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| == Imaging Prep Workflow ==
| | #REDIRECT [[Imaging Prep CASA 6.6.1]] |
| Commands for this page can be found in scriptForImagingPrep_template.py available on [https://github.com/aakepley/ALMAImagingScript github].
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| [[Image:Imaging_Prep_Flow.png|center|frame|1200px]] ''Use the Imaging Prep workflow to determine which sections of this guide are applicable to your dataset.'' | |
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| == Get a list of ms files to image ==
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| <figure id="Weblog_Home.png">
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| [[File:Weblog_Home.png|thumb|Figure 1: The Observation Overview page is opened with index.html.]]
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| </figure>
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| Once the calibration has run, it will produce ‘*.ms.split.cal’ files for each execution. These files contain the calibrated data. Run the following commands to grab the names of all files in the directory that have the extension ‘.ms.split.cal’ and place them into a list.
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| <source lang="python">
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| # in CASA
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| import glob
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| vislist=glob.glob('*.ms.split.cal')
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| </source>
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| == Review the Calibration ==
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| Once the list of measurement set (ms) files have been collected, review the calibration to make sure it appears reasonable. A summary of the calibration are in the qa directory of your delivered package.
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| If the dataset was pipeline calibrated, download and untar the pipeline weblog in the qa directory by using
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| <figure id="Weblog_MS_Overview.png">
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| [[File:Weblog_MS_Overview.png|thumb|Figure 2: The MS Overview gives information specific to each execution.]]
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| </figure>
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| <source lang="bash">
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| # in bash
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| for i in $(ls *.tar); do tar -xvf $i; done
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| </source>
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| <source lang="python">
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| # or in CASA
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| For i in glob.glob(‘*.tar’):
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| os.system(‘tar -xvf %s’ % (i))
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| </source>
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| Once this is done, look within the /html directory for the index.html file. This contains the html code for displaying the contents of the qa directory in a web browser. The command below opens the main page of the weblog in firefox.
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| <source lang="bash">
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| firefox index.html
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| </source>
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| <figure id="Weblog_By_Task.png">
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| [[File:Weblog_By_Task.png|thumb|Figure 3: The weblog By Task view shows an overview of QA scores.]]
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| </figure>
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| The standard opening page has three main sections: observation overview, pipeline summary, and observation summary. This is shown in Figure 1.
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| From the opening page, you can navigate to view the calibration '''By Topic''' and '''By Task''' and open observation specific information by clicking on the ms name.
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| Figure 2 shows an example of an ms summary page.
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| Figure 3 shows the '''By Task''' view. Each task can be opened to view important calibration plots, tables, and flagging statistics.
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| If the data was manually calibrated, you can review the calibration with the *.png and *.txt files in the qa directory. These display helpful plots and statistics about the calibration.
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| In addition, the tasks {{plotms}} and {{plotcal}} are useful if you need to explore the data beyond the plots that the calibration pipeline or QA2 process generates. The [https://casaguides.nrao.edu/index.php/TWHydraBand7_Calibration_4.3 TW Hydra guide] gives some nice examples showing how to use these tasks.
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| == Flag bad data (optional) ==
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| Save the original flags of each ms prior to flagging any additional data. Without this step, it will be very difficult if you need to remove flags generated later on.
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| <source lang="python">
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| # in CASA
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| for vis in vislist:
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| flagmanager(vis=vis,
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| mode='save',
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| versionname='original_flags')
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| </source>
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| The {{Plotms}} task can be used to find bad data by iterating through fields and spws for the *.ms.split.cal files in the calibrated directory. You can also use the GUI interface to iterate through spectral windows (spws).
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| Figure 4 shows an example of TW Hydra science spw 2.
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| <source lang="python">
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| # in CASA
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| fieldlist = ['3'] # change to list of science data fields to inspect
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| spwlist = ['1'] # change to list of science spws to inspect
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| for vis in vislist:
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| for field in fieldlist:
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| for spw in spwlist:
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| plotms(vis=vis,xaxis='uvwave',yaxis='amp',avgtime='3e8',
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| field=field,spw=spw)
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| raw_input("push enter to continue")
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| plotms(vis=vis,xaxis='chan',yaxis='amp',avgtime='3e8',
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| field=field,spw=spw)
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| raw_input("push enter to continue")
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| </source>
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| Use {{flagdata}} to flag bad antennas, spws, timeranges, etc. You can use the locate buttons in {{plotms}} and {{plotcal}} to identify what needs to be flagged. Locate will print the information about the selected points into the casalogger. Note that flagging in plotms is generally not recommended because a plotms crash will lose all flagging information.
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| To flag data, modify the {{flagdata}} command with the ms you want to flag and the flagging criteria. Flagging criteria can be any antenna, spw, timerange, etc. Once the data is flagged, check the flagging using plotms. The flagged data points should not appear on the plot. You can also plot the flagged data points using the "display" table on the left hand side of the {{plotms}} GUI.
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| <figure id="TWHydra_corrected_Spw2.png">
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| [[File:TWHydra_corrected_Spw2.png|thumb|Figure 4: Inspect the data.]]
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| </figure>
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| <source lang="python">
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| # in CASA
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| flagdata(vis='',mode='manual',action='apply',flagbackup=False)
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| </source>
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| If you need to restore original flags, use the following command. You will need to update the ms name in the vis parameter.
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| <source lang="python">
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| # in CASA
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| flagmanager(vis='',mode='restore',versionname='original_flags')
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| </source>
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| == Flux Equalization (optional) ==
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| If you have multiple executions taken at a similar time with the same phase calibrator, you can rescale the derived fluxes for the phase calibrator so that they are the same in all executions. If the executions are spaced by more than a day or two, it is likely that the flux of the phase calibrator has changed and thus this step is usually unnecessary. You can compare the derived flux densities of your calibrators for each execution block using the "hifa_gfluxscale" step of the weblog (pipeline calibrations) or the *.fluxscale file (manual calibration).
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| The following guides may be useful for assessing the derived fluxes of your phase cailbrators. [http://legacy.nrao.edu/alma/memos/html-memos/alma434/memo434vb.pdf ALMA Memo 434] outlines various uncertainties relevant to amplitude calibration. You can refer to the respective cycle’s Proposer’s Guide ([https://almascience.nrao.edu/documents-and-tools/cycle4/alma-proposers-guide Cycle 4]) for expected uncertainties. In addition, the [https://almascience.eso.org/sc/ ALMA Calibrator Source Catalog] can be used to compare variations detected from source monitoring.
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| <figure id="Gfluxscale table.png">
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| [[File:Gfluxscale_table.png|thumb|Figure 5: An example of the table created by hifa_gfluxscale.]]
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| </figure>
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| If you have determined flux equalization is required for your data, please file a [https://help.almascience.org Helpdesk ticket] and a NAASC staff member can assist you by generating a script for your measurement sets. In general, the first step in flux equalization is to set the flux density of the phase calibrator in each spectral window to the desired value using {{setjy}}. Then amplitude calibration tables are generated and applied using {{gaincal}} and {{applycal}} respectively. Finally, all calibrated measurement sets will be concatenated using {{concat}}.
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| If you have performed flux equalization, you can skip to the [[Imaging_Prep#Splitting off science target data | Splitting off science target data]] section below.
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| == Combining measurement sets from multiple executions ==
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| If there are multiple *.ms.split.cal files within the calibrated directory, combine them all into one single ms file in order to make later steps in the imaging process easier. Skip this section if you have only one execution.
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| Each execution of the scheduling block will generate multiple spectral windows with different sky frequencies, but the same rest frequency, due to the motion of the Earth. Thus, the resulting concatenated file will contain n spws, where n is (number of original science spws) x (number of executions). In other words, the multiple spws associated with a single rest frequency will not be regridded to a single spectral window in the ms. Combine all executions into one ms file, called calibrated.ms, using the {{concat}} command.
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| <source lang="python">
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| # in CASA
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| concatvis='calibrated.ms'
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| rmtables(concatvis)
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| os.system('rm -rf ' + concatvis + '.flagversions')
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| concat(vis=vislist,
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| concatvis=concatvis)
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| </source>
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| == Splitting off science target data ==
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| The calibrated.ms produced in the [[#Combining measurement sets from multiple executions | Combining measurement sets from multiple executions]] section, or your original ms file if you only have one execution, will contain the science target(s) as well as all the calibrator sources. At this point, you are only concerned with imaging your science targets so you can split them off from the calibrated.ms, which has both the science and calibrator sources. This will reduce the size of the file and make it more manageable during imaging. Depending on your project, there may be several executions or only one. Uncomment the line that fits with your data to set the concatvis variable properly.
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| <source lang="python">
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| # in CASA
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| # Uncomment following line for single executions
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| # concatvis = vislist[0]
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| # Uncomment following line for multiple executions
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| # concatvis='calibrated.ms'
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| </source>
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| Split out all target fields from the file and copy them, with only the data column, into a new ms file called calibrated_source.ms. Follow [https://casa.nrao.edu/Release4.1.0/doc/UserMan/UserMansu82.html this link] for more information regarding the structure of measurement sets.
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| <source lang="python">
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| # in CASA
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| sourcevis='calibrated_source.ms'
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| rmtables(sourcevis)
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| os.system('rm -rf ' + sourcevis + '.flagversions')
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| split(vis=concatvis,
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| intent='*TARGET*', # split off the target sources
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| outputvis=sourcevis,
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| datacolumn='data')
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| </source>
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| At this point, it is convenient to create a {{listobs}} file with information about the sources and spectral windows to reference during imaging. Inspect the {{listobs}} to make sure the {{split}} and {{concat}} worked as desired.
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| <source lang="python">
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| # in CASA
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| listobs(vis=sourcevis,listfile=sourcevis+'.listobs.txt')
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| </source>
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| An example {{listobs}} file is below.
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| <pre style="background-color: #fffacd;">
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| ================================================================================
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| Observer: Unknown Project: T.B.D.
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| Observation: ALMA
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| Telescope Observation Date Observer Project
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| ALMA [ 4.81015e+09, 4.81015e+09]Unknown T.B.D.
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| ALMA [ 4.81015e+09, 4.81016e+09]Unknown T.B.D.
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| ALMA [ 4.81016e+09, 4.81017e+09]Unknown T.B.D.
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| Data records: 126900 Total elapsed time = 16902.1 seconds
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| Observed from 22-Apr-2011/00:15:36.7 to 22-Apr-2011/04:57:18.8 (UTC)
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| ObservationID = 0 ArrayID = 0
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| Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
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| 22-Apr-2011/00:15:36.7 - 00:16:07.0 9 0 TW Hya 540 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 00:20:57.8 - 00:30:43.2 13 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 00:36:49.2 - 00:44:43.5 18 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 00:49:33.9 - 00:59:19.3 22 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 01:05:46.8 - 01:13:41.2 27 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 01:20:07.2 - 01:29:52.6 31 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 01:37:15.6 - 01:40:13.9 36 0 TW Hya 2700 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| ObservationID = 1 ArrayID = 0
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| Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
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| 22-Apr-2011/02:02:10.9 - 02:02:41.1 44 0 TW Hya 540 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 02:07:36.7 - 02:17:22.0 48 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 02:23:35.7 - 02:31:30.0 53 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 02:36:23.6 - 02:46:09.0 57 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 02:52:40.2 - 03:00:34.6 62 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 03:05:14.0 - 03:14:59.4 66 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 03:21:21.4 - 03:24:19.6 71 0 TW Hya 2700 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| ObservationID = 2 ArrayID = 0
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| Date Timerange (UTC) Scan FldId FieldName nRows SpwIds Average Interval(s) ScanIntent
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| 22-Apr-2011/03:44:46.9 - 03:45:17.1 79 0 TW Hya 540 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 03:50:08.4 - 03:59:53.7 83 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 04:06:03.7 - 04:13:58.1 88 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 04:18:49.1 - 04:28:34.4 92 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 04:34:59.5 - 04:42:53.9 97 0 TW Hya 7020 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| 04:47:33.5 - 04:57:18.8 101 0 TW Hya 8640 [0,1,2,3] [10.1, 10.1, 10.1, 10.1] [OBSERVE_TARGET#ON_SOURCE]
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| (nRows = Total number of rows per scan)
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| Fields: 1
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| ID Code Name RA Decl Epoch SrcId nRows
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| 0 none TW Hya 11:01:51.844983 -34.42.17.16088 J2000 0 126900
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| Spectral Windows: (4 unique spectral windows and 1 unique polarization setups)
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| SpwID Name #Chans Frame Ch0(MHz) ChanWid(kHz) TotBW(kHz) CtrFreq(MHz) Corrs
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| 0 3840 TOPO 356497.936 122.070 468750.0 356732.2500 XX YY
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| 1 3840 TOPO 357734.314 122.070 468750.0 357968.6279 XX YY
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| 2 3840 TOPO 346034.314 122.070 468750.0 345800.0000 XX YY
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| 3 3840 TOPO 343955.936 122.070 468750.0 343721.6221 XX YY
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| Sources: 4
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| ID Name SpwId RestFreq(MHz) SysVel(km/s)
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| 0 TW Hya 0 - -
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| 0 TW Hya 1 - -
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| 0 TW Hya 2 - -
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| 0 TW Hya 3 - -
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| Antennas: 9:
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| ID Name Station Diam. Long. Lat. Offset from array center (m) ITRF Geocentric coordinates (m)
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| East North Elevation x y z
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| 0 DV04 J505 12.0 m -067.45.18.0 -22.53.22.8 -7.2141 -541.3485 15.0178 2225061.036842 -5440128.036234 -2481534.422455
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| 1 DV06 T704 12.0 m -067.45.16.2 -22.53.22.1 42.8987 -520.1911 15.0694 2225110.551677 -5440116.726350 -2481514.951072
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| 2 DV07 J510 12.0 m -067.45.17.8 -22.53.23.5 -0.3652 -563.8032 14.9605 2225064.049398 -5440117.310745 -2481555.086720
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| 3 DV08 T703 12.0 m -067.45.16.2 -22.53.23.9 42.8798 -575.6928 14.6278 2225102.207484 -5440096.375809 -2481565.910698
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| 4 DV09 N602 12.0 m -067.45.17.4 -22.53.22.3 8.8012 -527.8598 15.0513 2225077.857538 -5440126.858119 -2481522.008896
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| 5 DV10 N606 12.0 m -067.45.17.1 -22.53.23.6 19.1981 -566.5667 14.9520 2225081.746122 -5440108.902762 -2481557.629365
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| 6 PM01 T702 12.0 m -067.45.18.6 -22.53.24.1 -23.6269 -582.3103 14.9195 2225039.780230 -5440119.418780 -2481572.120530
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| 7 PM02 T701 12.0 m -067.45.18.8 -22.53.22.2 -29.1268 -522.7917 15.0566 2225043.501617 -5440143.045000 -2481517.341976
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| 8 PM03 J504 12.0 m -067.45.17.0 -22.53.23.0 22.2015 -550.2548 14.9948 2225086.942689 -5440113.674706 -2481542.618537
| |
| </pre>
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| == Regridding spectral window (optional) ==
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| ALMA does what is called doppler setting, which means it adjusts the observed sky frequency for the motion of the source and the motion of the Earth around the Sun at the beginning of every execution block. Therefore, different execution blocks will have different observed sky frequencies because the vector of the motion of the Earth around the Sun will be different at different times.
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| In general, we strongly recommend using the {{clean}} command to regrid spectral windows to a common velocity/rest frequency scale on the fly. However, you may also regrid the frequency axis of the visibility data using {{cvel}} prior to imaging. The most common use case for this is where the motion of the Earth around the Sun causes the sky frequencies of the lines to shift too much between executions to identify a common channel range for continuum subtraction. If you regrid using {{cvel}} prior to imaging, you should make sure to use the same input parameters to the {{tclean}} task that you used for {{cvel}}. In other words, you shouldn't regrid the data using both {{cvel}} and {{clean}}.
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| You will likely need to regrid if you are imaging an ephemeris object. In this case, the outframe should be set to SOURCE, for example, outframe = 'SOURCE'.
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| '''Due to a bug in all released versions of CASA, {{mstransform}} and {{cvel}} should not be used to average greater than 2 channels together. This averaging should be done using {{clean}} until the bug is resolved.'''
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| We will start by setting the parameters specific to your observations.
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| <source lang="python">
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| # in CASA
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| sourcevis='calibrated_source.ms'
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| regridvis='calibrated_source_regrid.ms'
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| veltype = 'radio' # Keep set to radio. See notes in imaging section.
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| width = '0.23km/s' # due to bug in cvel/mstransform, do not regrid > 2 channels
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| nchan = -1 # leave this as the default
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| mode='velocity' # see science goals in the OT
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| start='' # leave this as the default
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| outframe = 'bary' # velocity reference frame. see science goals in the OT.
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| restfreq='115.27120GHz' # rest frequency of primary line of interest.
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| field = '4' # select science fields.
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| spw = '0,5,10' # spws associated with a single rest frequency. Do not attempt to combine spectral windows associated with different rest frequencies. This will take a long time to regrid and most likely isn't what you want.
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| </source>
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| Regridding here uses the {{cvel}} command to regrid multiple spws associated with a single rest frequency into a single spw. To avoid {{clean}} regridding the image a second time you will need to use the same parameters used for {{cvel}} for {{clean}}.
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| <source lang="python">
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| # in CASA
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| rmtables(regridvis)
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| os.system('rm -rf ' + regridvis + '.flagversions')
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|
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| cvel(vis=sourcevis,
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| field=field,
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| outputvis=regridvis,
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| spw=spw,
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| mode=mode,
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| nchan=nchan,
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| width=width,
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| start=start,
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| restfreq=restfreq,
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| outframe=outframe,
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| veltype=veltype)
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| </source>
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| The resulting ms will contain a single spw. You should regrid spws at different rest frequencies separately.
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| == Rename and backup data set ==
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| Depending on the different tasks that were performed above, the current measurement set will have a variety of names. For ease of beginning imaging with a common measurement set, rename the file to calibrated_final.ms.
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| If you haven’t regridded, your current ms should be named calibrated_source.ms.
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| <source lang="python">
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| # in CASA
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| # uncomment if you haven’t regridded
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| # os.system('mv -i ' + sourcevis + ' ' + 'calibrated_final.ms')
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| </source>
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| If you have regridded, your current ms should be named calibrated_source_regrid.ms.
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| <source lang="python">
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| # in CASA
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| # uncomment if you have regridded
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| # os.system('mv -i ' + regridvis + ' ' + 'calibrated_final.ms')
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| </source>
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| Now that you have fully calibrated measurement set containing all your science sources to image, we recommend creating a backup of the dataset. It is possible to corrupt a measurement set if you kill {{clean}}, {{tclean}}, and {{uvcontsub}} while they are running.
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| <source lang="python">
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| # in CASA
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| os.system('cp -ir calibrated_final.ms calibrated_final.ms.backup')
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| </source>
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| == Continue to Imaging ==
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| This guide is continued at '''[[Image_Continuum | Image the Continuum Template]]'''.
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| '''[[Guide_NA_ImagingTemplate | Return to the Main Page]]'''
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