-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME
110 lines (84 loc) · 4.63 KB
/
README
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
### 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.