### Welcome to kong_lib. 

This is my personal repo meant to store my helper scripts for radio astronomical data handling. 
As such, I can't guarantee that these scripts work for you! 

### Author
Bradley Frank (@foxmouldy)

### Support or Contact
Email me at [email protected] 


###KONG_LIB DOCUMENTATION
###======================
Bradley Frank, UCT, 2012
http://foxmouldy.github.com/kong_lib/

- The kong_lib scripts are casapy python scripts for the data reduction of spectral line 
sources as observed by the KAT-7 telescope.
However, the scripts are modular enough for you to adapt them to your specific
observation, as long as your telescope is similar to KAT-7 :)

- The philosophy and methodology behind the calibration and imaging setup here
are extracted from the documentation and tutorials as presented for the VLA use-cases:
http://casaguides.nrao.edu/index.php?title=Main_Page

- To download the library, you can easily visit the landing page and just download it 
and unzip it into your working directory: 
http://foxmouldy.github.com/kong_lib/
or you could clone the repo to track changes:
	git clone git://github.com/foxmouldy/kong_lib.git

- To fetch changes you would do the following: 
	- Point a remote to the repo: 
		git remote add upstream https://github.com/foxmouldy/kong_lib.git
	- Fetch (unmerged) changes: 
		git fetch upstream
	- Merge the changes to your copy:
		git merge upstream/master

- PLEASE USE THE READ-ONLY VERSION!!!


Cookbook
========

Each script is meant to be run from the CASA command line. For e.g. if you want to run easyflag.py:
run kong_lib/easy_flag.py
will return the help list. 

1. Observations utilize the conventional phase referencing method of observation, i.e.
observations of a primary flux calibrator, a phase calibrator close to the sources and, 
of course, the spectral line source(s). 

2. The calibration workflow as implemented by these scripts is as follows: 


+----------------+        +----------------+        +----------------+        +----------------+
|                |        |                |        |                |        |                |
|                |        |                |        |                |        |                |
|  easyflag.py   +------->|  calreduce.py  +------->|  calapply.py   +------->|  makedirty.py  +
|                |        |  kalreduce.py  |        |                |        |                |
|                |        |                |        |                |        |                |
+----------------+        +----------------+        +----------------+        +----------------+
 - Flags autocorrs          - setjy                                            - split corrected

 - Performs rflag           - Bandpass                                         - perform mfs on
                                                                                 each source
                            - Gaincal

                            - Fluxscale

3. easyflag.py
This flags the autocorrelations and uses tflagdata to do a simple rflag on the data. More sophisticated
flagging should be done manually. 

4. Calibrating the calibrators: calreduce.py and kalreduce.py
4.1. calreduce.py
This follows a very simple methodology: 

+----------+    +------------+     +-----------+    +------------+
|          |    |            |     |           |    |            |
|  setjy   +-K->|  bandpass  +---->|  gaincal  +--->| fluxscale  +------>  ftable
|          |    |            |  ^  |           | ^  |            |
+----------+    +-----+------+  |  +-----+-----+ |  +------------+
                      |         |        |       |
                      |         |        |       |
                      v         |        v       |
                    btable+-----+       gtable+--+

4.2. kalreduce.py
Identical to the process above, except where the K appears in the above workflow. The K represents the
following: 

                   +---------------+           +--------------+
                   |  gaincal      |           |  gaincal     |
                   | initial phase +----------+| K Correction |
                   | correction    |     ^     |              |
                   +---------------+     |     +--------------+
                          +              |             +
                          |              |             |
                          v              |             v
                       iptable+----------+          ktable

5. calapply.py 
Once you're satisfied with your tables, you can then simply apply them to your data. 

6. makedirty.py
This script will split the corrected data column into a new MS, and will make an MFS dirty image of 
each of the sources.