important/different for uniform and Brigg's weighting scheme. Use per field weighting if mosweight=True

• Cotton-Scwab major cycles• imagermode=’csclean’ or ‘mosaic’• returns to residual visibilities at major

cycles• cyclefactor : controls major cycle depth

• larger => more major cycles (clean will take longer)

• to a lesser extent cyclespeedup (for a non-converging clean)


Image Display• The viewer

• Cookbook Chapter 7


Image Analysis• see Chapter 6 of Cookbook

• task imhead - get and change image header information

• task immoments - computes moment images of spectral cube

• task immath - combine images and do image math

• using Lattice Expression Language (LEL)

• also: Spectral Index, Linear Polarized Intensity and Angle

• task imstat - return statistics on regions of image

• task imval - return values for pixel or region of image

• task imfit - fit a 2D Gaussian to the image


Task immoments• use viewer to look at NGC5921 moment

images

Overlay contours of moment 1 (velocity) on moment 0(intensity)


V - Scripts & Data• Tutorial Home Page

• http://www.cv.nrao.edu/~cbrogan/2009126.htm

• CASA Scripts & Data Page• http://casa.nrao.edu/casatraining.shtml


Crashes & Recovery• Beta Release still has crash modes

• plotting in particular (plotxy and plotcal) can get a segmentation fault

• If in doubt, exit and restart casapy• get rid of orphaned loggers

• you may have to use ps to find rogue casapy processes and kill them


Beta Disclaimer

• This really is a Beta Release!• still missing functionality

• there are known failure modes

• Improvements in upcoming patches

• move towards handling ALMA/EVLA “use cases”

• better handling of calibration


Next Steps

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• Everyone run the fast VLA script ngc5921_demo.py to ensure things are working properly

• Then you can run through the extensively annotated script for reduction of BIMA SONG data: ngc4926_tutorial.py

• Or for the VERY brave and patient…


New ‘Beta’ SMA Data Reduction Script

Available

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• http:www.cv.nrao.edu/~cbrogan/sma_reduction

• Many of the slow aspects of running this script: plotting, flagging, scratch column creation are actively being worked on this cycle – patience is required

• CARMA reduction is similarly possible via miriad: uvfits (be sure to do linelength correction first) to casapy: importuvfits.

• The NAASC has formed a collaboration with the U. of Maryland to write a “CARMA filler” – hopefully finished by end of summer

Introduction to CASA and Basic Data Reduction Overview Crystal Brogan ALMA CASA Subsystem Scientist...

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Transcript of Introduction to CASA and Basic Data Reduction Overview Crystal Brogan ALMA CASA Subsystem Scientist...