A software package to reduce angular differential imaging data from Subaru/HiCIAO
Python Shell
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
addsource
adiparam
centroid
combine
destripe
destripe_utils
easygui
loci
locitools
parallel
pca
photometry
progressbar
psfref
svd
transform
utils
.bashrc
.gitignore
LICENSE
README
acorns-adi.py
partial_sub.dat

README

ACORNS-ADI 
Algorithms for Calibration, Optimized Registration and Nulling the
Star in Angular Differential Imaging

To install and run, simply extract the directory and run
./acorns-adi.py 
or 
python acorns-adi.py .
Follow the on-screen instructions to set up the parameters and 
reduce your data.  Please e-mail tbrandt@astro.princeton.edu with
bugs or incompatibilities.  To use with data from an instrument 
other than HiCIAO, add the command line option prefix to match
file names from other instruments.  E.g., for Gemini North data
from 2006, you may use
acorns-adi.py --prefix="N2006"

You can also add the installation directory to your path and 
run acorns-adi.py from anywhere.

I do not currently have documentation other than a paper 
describing the algorithms (Brandt, McElwain, Turner, et al., 2012).

I have tried to adequately comment the code.

System Requirements:  Linux or Mac.  

Software Requirements: 
- python 2.7 (NOT python 3, which has different syntax!)
- scipy 0.9+ (http://scipy.org/Download)
- numpy 1.5+ (http://scipy.org/Download)
- pyephem (http://rhodesmill.org/pyephem/)
- pyfits (http://www.stsci.edu/institute/software_hardware/pyfits/)
- pylab (part of matplotlib, http://matplotlib.sourceforge.net/)

All of the above are free and open-source.  They should all be 
available with pip (Python Package Index).

Also, you need sufficient RAM to store a dataset in memory.
Typically, this means at least ~8 GB to reduce a dataset of ~200
HiCIAO frames.  It is possible to avoid this requirement, but it is
difficult and would require a lot of I/O, for LOCI, to destripe,
dewarp, compute centroids, and even to average the data.  NOT
recommended, unless you have no choice.  As an alternative, you can
set the dimensionality of the recentered data to be smaller and only
perform LOCI on this smaller dataset (which would then fit in RAM).


Tim Brandt
tbrandt@astro.princeton.edu
Princeton University
August 2012