Metadata-Version: 2.1
Name: spock
Version: 1.4.0
Summary: Stability of Planetary Orbital Configurations Klassifier
Home-page: https://github.com/dtamayo/spock
Author: Daniel Tamayo
Author-email: tamayo.daniel@gmail.com
License: GPL
Description: # SPOCK 🖖
        
        **Stability of Planetary Orbital Configurations Klassifier**
        
        [![image](https://badge.fury.io/py/spock.svg)](https://badge.fury.io/py/spock)
        [![image](https://travis-ci.com/dtamayo/spock.svg?branch=master)](https://travis-ci.com/dtamayo/spock)
        [![image](http://img.shields.io/badge/license-GPL-green.svg?style=flat)](https://github.com/dtamayo/spock/blob/master/LICENSE)
        [![image](https://img.shields.io/badge/launch-binder-ff69b4.svg?style=flat)](http://mybinder.org/repo/dtamayo/spock)
        [![image](http://img.shields.io/badge/arXiv-2007.06521-green.svg?style=flat)](http://arxiv.org/abs/2007.06521)
        [![image](http://img.shields.io/badge/arXiv-2101.04117-green.svg?style=flat)](https://arxiv.org/abs/2101.04117)
        
        ![image](https://raw.githubusercontent.com/dtamayo/spock/master/paper_plots/spockpr.jpg)
        
        [Documentation](https://spock-instability.readthedocs.io/en/latest/)
        
        The SPOCK package incorporates several machine learning and analytical tools for estimating the stability of compact planetary configurations.
        All estimators use a common API to facilitate comparisons between them and with N-body integrations.
        
        # Quickstart
        
        Let's predict the probability that a given 3-planet system is stable
        past 1 billion orbits with the XGBoost-based classifier of [Tamayo et al., 2020](http://arxiv.org/abs/2007.06521).
        
        ```python
        import rebound
        from spock import FeatureClassifier
        feature_model = FeatureClassifier()
        
        sim = rebound.Simulation()
        sim.add(m=1.)
        sim.add(m=1.e-5, P=1., e=0.03, pomega=2., l=0.5)
        sim.add(m=1.e-5, P=1.2, e=0.03, pomega=3., l=3.)
        sim.add(m=1.e-5, P=1.5, e=0.03, pomega=1.5, l=2.)
        sim.move_to_com()
        
        print(feature_model.predict_stable(sim))
        # >>> 0.06591137
        ```
        
        That model provides a simple scalar probability of stability over a billion orbits. 
        We can instead estimate its median expected instability time using the deep regressor from [Cranmer et al., 2021](https://arxiv.org/abs/2101.04117).
        
        ```python
        from spock import DeepRegressor
        deep_model = DeepRegressor()
        
        median, lower, upper = deep_model.predict_instability_time(sim, samples=10000)
        print(int(median))
        # >>> 242570.1378387966
        ```
        
        The returned time is expressed in the time units used in setting up the REBOUND Simulation above.
        Since we set the innermost planet orbit to unity, this corresponds to 242570 innermost planet orbits.
        
        Finally, we can compare these results to the semi-analytic criterion of [Tamayo et al., 2021]() for how likely the configuration is to be dynamically chaotic. .
        This is not a one-to-one comparison, but configurations that are chaotic through two-body MMR overlap are generally unstable on long timescales (see paper and examples).
        
        ```python
        from spock import AnalyticalClassifier
        analytical_model = AnalyticalClassifier()
        
        print(analytical_model.predict_stable(sim))
        # >>> 0.0
        ```
        
        To match up with the above classifiers, the analytical classifier returns the probability the configuration is *regular*, i.e., not chaotic.
        A probability of zero therefore corresponds to confidently chaotic.
        See [this example](https://github.com/dtamayo/spock/blob/master/jupyter_examples/QuickStart.ipynb) for more information about the analytical model.
        
        # Examples
        
        [Colab tutorial](https://colab.research.google.com/drive/1R3NrPmtI5DZFq_VZtv8gowINBrXM85Zv?usp=sharing)
        for the deep regressor.
        
        The example notebooks contain many additional examples:
        [jupyter\_examples/](https://github.com/dtamayo/spock/tree/master/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](brew.sh), 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).
        
Keywords: astronomy astrophysics exoplanets stability
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
