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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 firstname.lastname@example.org 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 email@example.com Princeton University August 2012