marblestation/posidonius

N-Body simulator for planetary and/or binary systems

Posidonius

Posidonius is a N-body code based on the tidal model used in Mercury-T (Bolmont et al. 2015). It uses a symplectic integrator (WHFast, Rein & Tamayo 2015) to compute the evolution of positions and velocities, which is also combined with a midpoint integrator to calculate the spin evolution in a consistent way. As Mercury-T, Posidonius takes into account tidal forces, rotational-flattening effects and general relativity corrections. It also includes different evolution models for FGKML stars and gaseous planets.

The N-Body code is written in Rust (Blanco-Cuaresma et al. 2017) and a python package is provided to easily define simulation cases in JSON format, which is readable by the Posidonius integrator.

Requirements

• Rust: see rustup
• Python: see anaconda

Installation

First, install the N-body simulator by running from the current directory:

cargo install --path . --force

The executable will be copied into $HOME/.cargo/bin/. Then, install the python package to create cases by running:

curl -O https://www.cfa.harvard.edu/~sblancoc/posidonius/input.tar.gz
tar -zxvf input.tar.gz && rm -f input.tar.gz
python setup.py install --user

The --user flag will install the package in your $HOME/.local/lib/python2.7/site-packages/.

Both tools can be uninstalled by executing:

cargo uninstall posidonius
python setup.py install --user --record files.txt
cat files.txt | xargs rm -rf && rm -f files.txt

Usage

Create a JSON case

The user can design his/her own simulation with a python script, which will create a JSON file with the simulation description. This file can be read later on by Posidonius to start the simulation. The code includes several examples in the cases directory, and they can be executed by running:

python cases/Bolmont_et_al_2015/case3.py target/case3.json
python cases/Bolmont_et_al_2015/case4.py target/case4.json
python cases/Bolmont_et_al_2015/case7.py target/case7.json
python cases/example.py target/example.json

Start the simulation of a JSON case

The simulations can be started using JSON files (which describe the initial conditions). When starting a simulation, the recovery and historic snapshot file names should be specified. The former will contain the information needed to resume interrupted simulations, while he latter stores the evolution of the simulation over the years.

posidonius start target/case3.json target/case3.bin target/case3_history.bin
posidonius start target/case4.json target/case4.bin target/case4_history.bin
posidonius start target/case7.json target/case7.bin target/case7_history.bin
posidonius start target/example.json target/example.bin target/example_history.bin

The flag --silent can be added to avoid printing the current year of the simulation. An execution time limit can also be specified with the flag --limit, this can be useful for supercomputers that only allow processes to last a given amount of real time (not simulation time).

Resume an interrupted simulation

Interrupted simulations can be restored using the recovery snapshot file. The historic snapshot filename has to be specified also to continue storing the history of the simulation.

posidonius resume target/case3.bin target/case3_history.bin
posidonius resume target/case4.bin target/case4_history.bin
posidonius resume target/case7.bin target/case7_history.bin
posidonius resume target/example.bin target/example_history.bin

The flag --silent can be added to avoid printing the current year of the simulation. n execution time limit can also be specified with the flag --limit, this can be useful for supercomputers that only allow processes to last a given amount of real time (not simulation time). In case the user wants to change historic or recovery snapshot periods when resuming a simulation, it can be done with the flags --historic-snapshot-period and --recovery-snapshot-period plus the new period (in days) after each one. The flag --time-limit can be used to change the simulation time limit (e.g., to increase it for a previous short simulation that looks promising).

Analyse a simulation

While a simulation is in progress or when it has ended, the historic snapshot file can be converted to plain text tab-separated files (one per body in the system):

python scripts/raw_history.py target/case3_history.bin

In the same way, the historic snapshot file can be interpreted by transforming positions/velocities to heliocentric coordinates using the most massive body as the reference one, computing keplerian parameters, and generating a plot + plain text tab-separated file with the history of the simulation:

python scripts/explore_history.py target/case3.json   target/case3_history.bin
python scripts/explore_history.py target/case4.json   target/case4_history.bin
python scripts/explore_history.py target/case7.json   target/case7_history.bin
python scripts/explore_history.py target/example.json target/example_history.bin

To explore what possible resonances might be present in the system:

python scripts/explore_timed_resonances.py target/case3_history.bin
python scripts/explore_timed_resonances.py target/case4_history.bin
python scripts/explore_timed_resonances.py target/case7_history.bin
python scripts/explore_timed_resonances.py target/example_history.bin

Finally, to study a given resonance (e.g., 3:2) between planet one and two:

python scripts/explore_single_resonance.py target/case3_history.bin 1 2 3 2
python scripts/explore_single_resonance.py target/case4_history.bin 1 2 3 2
python scripts/explore_single_resonance.py target/case7_history.bin 1 2 3 2
python scripts/explore_single_resonance.py target/example_history.bin 1 2 3 2

Testing

This section is intended only for developers.

NOTE: Rust floats (f64) follow IEEE_754 decimal64, hence only the first 16 decimals are significant (see page 74, Beginning Rust book).

Python

Prepare a python environment:

python3 -m venv venv # or "virtualenv venv" if still using Python 2
source venv/bin/activate
pip install -r requirements.txt
pip install -r dev-requirements.txt

Run the python tests:

pytest

If tests fail, it is because the current posidonius version generate JSON files that differ from the previous version. The error will contain the names of the files that differ so that the developer can compare them:

vimdiff posidonius/tests/data/test_tides-enabled_tides/case.json posidonius/tests/data/tmp/test_tides-enabled_tides/case.json

If the new file (in the tmp directory) is the expected one given the changes done to the code, then the case.json files could be deleted and recreated by running the test again:

find posidonius/tests/data/ -name 'case.json' -delete
pytest

Rust

Run the rust tests:

cargo test

Rust

If the test results for a case differ from previous executions and the difference is justified and expected by a change in the code, then all the files particle_*.json can be erased and recreated:

find tests/data/ -name 'particle_*.json' -delete
cargo test

But if the difference is not expected, then the changes made to the code have affected the expected behavior of posidonius and the changes you have made to your code to identify the reason/bug.

Rust vs Python

If the results when using a JSON generated with rust differ from the results when using a JSON generated by python, the first thing to check is if there are differences between the JSON files:

vimdiff tests/data/test_tides-enabled_tides/case.json posidonius/tests/data/test_tides-enabled_tides/case.json

If it is necessary to recreate all the rust test's case.json, it is enough to delete them all, run the test again and run the cleaning script (re-format the JSON files to make them easily comparable to the python generated ones):

find tests/data/ -name 'case.json' -delete
cargo test --no-fail-fast
python scripts/clean_json.py

If the results still differ, it is necessary to verify the changes you have made to your code to identify the reason/bug.

Benchmarks

We can use benchmarks to assess the relative performance of code changes (i.e., statistically significant variations in execution time / speed). First, we need a reference measurement before we make any changes:

rm -rf target/criterion/
cargo bench -- --save-baseline reference

Second, we can apply our changes to the code. And finally, we can run the benchmarks again to compare it to the previous reference measurement:

cargo bench -- --baseline reference

If we need to execute only some of the benchmarks, they can be filtered by their name:

cargo bench -- --baseline reference calculate_additional_effects/all

If gnuplot is installed in the system, a report with plots will be generated/updated after each execution:

open target/criterion/report/index.html

Finally, multiple baselines can also be compared among them:

cargo install critcmp --force
cargo bench -- --save-baseline before calculate_additional_effects/all
cargo bench -- --save-baseline after calculate_additional_effects/all
critcmp before after

Performance bottlenecks

Using cargo-flamegraph it is possible to generate a flame graph, which allows to easily visualize what elements in the code take more execution time (i.e., identify most expensive parts of posidonius at runtime). In the flame graph, the main function of posidonius will be closer to the bottom, and the called functions will be stacked on top.

The flame graph allows the developer to identify parts of the code to be optimized, but properly measuring the impact of the optimization needs to be done with the benchmark tests.

Install cargo-flamegraph and its system dependencies in GNU/Linux:

sudo apt install linux-tools-common linux-tools-generic # only in GNU/Linux (debian-based)
cargo install flamegraph --force

Setup the system to allow performance measurement for non-root users (only for GNU/Linux):

sudo sh -c 'echo -1 >/proc/sys/kernel/perf_event_paranoid'
sudo sysctl -w kernel.perf_event_paranoid=-1

Run an example to build a flame graph (add --root after cargo flamegraph if this is run on a system different from GNU/Linux, and it will ask for your user password):

python cases/example.py target/example.json
rm -f target/example*bin
cargo flamegraph --bin posidonius -- start target/example.json target/example.bin target/example_history.bin

The previous command will generate the files perf.data and flamegraph.svg, the former can be inspected with the perf command (only in GNU/Linux) while the latter is better visualized in a browser (it allows easy interaction to explore the graph and expand the function names).

posidonius's Issues

multi planets but no tides in all?

Hi,

Is it possible to set tides only for some planets, and ignore others, for example in a system where some planets are close-in but others far away from the host star. I guess that this would speed up my simulation..

Thanks!

starting the simulation

Hi,

after completed all the steps, I want to run the example JSON case by :

~/posidonius-2018.02.01/cases$ posidonius start target/example.json target/example.bin target/example_history.bin

however, I obtain :

posidonius: command not found

Any clue about what I'm doing wrong?

thanks in advance