https://github.com/jakevdp/multiband_ls

Source for our paper on multiband periodograms.
https://github.com/jakevdp/multiband_ls

Last synced: 24 days ago
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Source for our paper on multiband periodograms.

• Host: GitHub
• URL: https://github.com/jakevdp/multiband_ls
• Owner: jakevdp
• License: bsd-2-clause
• Created: 2014-11-04T20:50:48.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2019-07-09T01:15:48.000Z (over 5 years ago)
• Last Synced: 2024-10-05T01:19:17.715Z (about 1 month ago)
• Language: TeX
• Homepage:
• Size: 17.7 MB
• Stars: 30
• Watchers: 8
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Mutiband Lomb-Scargle Periodograms

==================================

This repository contains the source for our multiband periodogram paper.

It makes use of the [gatspy](http://github.com/jakevdp/gatspy/) package,

which has been developed concurrently.

The paper has been submitted to the Astrophysical Journal, and a preprint is available on [arXiv](http://arxiv.org/abs/1502.01344).

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](https://github.com/ewanmellor/gh-publisher)).

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

Reproducing the Paper

---------------------

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](http://ipython.org), [numpy](http://numpy.org), [scipy](http://scipy.org), [matplotlib](http://matplotlib.org), [scikit-learn](http://scikit-learn.org), [pandas](http://pandas.pydata.org/))

- [seaborn](http://stanford.edu/~mwaskom/software/seaborn/) for plot styles.

- [astroML](http://astroML.org) for general astronomy machine learning tools.

- [gatspy](http://github.com/astroML/gatspy) for astronomical time-series analysis.

- [supersmoother](http://github.com/jakevdp/supersmoother) for the supersmoother algorithm used by ``gatspy``.

With [conda](http://conda.pydata.org/miniconda.html), 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).