EMPEROR
Exoplanet Mcmc Parallel tEmpering for Rv Orbit Retrieval
Overview
EMPEROR (Exoplanet Mcmc Parallel tEmpering for Rv Orbit Retrieval), is a Python-based algorithm that automatically searches for signals in Radial Velocity timeseries (and also joint modelling with Astrometry), employing Markov chains and parallel tempering methods, convergence tests and Bayesian statistics, along with various noise models. A number of posterior sampling routines are available, focused on efficiently searching for signals in highly multi-modal posteriors. The code allows the analysis of multi-instrument and multi-planet data sets and performs model comparisons automatically to return the optimum model that best describes the data.
Make sure to check the documentation!
Why EMPEROR?
- It's really simple to use
- It has a series of configuration commands that will amaze you
- Advanced Noise Model
- Quite Flexible!
Dependencies
This code makes use of:
All of them can be easily installed with pip.
For additional capabilities, you can install:
Installation
Pip
In the console type
pip3 install astroEMPEROR
From Source
In the console type
git clone https://github.com/ReddTea/astroEMPEROR.git
Installation Verification
Download the tests folder and run test_basic.py to make sure everything works!
In terminal:
python test_basic.py
Quick Usage
We need to set up our working directory with two subfolders, datafiles and datalogs, the former for data input, the later for output.
📂working_directory
┣ 📜mini_test.py
┣ 📂datafiles
┃ ┣ 📂51Peg
┃ ┃ ┗ 📂RV
┃ ┃ ┃ ┗ 📜51peg.vels
┣ 📂datalogs
┃ ┣ 📂51Peg
┃ ┃ ┗ 📂run_1
Running the code is as simple as:
import astroemperor
sim = astroemperor.Simulation()
sim.set_engine('reddemcee')
sim.engine_config['setup'] = [2, 100, 500, 1]
sim.load_data('51Peg') # read from ./datafiles/
sim.plot_trace['plot'] = False # deactivate arviz plots
sim.autorun(1, 1) # (from=1, to=1): just 1 keplerian
Outputs
All results can be found in the datalogs folder. You will see chain plots, posterior plots, histograms, phasefolded curves, the chain sample and more!
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