Moving Planets Around is an education book project that teaches students to build a state-of-the-art N-Body code for planetary system dynamics from the stretch. The code built throughout the storyline of the book is hosted here. The book has been published by the MIT Press in September 2020. See: https://mitpress.mit.edu/books/moving-planets-around
In the book Moving Planets Around, Alice and Bob are two students interested in building an N-body code. With the help from Prof. Starmover, they eventually achieved their ambition. The resulting source code ABIE is named after them.
ABIE is a general-purpose direct N-body library for astrophysical simulations. It is especially well-suited for simulating planetary systems with or without a large number of test particles (e.g., comets, asteroids). ABIE implements all its integrators in both Python (for educational purpose) and in C (for performance). The currently supported integrators are:
- Forward Euler integrator
- Leapfrog
- Adams-Bashforth
- Runge-Kutta
- Gauss-Radau15 (default)
- Wisdom-Holman
In the scenarios where the number of particles is large, ABIE makes use of the GPUs (if available) to accelerate the calculation of gravity.
ABIE is a Python library with C backend. Like most Python packages, it can simply be installed with
pip install abie
By default, the build script will determine whether the local system has CUDA support and build ABIE accordingly. This can be overriden by specifying the environmental variable export WITH_CUDA=0 to force ABIE to be built without CUDA support.
A simple ABIE simulation can be created like this:
import abie
import numpy as np
# create an ABIE instance (Units: MSun, au, yr)
sim = abie.ABIE(CONST_G=4*np.pi**2, name="example_simulation", device=-1)
# select the integrator
sim.integrator = "WisdomHolman"
# add particles
sim.add(mass=1.0, pos=[0., 0., 0.,], vel=[0., 0., 0.,], name="Sun")
sim.add(mass=3.e-6, a=1.0, name='Earth', primary='Sun')
# Use `sim.add()` to add as many particles as needed
# print an overview of particles
print(sim.particles)
# initialize the integrator
sim.initialize()
# perform the integration for 1000 years
sim.integrate(1000)
sim.stop()In the example above, a simple planetary system is setup, such that a solar-type star named "Sun" is orbited by an Earth-mass planet at a=1 au. A particle can either be added using Cartesian coordinates or orbital elements. The Sun is sitting at the center of the reference frame with zero initial velocity. When the simulation is done, you will find a HDF5 file named example_simulation.h5 containing all the simulation data.
The argument device=-1 in the abie.ABIE() constructor tells the code to use CPU to carry out the simulation. In the scenario where the number of particles is high, GPU acceleration can be enabled with a non-negative device ID. For example, if the user wishes to use the first GPU, then device=0, and the second GPU (if exists) can be chosen with device=1, and so on.
It is also very straightforward to set up hierarchical systems. For examiple, consider a moon orbiting a planet, and that particular planet orbits the center-of-mass of a circumbinary stellar system:
sim.add(mass=1.0, pos=[0., 0., 0.,], vel=[0., 0., 0.,], name="star1") # add the first star
sim.add(mass=1.0, a=1, name="star2", primary='star1') # add the second star
sim.particles["star1"].primary = sim.particles["star2"] # make the second star also orbit the first one
# make a planet orbiting the center-of-mass of star1 and star2
sim.add(mass=1.e-5, a=10, e=0.2, i=0.1, name='planet', primary=['star1', 'star2'])
# make a moon orbiting the planet
sim.add(mass=1.e-9, a=0.01, e=0.0, i=0.0, name='moon', primary='planet') All simulation data can be found in the HDF5 output file. Alternative, it is also possible to use the recorder facility to record the simulation data. For example, if we are running a Jupyter notebook and would like to plot the orbital elements, we could do
# tell the recorder that semi-major axes, eccentricities, inclinations, time, and energy error should be recorded
sim.record_simulation(quantities=["a", "ecc", "inc", "time", "energy"]).start()
# perform the integration for some time
sim.integrate(200)After this, the simulation data can be accessed with sim.data, which is a dictionary. For example, if we would like to plot the orbital eccentrcities evolution, we could simply do
import matplotlib.pyplot as plt
plt.plot(sim.data['time'], sim.data['ecc'])
plt.show()Likewise, generate a 2D scatter plot of x-y would be as easy as
sim.record_simulation(quantities=["x", "y", "z", "time"]).start()
# perform the integration for some time
sim.integrate(200)
plt.scatter(sim.data['x'], sim.data['y'])ABIE uses the HDF5 format to store its integration output data. The internal layout of the HDF5 file looks like this:
/Step#0
ecc: eccentricities of each object (1 - N) as a function of time (0, t-1): [t * N] array
inc: inclinations of each object (1 - N) as a function of time (0, t-1): [t * N] array
hash: unique integer identifier for each particle: [t * N] array
mass: masses of each planet: [t * N] array
ptype: particle type of each object. 0 = massive particles; 1 = test particles; 2 = low-mass particles. [t * N] array
radius: radius of each object. [t * N] array
semi: semi-major axis of each object. [t * N] array
time: the time vector. [t * 1] vector
vx: [t * N]
vy: [t * N]
vz: [t * N]
x: [t * N]
y: [t * N]
z: [t * N]
/Step#1
...
/Step#2
...
...
/Step#n
For efficient output, ABIE maintains a buffer to temporarily store its integration data. The default buffer length is set to 1024, which means that it will accumulate 1024 snapshot output until it flushes the data into the HDF5 file and creates a Step#n group. The resulting HDF5 datasets can be loaded easily using the h5py package:
import h5py
h5f = h5py.File('data.hdf5', 'r')
semi = h5f['/Step#0/semi'][()]
ecc = h5f['/Step#0/ecc'][()]
...
h5f.close()Sometimes, it is more convenient to get rid of the Step#n data structure in the HDF5 file (i.e. combine Step#0, Step#1, ..., Step#n into flatten arrays). The ABIE package contains a tool to seralize the snapshot. For example, suppose that ABIE generates a data file data.hdf5 contains the Step#n structure, the following command
python snapshot_serialization.py -f data.hdf5
will generate a flattened file called data (still in hdf5 format). In this case, the data can be accessed in this way:
import h5py
h5f = h5py.File('data.hdf5', 'r')
semi = h5f['/semi'][()] # gets the semi-axis array for the entire simulation
ecc = h5f['/ecc'][()] # gets the eccentricity array for the entire simulation
...
h5f.close()By default, ABIE will execute the C implementation of the Gauss-Radau15 integrator. This integrator is well-optimized and preserves energy to ~ 10^{-15}. It is very straightforward to change the integrator from the Python interface:
from abie import ABIE
sim = ABIE()
sim.CONST_G = 1.0
sim.integrator = 'GaussRadau15' # or 'WisdomHolman', 'LeapFrog', etc.By default, ABIE uses double precision. For some special cases (for example, integrating a Kozai-Lidov system where the eccentricity can be very high), the precision of the integrator can be adjusted by simply changing the following lines in src/Makefile from
LONGDOUBLE = 0to
LONGDOUBLE = 1And run make clean; make again. This will causes the integrator to use the long double data type. When using the Gauss-Radau15 integrator, the energy conservation can be better than 10^{-16} in this case (shown as dE/E = 0 in the terminal), even after evolving the system through multiple Kozai cycles. This, however, will takes about 2x time to finish evolving the same system.
When ABIE is being installed as a Python package, the setup.py script will automatically determine whether your machine has CUDA GPUs or not. If GPUs are presented in the machine, it will automatically turn on the relevant compilation flags to compile the ABIE C library.
If you would like to directly use ABIE as a C library instead, please edit the src/Makefile:
GPU = 0to
GPU = 1And then recompile the code. Note that it is not recommended to use GPU for small-N systems.