This repository contains the source for our multiband periodogram paper. It makes use of the gatspy package, which has been developed concurrently. The paper has been submitted to the Astrophysical Journal, and a preprint is available on arXiv. To see a current build of the paper from the master branch of this repository, refer to http://jakevdp.github.io/multiband_LS (powered by gh-publisher).

Feel free to submit comments or feedback via the Issues tab on this repository.

The LaTeX source of the paper, including all figure pdfs, is in the writeup directory. The code to reproduce the analysis and figures in the paper is in the figures directory.

To reproduce the figures, first install the following packages (Python 2 or 3):

• Standard Python scientific stack: (IPython, numpy, scipy, matplotlib, scikit-learn, pandas)
• seaborn for plot styles.
• astroML for general astronomy machine learning tools.
• gatspy for astronomical time-series analysis.
• supersmoother for the supersmoother algorithm used by gatspy.

With conda, a new environment meeting these requirements can be set up as follows:

$ conda create -n multibandLS python=3.4 ipython-notebook numpy scipy matplotlib scikit-learn pandas seaborn pip
$ source activate multibandLS
$ pip install astroML gatspy supersmoother

Once these packages are installed, navigate to the figures directory and run any of the fig*.py scripts. For example, to create figure 1, type

$ cd figures
$ python fig01_basic_example.py

Several of the figures require the results of long computations. These results are cached as numpy binary files in figures/LSSTsims/ and figures/S82sims/. Code to recompute these results in parallel is in the compute_results.py script in each of these directories. Note that the full computation for these takes several dozen CPU hours, but is trivially parallelizable with IPython parallel (see scripts for details).