SPOCK 🖖 #

Stability of Planetary Orbital Configurations Klassifier

Quickstart #

Let’s predict the probability that a given 3-planet system is stable past 1 billion orbits with the XGBoost-based classifier, and then compute its median expected instability time with the deep regressor:

import rebound
from spock import FeatureClassifier, DeepRegressor
feature_model = FeatureClassifier()
deep_model = DeepRegressor()

sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-5, P=1., e=0.03, l=0.3)
sim.add(m=1.e-5, P=1.2, e=0.03, l=2.8)
sim.add(m=1.e-5, P=1.5, e=0.03, l=-0.5)
sim.move_to_com()

# XGBoost-based classifier
print(feature_model.predict_stable(sim))
# >>> 0.011505529

# Bayesian neural net-based regressor
median, lower, upper = deep_model.predict_instability_time(sim, samples=10000)
print(int(median))
# >>> 419759

# This time in the time units you used in setting up the REBOUND Simulation above
# Since we set the innermost planet orbit to unity, this corresponds to 419759 innermost planet orbits

Examples #

Colab tutorial for the deep regressor.

The example notebooks contain many additional examples: jupyter_examples/.

Installation #

SPOCK is compatible with both Linux and Mac. SPOCK relies on XGBoost, which has installation issues with OpenMP on Mac OSX. If you have problems (https://github.com/dmlc/xgboost/issues/4477), the easiest way is probably to install homebrew, and:

brew install libomp

The most straightforward way to avoid any version conflicts is to download the Anaconda Python distribution and make a separate conda environment.

Here we create we create a new conda environment called spock and install all the required dependencies

conda create -q --name spock -c pytorch -c conda-forge python=3.7 numpy scipy pandas scikit-learn matplotlib torchvision pytorch xgboost rebound einops jupyter pytorch-lightning ipython h5py
conda activate spock
pip install spock

Each time you want to use spock you will first have to activate this spock conda environment (google conda environments).