TriCCS Data Reduction
Project description
TriCCS Data Reduction (TDR)
Overview
Data reduction of data taken with Seimei/TriCCS could be done in this repository.
Though optimized for Seimei/TriCCS,
you can apply it for imaging data taken with other high-speed cameras
(Tomo-e Gozen etc.).
Procedures
-
Calibration (dark subtraction, flat-field correction)
-
Stacking fits by mean, median, etc.
-
Splitting fits (only for photometry using
Moving Object Photometry (movphot)
(bitbucket) ) -
Common ID search
Installing
# Install from PyPI
pip install tdr
or
# Install with `setup.py`
git clone https://jin_beniyama@bitbucket.org/jin_beniyama/triccs-data-reduction.git
python setup.py install
Usage
Here g-band data taken with Seimei/TriCCS is considered. All band data could be analyzed in the same way.
Fits data taken with TriCCS have a format like TRCS00005180.fits
.
First 4 characters TRCS
means the instrument TriCCS,
the next 7 characters are the exposure ID
and the last 1 character is band identical number
(0
for g-band, 1
for r-band and 2
for i/z-band).
After each reduction stage, a prefix is added to the filename. History can be checked in fits header as well.
1. Calibration
Here, consider the situation: exposure time for an object frame is 10 s, for a flat frame is 1 s, and for dark frames are 1s and 10s (all in g-band) like below.
- object
TRCS00000010.fits
(10 s) - dark for flat
TRCS00000020.fits
(1s) - dark for object
TRCS00000030.fits
(10s) - flat
TRCS00000040.fits
(1s)
Dark subtraction and flat-field correction are done as follows.
First, create master dark frame, which has prefix d
like dTRCS00000020.fits
.
The maximum count frame is not used for stacking, which leads to avoiding cosmic rays or fast-moving object contamination.
[usage]
# Create master dark
makedark (3-d dark)
[example]
# Create master dark for flat
makedark TRCS00000020.fits
# Create master dark for object
makedark TRCS00000030.fits
Next, create a master normalized flat frame using master dark for a flat frame.
, which has prefix f
like fTRCS00000040.fits
.
The maximum count frame is not used for stacking as well.
[usage]
# Create master flat
makeflat --flat (3-d flat) --dark (2-d master dark)
[example]
# Create master flat
makeflat --flat TRCS00000040.fits --dark dTRCS00000020.fits
Finally, reduce an object frame using both master dark and flat frames.
The reduced object frame has prefix r
like rTRCS00000010.fits
.
[usage]
# Do dark subtraction and flat-field correction
reduce --obj (3-d object) --dark (2-d master dark) --flat (2-d master flat)
[example]
# Do dark subtraction and flat-field correction
reduce --obj TRCS00000010.fits --dark dTRCS00000020.fits --flat fTRCS00000040.fits
2. Stacking
Output fits have format like
maxrTRCS00000010.fits
(max),
minrTRCS00000010.fits
(min),
meanrTRCS00000010.fits
(mean) and
medianrTRCS00000010.fits
(median).
[usage]
# Maximum stacking
stackfits (3-d reduced fits) max
# Minimum stacking
stackfits (3-d reduced fits) min
# Mean stacking
stackfits (3-d reduced fits) mean
# Median stacking
stackfits (3-d reduced fits) median
[example]
# Maximum stacking
stackfits rTRCS00000010.fits max
# Minimum stacking
stackfits rTRCS00000010.fits min
# Mean stacking
stackfits rTRCS00000010.fits mean
# Median stacking
stackfits rTRCS00000010.fits median
3. Splitting [in preparation]
If you are going to use Moving Object Photometry (movphot)
(bitbucket) for photometry,
3-d fits cube should be split into multiple 2-d fits.
[usage]
# Mask pixels and split fits into multiple 2-d fits
mask_split (3-d fits)
[example]
# Mask pixels and split fits to multiple 2-d fits
mask_split rTRCS00000010.fits
Output fits are as follows (when the number of frames is 3).
The masked and splitted frames hav suffix ms
like rTRCS00000010ms0001.fits
.
4. Common ID search
If the wcs pasting failed for some fits, it is necessary to extract common ID fits.
[example]
# Extract common fits ID from g,r,i bands list (glist.txt, rlist.txt, ilist.txt)
cat glist.txt | awk '{print substr($0,17,3)}' > gID.txt
cat rlist.txt | awk '{print substr($0,17,3)}' > rID.txt
cat ilist.txt | awk '{print substr($0,17,3)}' > iID.txt
# Create common ID list
commonIDsearch gID.txt rID.txt iID.txt --pre rTRCS00001260ce0
--post w.fits > glist_common.txt
Dependencies
This library is depending on NumPy
.
Scripts are developed on Python 3.7.10
and NumPy 1.19.2
.
LICENCE
This software is released under the MIT License.
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