astroemperor 1.0.1

Flexible python exoplanet fitter

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:

• Numpy

• Scipy

• pandas

• matplotlib>=3.5.1

• kepler

• reddemcee

• reddcolors

• tabulate

• termcolor

• tqdm

All of them can be easily installed with pip.

For additional capabilities, you can install:

• arviz
• celerite2
• corner
• dynesty
• emcee
• scikit-learn

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!