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''This guide is applicable to CASA version 4.0.  For simulation instructions for older versions of CASA see: [[Einstein-Face (CASA 3.4)]].''
''This guide is applicable to CASA version 4.0.  For simulation instructions for older versions of CASA see: [[Einstein-Face (CASA 3.4)]].''


'''Simulations using non-science images: the face of Einstein'''
==Simulations using non-science images: the face of Einstein==


simobserve can be used to simulate any digitized image, while simanalyze can be used to image and analyze simulated data. These toy models can be particularly useful for examining the effects of varying uv-coverage on image fidelity if the "truth" model is a familiar object or image. In this example we use the face of Albert Einstein.
'''simobserve''' can be used to simulate an observation of any digitized image, and '''simanalyze''' can be used to image and analyze the data from that simulated observation. These toy models can be particularly useful for examining the effects of varying uv-coverage on image fidelity. In this example we use a photo of Albert Einstein as the input model.


In this example, we grabbed a JPG image of Einstein's face from the internet.  simobserve likes input images in fits format, so we used Gimp to convert the file.  (Instructions to [[Convert jpg to fits]].)  The original image is shown below.  You can grab the converted fits file here from the [http://casaguides.nrao.edu/index.php?title=Sim_Inputs Simulation Inputs CASA Guide page].
We start with a JPG image of Einstein's face.  '''simobserve''' likes input images in FITS format, so we used Gimp to convert the file.  (Instructions to [[Convert jpg to fits]].)  The original image is shown below.  You can grab the converted FITS file directly [http://safe.nrao.edu/wiki/pub/ALMA/InputExamples/Einstein.fits here] or from the [http://casaguides.nrao.edu/index.php?title=Sim_Inputs Simulation Inputs CASA Guide page].


[[File:Einstein.jpg]]
[[File:Einstein.jpg]]


'''Prepare inputs for simobserve and simanalyze'''
==Prepare inputs for simobserve==


Start with the 10min full science observation. Inputs to simobserve and simanalyze are given below. The integration time is set much longer than realistic (300s, compared to 1-10s in practice) to speed the computation. The map spacing is set to ensure that only one pointing is observed. The image scale is chosen to ensure good sampling of the beam, and the observation is pointed near the Chandra Deep Field South:
We will start with a 10 minute observation using one of the "full science" antenna configurations. Inputs to '''simobserve''' and '''simanalyze''' are given below. The integration time (300 s) is set much longer than we would see in a real ALMA observation (1 to 10 s) to speed the computation. We set the angular size of the image to 0.043 arcsecond pixels with the '''incell''' parameter, ensuring that only one pointing is needed to map the whole image area.   The image coordinates are set to a position near the Chandra Deep Field South.


<source lang="python">
<source lang="python">
Line 31: Line 31:
pointingspacing = '1arcmin'
pointingspacing = '1arcmin'
graphics = 'both'
graphics = 'both'
obsmode = 'int'
antennalist =  "alma.out08.cfg"
totaltime = '600s'
thermalnoise = ""
simobserve()
</source>
</source>


Antenna configuration:
==An aside about antenna configurations==
ALMA antenna configuration files are stored in a directory found in the CASA installation.  
 
Details on configuration choices are given in the M51 simulation guide [http://casaguides.nrao.edu/index.php?title=M51_at_z_%3D_0.1_and_z_%3D_0.3_(CASA_3.4)].
ALMA will observe with many different array configurations.  In our simulations we can specify which antenna configuration to use with the '''antennalist''' parameter.  Your CASA distribution comes with a set of antenna configurations.  You can locate the configurations like so:
 
CASA <49>: print os.getenv('CASAPATH').sp lit(' ')[0] + '/data/alma/simmos/'
/usr/lib64/casapy/stable/data/alma/simmos/
 
CASA <50>: ls /usr/lib64/casapy/stable/data/alma/simmos/
aca_cycle1.cfg            alma.out05.cfg  alma.out21.cfg  pdbi-c.cfg
aca.i.cfg                alma.out06.cfg  alma.out22.cfg  pdbi-d.cfg
aca.ns.cfg                alma.out07.cfg  alma.out23.cfg  sma.compact.cfg
aca.tp.cfg                alma.out08.cfg  alma.out24.cfg  sma.compact.n.cfg
alma.cycle0.compact.cfg  alma.out09.cfg  alma.out25.cfg  sma.extended.cfg
alma.cycle0.extended.cfg  alma.out10.cfg  alma.out26.cfg  sma.subcompact.cfg
alma_cycle1_1.cfg        alma.out11.cfg  alma.out27.cfg  sma.vextended.cfg
alma_cycle1_2.cfg        alma.out12.cfg  alma.out28.cfg  vla.a.cfg
alma_cycle1_3.cfg        alma.out13.cfg  carma.a.cfg    vla.b.cfg
alma_cycle1_4.cfg        alma.out14.cfg  carma.b.cfg    vla.bna.cfg
alma_cycle1_5.cfg        alma.out15.cfg  carma.c.cfg    vla.c.cfg
alma_cycle1_6.cfg        alma.out16.cfg  carma.d.cfg    vla.cnb.cfg
alma.out01.cfg            alma.out17.cfg  carma.e.cfg    vla.d.cfg
alma.out02.cfg            alma.out18.cfg  meerkat.cfg    vla.dnc.cfg
alma.out03.cfg            alma.out19.cfg  pdbi-a.cfg      WSRT.cfg
alma.out04.cfg            alma.out20.cfg  pdbi-b.cfg


==Imaging with simanalyze==
Now back to the simulation.  Execute '''simanalyze''' with the following parameters:


<source lang="python">
<source lang="python">
obsmode = 'int'
antennalist =  "alma.out08.cfg"
totaltime = '600s'
thermalnoise = ""
simobserve()
# simanalyze is used to take the simulation output, and provide helpful plots
# simanalyze is used to take the simulation output, and provide helpful plots
default('simanalyze')
default('simanalyze')
Line 62: Line 84:
[[File:einstein_fs_cfg8_10min.gif]]
[[File:einstein_fs_cfg8_10min.gif]]


Now we repeat for an 1hr observation:
==Experimenting with longer observations and different array configurations==
 
We can repeat the simulation, this time for an 1 hr observation:


<source lang="python">
<source lang="python">
Line 76: Line 100:
</source>
</source>


Which should look something like: [[File:einstein_fs_cfg8_1hr.gif]]
The result should look something like: [[File:einstein_fs_cfg8_1hr.gif]]


Finally, two Early Science simulations, using the extended configuration. One 10min simulation:
Next, we can see the result of observing this model image with the Early Science extended antenna configuration. First we use a 10 min observation:


<source lang="python">
<source lang="python">
Line 94: Line 118:
which looks like this: [[File:einstein_es_extended_10min.gif]]
which looks like this: [[File:einstein_es_extended_10min.gif]]


and a 4hr simulation:
We see the limited ability of the Early Science configuration for imaging complex structure with short observations.  Next we can try a 4 hr observation, using the same configuration.


<source lang="python">
<source lang="python">
Line 109: Line 133:
which looks like this: [[File:einstein_es_extended_4hr.gif]]
which looks like this: [[File:einstein_es_extended_4hr.gif]]


'''Further experiments:'''


Some more things to try:
Going back to one of the Full Science, compact array configurations, we can simulate an 8 hr observation to show the improvement obtained by obtained by filling in uv-coverage.


An 8hr observation shows the improvement obtained by obtaining fuller uv-coverage in the full science array:
<source lang="python">
<source lang="python">
tget(simobserve)
tget(simobserve)
Line 128: Line 150:
Which should look something like: [[File:einstein_fs_cfg8_8hr.gif]]
Which should look something like: [[File:einstein_fs_cfg8_8hr.gif]]


An attempt to make a higher resolution image shows what happens when short spacings are missing in the configuration.
We can simulate an observation using a Full Science, extended configuration to achieve high spatial resolution.  In this case there are no short spacings and we will see the drastic effects of missing those.  We select configuration 16, which has a 0.17x0.15 beam, still better than Nyquist sampling of the model image (which has 0.043" pixels).
Configuration 16 has a 0.17x0.15 beam, still better than Nyquist sampling of the model image (which has 0.043" pixels).
However, the lack of short spacings in the configuration leads to poorly sampled structure on large spatial scales. In practice, one would need to combine these observations with a set in a more compact configuration (such as 8) to sample both the large
and small spatial structures.


<source lang="python">
<source lang="python">
Line 146: Line 165:


The result is: [[File:einstein_fs_cfg16_1hr.gif]]
The result is: [[File:einstein_fs_cfg16_1hr.gif]]
The lack of short spacings in the configuration leads to poorly sampled structure on large spatial scales. In practice, one would need to combine observations using this configuration with a set in a more compact configuration (such as 8) to sample both the large and small spatial structures.


{{Simulations Intro 4.0}}
{{Simulations Intro 4.0}}
{{Checked 4.0.0}}
{{Checked 4.0.0}}

Latest revision as of 22:07, 9 November 2012

↵ Simulating Observations in CASA

This guide is applicable to CASA version 4.0. For simulation instructions for older versions of CASA see: Einstein-Face (CASA 3.4).

Simulations using non-science images: the face of Einstein

simobserve can be used to simulate an observation of any digitized image, and simanalyze can be used to image and analyze the data from that simulated observation. These toy models can be particularly useful for examining the effects of varying uv-coverage on image fidelity. In this example we use a photo of Albert Einstein as the input model.

We start with a JPG image of Einstein's face. simobserve likes input images in FITS format, so we used Gimp to convert the file. (Instructions to Convert jpg to fits.) The original image is shown below. You can grab the converted FITS file directly here or from the Simulation Inputs CASA Guide page.

Prepare inputs for simobserve

We will start with a 10 minute observation using one of the "full science" antenna configurations. Inputs to simobserve and simanalyze are given below. The integration time (300 s) is set much longer than we would see in a real ALMA observation (1 to 10 s) to speed the computation. We set the angular size of the image to 0.043 arcsecond pixels with the incell parameter, ensuring that only one pointing is needed to map the whole image area. The image coordinates are set to a position near the Chandra Deep Field South. 

# simobserve is used to simulate the observation
default('simobserve')
# Full science, configuration 08, 10 minutes
project = 'fs_cfg8_10m'  
skymodel = 'Einstein.fits'
indirection = 'J2000 03h30m00 -28d00m00'
incell = '0.043arcsec'
incenter = '245GHz'
inwidth = '2GHz'
setpointings = T
integration = '300s'
mapsize = ['1arcmin','1arcmin']
maptype = 'hexagonal'
pointingspacing = '1arcmin'
graphics = 'both'
obsmode = 'int'
antennalist =  "alma.out08.cfg"
totaltime = '600s'
thermalnoise = ""
simobserve()

An aside about antenna configurations

ALMA will observe with many different array configurations. In our simulations we can specify which antenna configuration to use with the antennalist parameter. Your CASA distribution comes with a set of antenna configurations. You can locate the configurations like so: 

CASA <49>: print os.getenv('CASAPATH').sp lit(' ')[0] + '/data/alma/simmos/'
/usr/lib64/casapy/stable/data/alma/simmos/ 

CASA <50>: ls /usr/lib64/casapy/stable/data/alma/simmos/
aca_cycle1.cfg            alma.out05.cfg  alma.out21.cfg  pdbi-c.cfg
aca.i.cfg                 alma.out06.cfg  alma.out22.cfg  pdbi-d.cfg
aca.ns.cfg                alma.out07.cfg  alma.out23.cfg  sma.compact.cfg
aca.tp.cfg                alma.out08.cfg  alma.out24.cfg  sma.compact.n.cfg
alma.cycle0.compact.cfg   alma.out09.cfg  alma.out25.cfg  sma.extended.cfg
alma.cycle0.extended.cfg  alma.out10.cfg  alma.out26.cfg  sma.subcompact.cfg
alma_cycle1_1.cfg         alma.out11.cfg  alma.out27.cfg  sma.vextended.cfg
alma_cycle1_2.cfg         alma.out12.cfg  alma.out28.cfg  vla.a.cfg
alma_cycle1_3.cfg         alma.out13.cfg  carma.a.cfg     vla.b.cfg
alma_cycle1_4.cfg         alma.out14.cfg  carma.b.cfg     vla.bna.cfg
alma_cycle1_5.cfg         alma.out15.cfg  carma.c.cfg     vla.c.cfg
alma_cycle1_6.cfg         alma.out16.cfg  carma.d.cfg     vla.cnb.cfg
alma.out01.cfg            alma.out17.cfg  carma.e.cfg     vla.d.cfg
alma.out02.cfg            alma.out18.cfg  meerkat.cfg     vla.dnc.cfg
alma.out03.cfg            alma.out19.cfg  pdbi-a.cfg      WSRT.cfg
alma.out04.cfg            alma.out20.cfg  pdbi-b.cfg

Imaging with simanalyze

Now back to the simulation. Execute simanalyze with the following parameters: 

# simanalyze is used to take the simulation output, and provide helpful plots
default('simanalyze')
project = 'fs_cfg8_10m'
image = T
vis = project+'.alma.out08.ms'  
imsize = [300,300]
cell = '0.043arcsec'
niter = 2000
weighting = 'natural'
analyze = F
overwrite = T
simanalyze()

The output image should have a clean beam of 0.62"x0.56" and look something like:

Experimenting with longer observations and different array configurations

We can repeat the simulation, this time for an 1 hr observation: 

tget(simobserve)
# Full science, configuration 08, 1 hour
project = 'fs_cfg8_1hr'  
totaltime = '3600s'
simobserve()
tget(simanalyze)
project = 'fs_cfg8_1hr'
vis = project+'.alma.out08.ms'
simanalyze()

The result should look something like:

Next, we can see the result of observing this model image with the Early Science extended antenna configuration. First we use a 10 min observation: 

tget(simobserve)
project = 'es_extended_10m'
antennalist = "alma.cycle0.extended.cfg"
totaltime = '600s'
simobserve()
tget(simanalyze)
project = 'es_extended_10m'
vis = project+'.alma.cycle0.extended.ms'
simanalyze()

which looks like this:

We see the limited ability of the Early Science configuration for imaging complex structure with short observations. Next we can try a 4 hr observation, using the same configuration. 

tget(simobserve)
project = 'es_extended_4hr'
totaltime = '14400s'
simobserve()
tget(simanalyze)
project = 'es_extended_4hr'
vis = project+'.alma.cycle0.extended.ms'
simanalyze()

which looks like this:


Going back to one of the Full Science, compact array configurations, we can simulate an 8 hr observation to show the improvement obtained by obtained by filling in uv-coverage. 

tget(simobserve)
antennalist = "alma.out08.cfg"
project = 'fs_cfg8_8hr'
totaltime = '28800s'
simobserve()
tget(simanalyze)
project = 'fs_cfg8_8hr'
vis = project+'.alma.out08.ms'
simanalyze()

Which should look something like:

We can simulate an observation using a Full Science, extended configuration to achieve high spatial resolution. In this case there are no short spacings and we will see the drastic effects of missing those. We select configuration 16, which has a 0.17x0.15 beam, still better than Nyquist sampling of the model image (which has 0.043" pixels). 

tget(simobserve)
antennalist = "alma.out16.cfg"
project = 'fs_cfg16_1hr'
totaltime = '3600s'
simobserve()
tget(simanalyze)
project = 'fs_cfg16_1hr'
vis = project+'.alma.out16.ms'
simanalyze()

The result is:

The lack of short spacings in the configuration leads to poorly sampled structure on large spatial scales. In practice, one would need to combine observations using this configuration with a set in a more compact configuration (such as 8) to sample both the large and small spatial structures.

↵ Simulating Observations in CASA

Last checked on CASA Version 4.0.0.

Retrieved from "https://casaguides.nrao.edu/index.php?title=Einstein-Face_(CASA_4.0)&oldid=13586"