Skip to content

RTIP/artip

Repository files navigation

Introduction

Automated Radio Telescope Image Processing Pipeline (ARTIP) is an end to end pipeline automating the entire process of flagging, calibration and imaging for radio-interferometric data.

ARTIP starts with raw data i.e. a Measurement Set and goes through multiple stages like Flux Calibration, Bandpass Calibration, Phase Calibration and Imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs.

It is written using standard python libraries and the CASA package.

The latest version of the pipeline can deal with datasets with (i) multiple spectral windows and (ii) multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators. It has been tested against narrow-band (1 - 10 MHz) datasets (~ 6 to 30GB) produced by Giant Metrewave Radio Telescope, Pune (GMRT) and Very Large Array, NM (VLA).

The future versions will have support for large wideband datasets.

This version of ARTIP is developed for CASA 4.7.2, and not tested for later versions.

Obtaining ARTIP

ARTIP releases are present at https://github.com/TWARTIP/artip/releases. Download and unzip the artip package to run the pipeline.

$ unzip artip<version>.zip -d artip

Prerequisites

  1. Install Anaconda and CASA

    1.1. Installation test

        Linux:
            $ casa --help
            $ conda --version
        OSX:
            $ casapy --help
            $ conda --version
  2. Install pipeline dependencies

     $ cd artip
     $ ./setup.sh

    setup.sh will

    • Setup casa-pip for installing python modules CASA from PyPI
    • Create "artip" conda environment from artip/environment.yml

    2.1. Installation test

      $ source <rc_file_path>
      $ casa-pip -h
      $ source activate artip
      $ pyb --version

Documentation

https://github.com/TWARTIP/artipdoc/blob/master/artip_documentation.pdf

Running Pipeline

  1. Default conf directory is present at <artip_path>/conf. You can either update it or create your own conf directory having same format.

  2. Update the CASA path and model_path in <conf_dir_path>/casa.yml

  3. Specify flags from the observation logs in "<conf_dir_path>/user_defined_flags.txt". Flags follow format similar to CASA flagdata command with mode='list'.

    Below are the examples for the same :

     * Flag antennas
             reason='BAD_ANTENNA' correlation='RR,LL' mode='manual' antenna='1,18' scan='1,7,2,4,6,3,5'
     * Flag Baselines
             reason='BAD_BASELINE' correlation='RR,LL' mode='manual' antenna='11&19' scan='1,7,2,4,6,3,5'   
     * Flag Time
             reason='BAD_ANTENNA_TIME' correlation='LL' mode='manual' antenna='15' scan='1' timerange='2013/01/05/06:59:49~2013/01/05/07:00:00'
             reason='BAD_BASELINE_TIME' correlation='LL' mode='manual' antenna='7&8' scan='4' timerange='2013/01/05/06:59:49~2013/01/05/07:00:00'
    
  4. Run pipeline through command line but make sure casa_path is set properly in <conf_dir_path>/casa.yml

    $ cd <artip_path>
    $ source activate artip
    $ pyb run -P dataset="<ms_dataset_path>" -P conf="<conf_dir_path>" -P output="<output_dir>"

Pipeline Output

All the output artifacts like caltables, flag files, continuum and spectral line images are persisted in <output_path>/<ms_dataset_name> directory.

Plotting Flagging Graphs

Pipeline records antenna wise flags summary at different stages. After pipeline completion, user can generate flag summary plots using below scripts :

    $ cd <artip_path>/flagged_data_summary
    $ python generate_graph.py "<output_path>/<ms_dataset_name>"  

NOTE : Flags summary is recorded only when pipeline is run with "flag_summary: true" in <conf_dir_path>/pipeline.yml

Charts can be accessed at http://localhost:8000/chart.html

Publication acknowledgement

Include the following in publications using ARTIP:

"This paper makes use of ARTIP - the Automated Radio Telescope Imaging Pipeline developed by IUCAA (http://www.iucaa.in/) and ThoughtWorks (https://www.thoughtworks.com/)"