############## script tested for CASA 6.2.1.7 - pipeline ############# ### For running a selection of a few steps use: #my_steps=[0,1,2,3,4,5,6,7,8,9,10,11,12,13] ### For running a row of many steps use: # my_steps=range(0,8) # from 0 to 7 ################# REQUIRES ANALYSIS UTILITIES #################### ### to obtain analysis scripts from NRAO site https://casaguides.nrao.edu/index.php/Analysis_Utilities ### open terminal and use following 2 bash commands: # wget ftp://ftp.cv.nrao.edu/pub/casaguides/analysis_scripts.tar # tar xvf analysis_scripts.tar ################################################################## pathToAU='YOUR PATH TO AU-FOLDER' import sys sys.path.append(pathToAU) import analysisUtils as aU my_vis = 'xx' if 0 in my_steps : ## Import the asdm in .ms os.system('rm -rf '+my_vis) # erase existing '*.ms' importasdm(asdm='xx', # location of the raw data vis=my_vis, # name of the MeasurementSet to be created from the raw data lazy=True, # use symbolic link between raw data and MeasurementSet asis='*', # copy all metadata available (antennae position, pointing information bdfflags=True) # keep all existing flags of the raw data ## create summary of this EB os.system('rm -rf '+my_vis+'.listobs') # erase existing LISTOBS listobs(vis=my_vis, # name of the MS to be summarized listfile=my_vis+'.listobs') # Filename containing the summary ## Visualize the array configuration at the time of the observations os.system('rm -rf '+my_vis+'.plotants.png') # erase existing plot plotants(vis=my_vis, # name of the MS figfile=my_vis+'.plotants.png') # Filename of the output ##################################### #### Summary of listobs/plotants #### ##################################### # # ALMA Band-XX observations at ~ XX GHz with XX SpWs with XX channel each # # # CALIBRATE FLUX/AMPLI: XX (ID:XX) SpW: XX-XX-XX-XX # CALIBRATE BANDPASS : XX (ID:XX) SpW: XX-XX-XX-XX # CALIBRATE PHASE : XX (ID:XX) SpW: XX-XX-XX-XX # TARGET : XX (ID:XX) SpW: XX-XX-XX-XX # # Tsys : Scan XX SpW: XX # # # WVR : xx SpW: xx # # # ARRAY CONFIGURATION : xx Antennae; Longest Baseline ~ XX m -> Synthesized Beam ~ xx arcsec # Shortest Baseline ~ XX m -> largest angular scale ~ xx arcsec # # ##################################### ##################################### ##################################### if 1 in my_steps : ##################################### ### A Priori Calibration: Tsys ### ##################################### ## Generation of the Tsys caltable using the task GENCAL # It is good practice to erase existing 'caltable' that # the forthcoming task will create. This avoids problems # when re-running a given calibration step os.system('rm -rf '+my_vis+'.tsys') # erase existing TSYS CALTABLE # generate of Tsys caltable (naming convention name_ms.caltable_name) gencal(vis=my_vis, # Name of the MS caltable=my_vis+'.tsys', # Name of the CALTABLE output by GENCAL caltype='xx') # Tell GENCAL what we want to do # Tsys measurements are known to be corrupted at the edges # of each SpWs. We must flag these channels in the Tsys CALTABLE # using the task FLAGDATA # # CASA convention: # - '0:20,1:110' --> SpW 0 channel 20 and SpW1 channel 110 # - '0;2:20;110' --> SpW 0 and 2 and channel 20 and 110 # - '0~2:20;110' --> SpW 0 to 2 channel 20 and 110 # - '0~2:20;110,3:50 --> SpW 0 to 2 channel 20 and 110 and SpW 3 channel 50 # - '0~2:20~110,3:50 --> SpW 0 to 2 channel 20 to 110 and SpW 3 channel 50 flagdata(vis=my_vis+'.tsys', # Name of the MS or CALTABLE to be flagged mode='manual', # spw='xx', # SpW and Channel to be flagged flagbackup = False) # Do we want to keep a backup of the flag before the run # We inspect these Tsys CALTABLE using the task PLOTBANDPASS, looking # for not well behaved antennae/scans. In case we find some, we will need # to flag the corresponding visibilities using FLAGDATA # plot for each antenna and each SPW, Tsys vs. Freq, overlaying all tsys scan together # and add the atmospheric transmission os.system('rm -rf '+my_vis+'.tsys.plots') # delete old folder for plots os.system('mkdir '+my_vis+'.tsys.plots') # make new folder for plots aU.plotbandpass(caltable=my_vis+'.tsys', # name of the CALTABLE to use xaxis='xx', yaxis='xx', antenna='', # Which antennae do you want to inspect? spw='', # Which SpW do you want to inspect? showatm=True, # Show the atmospheric transmission? overlay='xx', # figfile=my_vis+'.tsys.plots/'+my_vis, # convention: put it in a folder name_ms.caltable_name/name_ms interactive=False) # Open all the files generated by PLOTBANDPASS # >> eog my_files* & # # Inspect these plots and look for peculiar antennae/spw/scan # # Did you find any? if yes, those have to be flagged with FLAGDATA # These flags can be added in step 4 if 2 in my_steps : ##################################### ### A Priori Calibration: WVR ### ##################################### ## Generation of the WVR caltable using the task WVRGCAL # erase existing WVR CALTABLE os.system('rm -rf '+my_vis+'.wvr') # generate WVR CALTABLE wvrgcal(vis=my_vis, # Name of the MS to be process caltable=my_vis+'.wvr', # Name of the CALTABLE output by WRVGCAL spw=[xx], # SpW to which solutions should be applied afterward smooth='xx', # Smooth the calibration on the given timescale tie=[xx], # Prioritise tieing the phase of these sources as well as possible (typically phase cal and target) statsource=xx) # Compute the statistics (Phase RMS, Disc) on this source only # Check the output of WVRGCAL in the CASA logger # Check the PWV value # PWV = xx mm # # As written in Help(WVRGCAL), Disc > few hundreds # indicates that WVR corrections should not be applied # # Did you find such cases? # # # Antenna with peculiar values should be flagged in WVRGCAL (wvrflag=xx). # # Do you find such antenna ? # # if 3 in my_steps : ############################################# ### A Priori Calibration: Application ### ############################################# ## Application of the WVR and Tsys CALTABLE using ## the task APPLYCAL # Application of these corrections on the field x applycal(vis=my_vis, # Name of the MS to process field='xx', # field ID to correct spw='xx', # SpW to be corrected (i.e., science SpWs) gaintable= [my_vis+'.xx',my_vis+'.xx'], # name of CALTABLEs to use gainfield= ['xx','xx'], # ['Apply the Tsys from field x', 'Apply the WVR from field x'] interp ='linear,linear', # 'Time-interpolation method,Frequency-interpolation-method' calwt= True, # Applycal will scale weights of each visibility by 1/sqrt(Tsys(i) * Tsys(j)) flagbackup = False, spwmap=[[xx],[xx]]) #[[apply Tsys SpW-x solutions to SpW-0, ... , Apply Tsys SpW-x solutions to SpW-i, ... , Apply Tsys SpW-x solutions to SpW-N],[same for WVR]] # your spw setup is # spws = [0,1,2,3,4,5,6,7,8] # spwmap applies the solution from the spw specified at a given list index in spwmap # to the spw at same list index in the spws list # spwmap = [0,0,2,2,4,4,6,7,8] ## Application of these corrections on the field x applycal(vis=my_vis, # Name of the MS to process field='xx', # field ID to correct spw='xx', # SpW to be corrected (i.e., science SpWs) gaintable= [my_vis+'.xx',my_vis+'.xx'], # name of CALTABLE to use gainfield= ['xx','xx'], # [Apply the Tsys from field 'x', Apply the WVr from field 'x'] interp ='linear,linear', # 'Time-interpolation method,Frequency-interpolation-method' calwt= True, # Applycal will scale weights of each visibility by 1/sqrt(Tsys(i) * Tsys(j)) flagbackup = False, spwmap=[[xx],[xx]]) #[[apply Tsys SpW-x solutions to SpW-0, ... , Apply Tsys SpW-x solutions to SpW-i, ... , Apply Tsys SpW-x solutions to SpW-N ],[same for WVR]] if 4 in my_steps : ############################################# ### A Priori Calibration: Flagging ### ############################################# ## We flag things which not anymore needed ## using the task FLAGDATA ### optional but skip for now: probably see suspicious antennas in autocorr ### # # # plotms(vis=my_vis, # # xaxis='frequency', # # yaxis='amp', # # field='0~2', # # spw='9,11,13,15', # Tsys channels # uvrange='', # # antenna='*&&&', # auto-correlations # avgtime='360', # scan length # avgfield=True, # average over all fields # iteraxis='baseline', # iterate over baseline # coloraxis='corr', # colour of data points = correlation # showgui=True, # # plotfile='field-0-1-2-amp-freq.png', # # overwrite=True) # # # ############################################################################### # We flag auto-correlation visibilities (i.e., antenna-i correlated with antenna-i) flagdata(vis = my_vis, # Name of the MS mode = 'xx', # Mode to be used spw = 'xx', # everything but the WVR SpW autocorr = True, flagbackup = False) # We flag un-necessary scans (POINTING, SIDEBAND_RATIO, ATMOSPHERE) flagdata(vis = my_vis, # Name of the MS mode = 'xx', intent = 'xx', flagbackup = False) # We flag visibilities coming from antennae which are shadowed by an other one. flagdata(vis = my_vis, mode = 'xx', flagbackup = False) ## Additional flagging up to the students: ## Found while looking at Tsys flagdata(vis = my_vis, mode = 'xx', spw = 'xx', antenna='xx', correlation='xx', flagbackup = False) if 5 in my_steps : ################################################### ### SPLIT I: slim down data for calibration ### ################################################### ## We now extract the Tsys and WVR corrected data of the science channels and drop the rest. ## In this way, the make the data easier to process for the main calibration. os.system('rm -rf '+my_vis+'.split') # erase existing '*.ms.split' mstransform(vis = my_vis, # Name of the input MS outputvis = my_vis+'.split', # Name of the output MS datacolumn = 'xx', # only data with the the Tsys and WVR tables applied spw = 'xx', # science SpW typically keepflags = True) # keep flagged rows in the output ## We generate a listobs of the splitted data set which will be more tidy and easier to skim than the inital listobs. os.system('rm -rf '+my_vis+'.split.listobs') # erase existing LISTOBS listobs(vis = my_vis+'.split', listfile = my_vis+'.split.listobs') ## Scan and field IDs are the same, SpW Ids are renamed (compare frequencies)! # ## SpW 17 -> 0 ## SpW 19 -> 1 ## SpW 21 -> 2 ## SpW 23 -> 3 # ##############################################################################