Tohban EOVSA Imaging Tutorial A-Z: Difference between revisions

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===== Step 1: Downloading raw data (IDB) on pipeline server=====
=== Connection details to pipeline server ===
==== Step 1: Downloading raw data (IDB) on pipeline server====
On pipeline, CASA
On pipeline, CASA


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calibeovsa(vis='IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True)
calibeovsa(vis='IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True)
</pre>
</pre>
 
=== Connection details to Inti server ===
====Step 4: Self-calibration on inti server ====
https://github.com/binchensun/casa-eovsa/blob/master/slfcal_example.py
 
====Step 5: Quick-look imaging ====
 
 
=== Step 6: Final imaging ===
 
 
=== Connection details to pipeline and inti servers ===
=== Transferring details between servers ===
=== Transferring details between servers ===
=== Software details on the servers ===
=== Software details on the servers ===
On Inti,
On Inti,
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>>vi .bashrc   
>>vi .bashrc   
Insert the text given below and save it.
Insert the text given below and save it.
<pre style="background-color: #FCEBD9">
#### setting start ####
#### setting start ####
if [ $HOSTNAME == "baozi.hpcnet.campus.njit.edu" ]; then
if [ $HOSTNAME == "baozi.hpcnet.campus.njit.edu" ]; then
Line 95: Line 87:
fi
fi
#### setting end ####
#### setting end ####
</pre>


Both CASA 5 and 6 are available on into.  
Both CASA 5 and 6 are available on Inti.  


Please enter the bash environment on inti, and load the desired casa environment with the alias below.
Please enter the bash environment on inti, and load the desired casa environment with the alias below.
To load CASA 5:
To load CASA 5:
Enter bash environment by giving >>bash  
Enter bash environment by giving >>bash  
 
<pre style="background-color: #FCEBD9">
>> loadcasa5
>> loadcasa5
>> casa  or suncasa              #This should load the software and you are ready for analysis
>> casa  or suncasa              #This should load the software and you are ready for analysis
 
</pre>
To load CASA 6:
To load CASA 6:
Enter bash environment by giving >>bash  
Enter bash environment by giving >>bash  
 
<pre style="background-color: #FCEBD9">
>>loadcasa6
>>loadcasa6
>>ipython                      #This should load the software and you are ready for analysis
>>ipython                      #This should load the software and you are ready for analysis
</pre>
Here, for example, to use clean, first start ipython as given above, then type in >>from casatasks import tclean
====Step 4: Self-calibration on Inti server ====
https://github.com/binchensun/casa-eovsa/blob/master/slfcal_example.py


Here, for example, to use clean, first start ipython as given above, then type in >>from casatasks import tclean
====Step 5: Quick-look imaging ====
====Step 6: Final imaging ====

Revision as of 14:37, 16 July 2021

Connection details to pipeline server

Step 1: Downloading raw data (IDB) on pipeline server

On pipeline, CASA

from astropy.time import Time
import os
trange = Time(['2017-08-21 20:15:00', '2017-08-21 20:25:00'])
#### (Optional) change output path, default current directory "./" #####
outpath = './msdata/'
if not os.path.exists(outpath):
    os.makedirs(outpath)
######################################################
msfiles = importeovsa(idbfiles=trange, ncpu=1[, visprefix=outpath])

OR

from suncasa.tasks import task_calibeovsa as calibeovsa
from suncasa.tasks import task_importeovsa as timporteovsa
from split_cli import split_cli as split
import dump_tsys as dt
from util import Time
import numpy as np
import os
from glob import glob
from eovsapy import util


trange = Time(['2020-06-07 21:35:00', '2020-06-07 21:55:00'])
idbdir = util.get_idbdir(trange[0])

info = dt.rd_fdb(trange[0])
sidx = np.where(
    np.logical_and(info['SOURCEID'] == 'Sun', info['PROJECTID'] == 'NormalObserving') & np.logical_and(
        info['ST_TS'].astype(np.float) >= trange[0].lv,
        info['ST_TS'].astype(np.float) <= trange[
            1].lv))
filelist = info['FILE'][sidx]

outpath = './msdata/'
if not os.path.exists(outpath):
    os.makedirs(outpath)
inpath = idbdir + '{}/'.format(trange[0].datetime.strftime("%Y%m%d"))
ncpu = 1

msfiles = timporteovsa.importeovsa(idbfiles=[inpath + ll for ll in filelist], ncpu=ncpu, timebin="0s", width=1,
                                   visprefix=outpath,
                                   nocreatms=False, doconcat=False,
                                   modelms="", doscaling=False, keep_nsclms=False, udb_corr=True)


Step 2: Concatenate all the 10 mins data

# This is to set the path/name for the concatenated files
concatvis = os.path.basename(msfiles[0])[:11] + '_concat.ms'
vis = calibeovsa(msfiles, doconcat=True, concatvis=concatvis[, msoutdir=outpath])


Step 3: Calibration

calibeovsa(vis='IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True)

Connection details to Inti server

Transferring details between servers

Software details on the servers

On Inti, when logging in for the first time, please add the following lines to your accounts .bashrc file.

>>vi .bashrc Insert the text given below and save it.

#### setting start ####
if [ $HOSTNAME == "baozi.hpcnet.campus.njit.edu" ]; then
    source /srg/.setenv_baozi
fi
if [ $HOSTNAME == "inti.hpcnet.campus.njit.edu" ]; then
    source /inti/.setenv_inti
fi
if [ $HOSTNAME == "guko.resource.campus.njit.edu" ]; then
    source /data/data/.setenv_guko
fi
#### setting end ####

Both CASA 5 and 6 are available on Inti.

Please enter the bash environment on inti, and load the desired casa environment with the alias below. To load CASA 5: Enter bash environment by giving >>bash

>> loadcasa5
>> casa  or suncasa              #This should load the software and you are ready for analysis

To load CASA 6: Enter bash environment by giving >>bash

>>loadcasa6
>>ipython                       #This should load the software and you are ready for analysis

Here, for example, to use clean, first start ipython as given above, then type in >>from casatasks import tclean

Step 4: Self-calibration on Inti server

https://github.com/binchensun/casa-eovsa/blob/master/slfcal_example.py

Step 5: Quick-look imaging

Step 6: Final imaging