Celery

DNest4 + Celerite = Celery. This program does inference of an unknown number of damped, stochastically excited signals from noisy data. The basic modelling ideas are covered in this paper, but the technology is much better.

(c) 2017, 2018 Brendon J. Brewer

LICENSE: GNU General Public License, version 3. http://www.gnu.org/licenses/gpl.txt

You'll need: git, Eigen3, LAPACK (including header files), a recent C++ compiler, Python (3?), numpy, matplotlib, numba.

Download and compile

Do this:

git clone --recursive https://github.com/eggplantbren/Celery
cd Celery/code
make

Example Data

There is an example dataset. To regenerate and view it, use

python generate_data.py

This plots the data and (once you close the first plot) the periodogram. The data consists of a single mode with a period of 20 (log10-period of 1.301), and a quality factor of 50 (mode lifetime = 20x50 = 1000).

The data also contains red noise and the error bars are too small. The modelling assumptions explicitly allow for both of these features.

Running Celery

To run Celery on the example data using 8 threads (recommended), use

./Celery -t 8 -d example_data.txt

Celery will run and you will see DNest4 output. Give it about 20 minutes (on the example dataset) before doing any postprocessing.

Postprocessing

Celery will run for a long time. The longer you run it, the more reliable the output will be. You can manually terminate it, or you can do the postprocessing without terminating the main process.

The postprocessing will require that you've installed the DNest4 Python package (see here for instructions). Simply invoke

python showresults.py

This will generate a bunch of output plots. As you close them, more will appear. Posterior samples (currently only a subset of the parameters) will also be saved in a YAML file for your convenience. These files have passed verification with yamllint.

The maximum number of modes

By default, Celery has a hard maximum of 30 on the number of modes it will search for. The computational expense scales as the square of this maximum, but it is recommended that you increase it (to 200, as a suggestion) in serious research applications. To do this, edit the value in MyModel.h and recompile by running make. Sorry it's hard coded at this point.