Sunspot Band6 SingleDish for CASA 6.5.4: Difference between revisions

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[[Category:ALMA]][[Category:Sun]][[Category:Continuum]][[Category:Calibration‏‎]]
[[Category:ALMA]][[Category:Sun]][[Category:Continuum]][[Category:Calibration‏‎]]
{{checked_6.5.4}}


==Overview==
==Overview==
This portion of the Sunspot Band6 CASA Guide for CASA 5.1 will cover the calibration and imaging of the data obtained with the TP array.
{{CARTA_6.5.4}}


From next, we will show all commands for the calibration and be creating the map of a full Sun with Band6.
This portion of the [[Sunspot Band6]] guide will cover the calibration and imaging of the data obtained with the Total Power array. We create the map of a full Sun with Band6.


==Import the data and split into the files for each antenna==
==Import the Data==
We assume that you already complete the calibration of the visibility data along the Sunspot Band6 Calibration for CASA 5.1, and you are working in the directory ‘Sunspot_Band6_UncalibratedData’. After starting CASA, we start by defining the directory name of the ASDM and Measurement set for the calibration.
If you completed the calibration of the visibility data following [[Sunspot_Band6_Calibration_for_CASA_6.5.4]], we assume you are working in the directory 'Sunspot_Band6_UncalibratedData'. After starting CASA, we start by defining the directory name of the ASDM and Measurement Set.


<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
sduid ='uid___A002_Xae00c5_X2e6b'
sd_asdm = 'uid___A002_Xae00c5_X2e6b' # single dish ASDM file
sdmsd = sduid + '.ms'
sd_mso = sd_asdm+'.ms'               # measurement set original
sdmsc = sdmsd + '.split.cal'
sd_msc = sd_asdm+'.ms'+'.split.cal' # measurement set calibrated
ant='PM03'
</source>


import numpy as np
First we will need to convert the data to MS format. This is done with the task {{importasdm_6.5.4}}.
</source>
In the tutorial, we will use the data obtained with the PM03 antenna.  


Before we can proceed to the calibration, we will need to convert those data to the CASA MS format. This is done simply with the task {{importasdm}}.
<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
importasdm(asdm=sduid,with_pointing_correction=True, verbose=True,vis=sdmsd,overwrite=True)
importasdm(asdm=sd_asdm, with_pointing_correction=True, verbose=True, vis=sd_mso, overwrite=True)
</source>
</source>
The usual first step is then to get some basic information about the data.  
 
Then we get some basic information about the data using {{listobs_6.5.4}}.
 
<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
listobs(sdmsd, listfile = sduid + '.listobs')
listobs(vis=sd_mso, listfile=sd_asdm+'.listobs.txt')
</source>
</source>
<pre style="background-color: #fffacd;">
  Observer: lknee    Project: uid://A002/X5ca254/X1 
Observation: ALMA
Data records: 6336672      Total elapsed time = 578.744 seconds
  Observed from  18-Dec-2015/20:11:02.7  to  18-Dec-2015/20:20:41.4 (UTC)
  ObservationID = 0        ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName            nRows    SpwIds  Average Interval(s)    ScanIntent
  18-Dec-2015/20:11:02.7 - 20:11:52.8    1      0 Sun                    276480  [0,1,2,3,4,5,6,7]  [0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_ATMOSPHERE#REFERENCE]
              20:12:14.4 - 20:20:41.4    2      0 Sun                    6060192  [0,1,2,3]  [0.001, 0.001, 0.001, 0.001] [OBSERVE_TARGET#OFF_SOURCE,OBSERVE_TARGET#ON_SOURCE]
          (nRows = Total number of rows per scan)
Fields: 1
  ID  Code Name                RA              Decl          Epoch  SrcId      nRows
  0    none Sun                17:44:08.590935 -23.23.12.63602 ICRS    0        6336672
Spectral Windows:  (8 unique spectral windows and 1 unique polarization setups)
  SpwID  Name      #Chans  Frame  Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs 
  0      BB_1#SQLD      1  TOPO  230000.000  2000000.000  2000000.0 230000.0000        1  XX  YY
  1      BB_2#SQLD      1  TOPO  232000.000  2000000.000  2000000.0 232000.0000        2  XX  YY
  2      BB_3#SQLD      1  TOPO  246000.000  2000000.000  2000000.0 246000.0000        3  XX  YY
  3      BB_4#SQLD      1  TOPO  248000.000  2000000.000  2000000.0 248000.0000        4  XX  YY
  4      BB_1#SQLD      1  TOPO  219559.000  2000000.000  2000000.0 219559.0000        1  XX  YY
  5      BB_2#SQLD      1  TOPO  219559.000  2000000.000  2000000.0 219559.0000        2  XX  YY
  6      BB_3#SQLD      1  TOPO  219559.000  2000000.000  2000000.0 219559.0000        3  XX  YY
  7      BB_4#SQLD      1  TOPO  219559.000  2000000.000  2000000.0 219559.0000        4  XX  YY
Sources: 8
  ID  Name                SpwId RestFreq(MHz)  SysVel(km/s)
  0    Sun                0    -              -           
  0    Sun                1    -              -           
  0    Sun                2    -              -           
  0    Sun                3    -              -           
  0    Sun                4    -              -           
  0    Sun                5    -              -           
  0    Sun                6    -              -           
  0    Sun                7    -              -           
Antennas: 3:
  ID  Name  Station  Diam.    Long.        Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)       
                                                                    East        North    Elevation              x              y              z
  0    PM02  T702      12.0 m  -067.45.18.6  -22.53.24.1        -23.6242    -582.3080      22.0682  2225042.276282 -5440125.514185 -2481574.899005
  1    PM03  T701      12.0 m  -067.45.18.8  -22.53.22.2        -29.1260    -522.7876      22.2057  2225045.996268 -5440149.142221 -2481520.118897
  2    PM04  T703      12.0 m  -067.45.16.2  -22.53.23.9        42.8802    -575.6911      21.7765  2225104.701297 -5440102.471920 -2481568.689682
</pre>


==Calibration ==
==Calibration ==
In the observation, the atmospheric calibration was done before the fast-scanning of the Sun. We use the calibration date to apply the solar data using the option ‘ps, tsys, apply’ for the ‘calmode’.
In the observation, the atmospheric calibration scan (1) was done before the fast-scanning of the Sun (2). We derive calibrations for position switching (ps) and system temperature (Tsys) from the calibration scan, and apply them to the science data using ''calmode='ps,tsys,apply''' in {{sdcal_6.5.4}}.
 
<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
sdcal(infile=sdmsd, calmode = 'ps,tsys,apply', spw='0,1,2,3')
sdcal(infile=sd_mso, calmode='ps,tsys,apply', spw='0,1,2,3')
split(vis = sdmsd, spw = '0,1,2,3', outputvis = sdmsc, scan = '2', datacolumn = 'corrected', keepflags = True)
 
split(vis=sd_mso, spw='0,1,2,3', outputvis=sd_msc, scan='2', datacolumn='corrected')
</source>
</source>
Unlike non-solar single-dish observations, the flux calibrator is not observed in solar single-dish observations in Cycle 4. Therefore, we have to calibrate the data using a correction factor which includes various antenna efficiencies of the PM antenna.  
Unlike non-solar single-dish observations, the flux calibrator is not observed in solar single-dish observations in Cycle 4. Therefore, we have to calibrate the data using a correction factor which includes various antenna efficiencies of the PM antenna.  


Based on the commissioning observations, the average correction factor of the PM antennas with Band6 is 0.862, which enables us to calibrate the observed antenna temperature to the absolute brightness temperature. We will calibrate using the value, as follows.
Based on the commissioning observations, the average correction factor of the PM antennas with Band6 is '''0.862''', which enables us to calibrate the observed antenna temperature to the absolute brightness temperature. We will create the calibration table using this value in {{gencal_6.5.4}} and apply the table with {{applycal_6.5.4}} as follows:
 
<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
gencal(vis=sdmsc,caltable=sduid+'.cal.eff.tbl', caltype='amp', spw='',parameter=[np.sqrt(0.862)])
gencal(vis=sd_msc, caltable=sd_asdm+'.cal.eff.tbl', caltype='amp', parameter=[0.862**0.5])
applycal(vis=sdmsc, gaintable=sduid+'.cal.eff.tbl')
 
applycal(vis=sd_msc, gaintable=sd_asdm+'.cal.eff.tbl')
</source>
</source>


Caution: The correction factor in this document is written in White et al. (2017)
Caution: The correction factor in this document is written in [http://arxiv.org/abs/1705.04766 White et al. (2017)]


==Imaging==
==Imaging==
[[File:sunspot_fullsun_spw3_PM03_CASA_6.5.4.png|500px|thumb|right|'''Fig. 1.''' The full-sun image in Band6 observed with PM03, spw 3. On the left is the original image and on the right is the rescaled image with raster scaling matching toggled on.]]


Here we image the full sun map of spectral window (spw) 3 obtained with the PM03 antenna using {{tsdimaging_6.5.4}}.


[[File:sunspot_fullsun1.png|thumb|right|'''Fig. 1.''' The full-sun image with Band6]]
<source lang='python'>
#In CASA
ant = 'PM03'
sd_img = sd_asdm+'.'+ant+'.StkI.Spw3'


To create the full sun map, we use the data of the spectrum window #3 obtained with the PM03 antenna. The command for the process is as follows.
import os
<source lang='python'>
os.system(f'rm -rf {sd_img}.image')
#In Casa
 
sd_img =sduid+ '.'+ant+'.StkI.Spw3'
tsdimaging(
tsdimaging(infiles = sdmsc, spw = '3', antenna =ant, field='0', nchan = 1, cell = ['3.0arcsec','3.0arcsec'], outfile = sd_img, imsize=[800,800], gridfunction = 'SF', convsupport = 6, intent ='*ON_SOURCE*', stokes = 'I')
infiles=sd_msc,
spw='3',
antenna=ant,
field='0',
nchan=1,
cell=['3.0arcsec','3.0arcsec'],
outfile=sd_img,
imsize=[800,800],
gridfunction='SF',
convsupport=6,
intent='*ON_SOURCE*',
stokes='I',
brightnessunit='K'
)
</source>
</source>
After finishing the process successfully, you can see the full-sun image as shown in Figure 1.
 
When you carry out the imaging of Band3 data, we recommend the following parameters for the {{sdimaging}} task.
<pre style="background-color: #E0FFFF;">
<source lang='python'>
When you image solar Band3 data, we recommend using cell=['6.0arcsec','6.0arcsec'] and imsize=[400,400] instead.
#In Casa
</pre>
sdimaging(infiles = sdmsc, spw = '3', antenna =ant, field='0', nchan = 1, cell = ['6.0arcsec','6.0arcsec'], outfile = sd_img, imsize=[400,400], gridfunction = 'SF', convsupport = 6, intent ='*ON_SOURCE*', stokes = 'I', ephemsrcname = 'sun')
 
After this has finished, you should now open the image in CARTA. If using NRAO machines, you can open a new terminal tab, cd to the working directory, then type:
 
<source lang='bash'>
#In bash
carta --no_browser
</source>
</source>


==Calibration (re-scaling) after Imaging ==
Copy the output URL into a browser to view your CARTA session. You can see the full-sun image as shown in Figure 1. Select and load:<br> ''uid___A002_Xae00c5_X2e6b.PM03.StkI.Spw3.image''


White et al. (2017) recommended that the standard ALMA single-dish image products be scaled to match their best determination of quiet-Sun temperatures at disk center (specifically, 5900 K averaged over an 80″-square region centered on the apparent disk center at Band 6, and 7300 K averaged over a 120″-square region centered on the apparent disk center at Band 3, with uncertainties of about 100 K). For the re-scaling, the following commands are executed.
==Rescaling after Imaging ==
 
[http://arxiv.org/abs/1705.04766 White et al. (2017)] recommended that the standard ALMA single-dish image products be scaled to match their best determination of quiet-Sun temperatures at disk center:
* 5900K averaged over an 80" square region centered on the apparent disk center at Band6
* 7300K averaged over a 120" square region centered on the apparent disk center at Band3
* uncertainties of about 100K
 
For the re-scaling in Band6, the following {{immath_6.5.4}} command is executed.


<source lang='python'>
<source lang='python'>
#In Casa
#In CASA
res = imstat(sd_img+'.image', region='circle[[400pix,400pix],80pix]')
res = imstat(sd_img+'.image', region='circle[[400pix,400pix],80pix]')
immath(imagename=sd_img+'.image', expr = 'IM0*'+str(5900./res['mean'][0]), outfile = sd_img+'.rescl')
 
os.system(f'rm -rf {sd_img}.rescl')
immath(imagename=sd_img+'.image', expr='IM0*'+str(5900./res['mean'][0]), outfile=sd_img+'.rescl')
</source>
</source>


For Band3
<pre style="background-color: #E0FFFF;">
<source lang='python'>
When you image solar Band3 data, choose region='circle[[200pix,200pix],40pix]' and expr='IM0*'+str(7300./res['mean'][0]) instead.
#In Casa
</pre>
res = imstat(sd_img+'.image', region='circle[[200pix,200pix],40pix]') 
 
immath(imagename=sd_img+'.image', expr = 'IM0*'+str(7300./res['mean'][0]), outfile = sd_img+'.rescl')
Now append the following image in CARTA:<br> ''uid___A002_Xae00c5_X2e6b.PM03.StkI.Spw3.rescl''
</source>


'''Note: All full-sun images that are included in the SV data package (ex. Sunspot_Band6_ReferenceImages.tgz) were not applied the re-scaling because the SV data were released before publishing White et al. (2017). When you use the full-sun image of the SV data for your studies, you should be done the re-scaling process. While the archive data obtained in Cycles were applied the re-scaling process.'''
<font color="red">
Note: All full-sun images that are included in the SV data package (ex. Sunspot_Band6_ReferenceImages.tgz) were not applied the re-scaling because the SV data were released before publishing [http://arxiv.org/abs/1705.04766 White et al. (2017)]. When you use the full-sun image of the SV data for your studies, you should be done the re-scaling process. While the archive data obtained in Cycles were applied the re-scaling process.
</font>

Latest revision as of 20:50, 12 April 2024

Last checked on CASA Version 6.5.4

Overview

This guide features CARTA, the “Cube Analysis and Rendering Tool for Astronomy,” which is the new NRAO visualization tool for images and cubes. The CASA viewer (imview) has not been maintained for a few years and will be removed from future versions of CASA. We strongly recommend using CARTA, as it provides a much more efficient, stable, and feature rich user experience. A comparison of the CASA viewer and CARTA, as well as instructions on how to use CARTA at NRAO, is provided in the CARTA section of the CASA docs.

This portion of the Sunspot Band6 guide will cover the calibration and imaging of the data obtained with the Total Power array. We create the map of a full Sun with Band6.

Import the Data

If you completed the calibration of the visibility data following Sunspot_Band6_Calibration_for_CASA_6.5.4, we assume you are working in the directory 'Sunspot_Band6_UncalibratedData'. After starting CASA, we start by defining the directory name of the ASDM and Measurement Set. 

#In CASA
sd_asdm = 'uid___A002_Xae00c5_X2e6b' # single dish ASDM file
sd_mso = sd_asdm+'.ms'               # measurement set original
sd_msc = sd_asdm+'.ms'+'.split.cal'  # measurement set calibrated

First we will need to convert the data to MS format. This is done with the task importasdm. 

#In CASA
importasdm(asdm=sd_asdm, with_pointing_correction=True, verbose=True, vis=sd_mso, overwrite=True)

Then we get some basic information about the data using listobs. 

#In CASA
listobs(vis=sd_mso, listfile=sd_asdm+'.listobs.txt')

   Observer: lknee     Project: uid://A002/X5ca254/X1  
Observation: ALMA
Data records: 6336672       Total elapsed time = 578.744 seconds
   Observed from   18-Dec-2015/20:11:02.7   to   18-Dec-2015/20:20:41.4 (UTC)

   ObservationID = 0         ArrayID = 0
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  18-Dec-2015/20:11:02.7 - 20:11:52.8     1      0 Sun                     276480  [0,1,2,3,4,5,6,7]  [0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_ATMOSPHERE#REFERENCE]
              20:12:14.4 - 20:20:41.4     2      0 Sun                    6060192  [0,1,2,3]  [0.001, 0.001, 0.001, 0.001] [OBSERVE_TARGET#OFF_SOURCE,OBSERVE_TARGET#ON_SOURCE]
           (nRows = Total number of rows per scan) 
Fields: 1
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    none Sun                 17:44:08.590935 -23.23.12.63602 ICRS    0        6336672
Spectral Windows:  (8 unique spectral windows and 1 unique polarization setups)
  SpwID  Name      #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs  
  0      BB_1#SQLD      1   TOPO  230000.000   2000000.000   2000000.0 230000.0000        1  XX  YY
  1      BB_2#SQLD      1   TOPO  232000.000   2000000.000   2000000.0 232000.0000        2  XX  YY
  2      BB_3#SQLD      1   TOPO  246000.000   2000000.000   2000000.0 246000.0000        3  XX  YY
  3      BB_4#SQLD      1   TOPO  248000.000   2000000.000   2000000.0 248000.0000        4  XX  YY
  4      BB_1#SQLD      1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        1  XX  YY
  5      BB_2#SQLD      1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        2  XX  YY
  6      BB_3#SQLD      1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        3  XX  YY
  7      BB_4#SQLD      1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        4  XX  YY
Sources: 8
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
  0    Sun                 0     -              -            
  0    Sun                 1     -              -            
  0    Sun                 2     -              -            
  0    Sun                 3     -              -            
  0    Sun                 4     -              -            
  0    Sun                 5     -              -            
  0    Sun                 6     -              -            
  0    Sun                 7     -              -            
Antennas: 3:
  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
                                                                     East         North     Elevation               x               y               z
  0    PM02  T702      12.0 m   -067.45.18.6  -22.53.24.1        -23.6242     -582.3080       22.0682  2225042.276282 -5440125.514185 -2481574.899005
  1    PM03  T701      12.0 m   -067.45.18.8  -22.53.22.2        -29.1260     -522.7876       22.2057  2225045.996268 -5440149.142221 -2481520.118897
  2    PM04  T703      12.0 m   -067.45.16.2  -22.53.23.9         42.8802     -575.6911       21.7765  2225104.701297 -5440102.471920 -2481568.689682

Calibration

In the observation, the atmospheric calibration scan (1) was done before the fast-scanning of the Sun (2). We derive calibrations for position switching (ps) and system temperature (Tsys) from the calibration scan, and apply them to the science data using calmode='ps,tsys,apply' in sdcal. 

#In CASA
sdcal(infile=sd_mso, calmode='ps,tsys,apply', spw='0,1,2,3')

split(vis=sd_mso, spw='0,1,2,3', outputvis=sd_msc, scan='2', datacolumn='corrected')

Unlike non-solar single-dish observations, the flux calibrator is not observed in solar single-dish observations in Cycle 4. Therefore, we have to calibrate the data using a correction factor which includes various antenna efficiencies of the PM antenna.

Based on the commissioning observations, the average correction factor of the PM antennas with Band6 is 0.862, which enables us to calibrate the observed antenna temperature to the absolute brightness temperature. We will create the calibration table using this value in gencal and apply the table with applycal as follows: 

#In CASA
gencal(vis=sd_msc, caltable=sd_asdm+'.cal.eff.tbl', caltype='amp', parameter=[0.862**0.5])

applycal(vis=sd_msc, gaintable=sd_asdm+'.cal.eff.tbl')

Caution: The correction factor in this document is written in White et al. (2017)

Imaging

Fig. 1. The full-sun image in Band6 observed with PM03, spw 3. On the left is the original image and on the right is the rescaled image with raster scaling matching toggled on.

Here we image the full sun map of spectral window (spw) 3 obtained with the PM03 antenna using tsdimaging. 

#In CASA
ant = 'PM03'
sd_img = sd_asdm+'.'+ant+'.StkI.Spw3'

import os
os.system(f'rm -rf {sd_img}.image')

tsdimaging(
infiles=sd_msc,
spw='3',
antenna=ant,
field='0',
nchan=1,
cell=['3.0arcsec','3.0arcsec'],
outfile=sd_img,
imsize=[800,800],
gridfunction='SF',
convsupport=6,
intent='*ON_SOURCE*',
stokes='I',
brightnessunit='K'
)

When you image solar Band3 data, we recommend using cell=['6.0arcsec','6.0arcsec'] and imsize=[400,400] instead.

After this has finished, you should now open the image in CARTA. If using NRAO machines, you can open a new terminal tab, cd to the working directory, then type: 

#In bash
carta --no_browser

Copy the output URL into a browser to view your CARTA session. You can see the full-sun image as shown in Figure 1. Select and load:
uid___A002_Xae00c5_X2e6b.PM03.StkI.Spw3.image

Rescaling after Imaging

White et al. (2017) recommended that the standard ALMA single-dish image products be scaled to match their best determination of quiet-Sun temperatures at disk center:

  • 5900K averaged over an 80" square region centered on the apparent disk center at Band6
  • 7300K averaged over a 120" square region centered on the apparent disk center at Band3
  • uncertainties of about 100K

For the re-scaling in Band6, the following immath command is executed. 

#In CASA
res = imstat(sd_img+'.image', region='circle[[400pix,400pix],80pix]')

os.system(f'rm -rf {sd_img}.rescl')
immath(imagename=sd_img+'.image', expr='IM0*'+str(5900./res['mean'][0]), outfile=sd_img+'.rescl')

When you image solar Band3 data, choose region='circle[[200pix,200pix],40pix]' and expr='IM0*'+str(7300./res['mean'][0]) instead.

Now append the following image in CARTA:
uid___A002_Xae00c5_X2e6b.PM03.StkI.Spw3.rescl

Note: All full-sun images that are included in the SV data package (ex. Sunspot_Band6_ReferenceImages.tgz) were not applied the re-scaling because the SV data were released before publishing White et al. (2017). When you use the full-sun image of the SV data for your studies, you should be done the re-scaling process. While the archive data obtained in Cycles were applied the re-scaling process.

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