Simulation of a set of bodies subjected to gravity#
Scope#
This notebook uses the (Point Mass Dynamics) PMD class to simulate gravitational interaction between massive objects.
Required files
Before using this notebook, download the module PMD.py
and put it in your working directory.
Coding#
%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy import integrate, optimize, spatial
from matplotlib import animation, rc
from PMD import PMD, distances, MetaForce
rc("animation", html="html5")
np.random.seed(333)
class Gravity(PMD):
def __init__(self, G=6.67e-11, **kwargs):
"""
2D gravity
"""
self.G = G
super().__init__(**kwargs)
def derivative(self, X, t, cutoff_radius=1.0e-2):
"""
Acceleration de chaque masse !
"""
m, G = self.m, self.G
n = len(m)
P = X[: 2 * n].reshape(n, 2)
V = X[2 * n :].reshape(n, 2)
M = m * m[:, np.newaxis]
D, R, U = distances(P)
np.fill_diagonal(R, np.inf)
if cutoff_radius > 0.0:
R = np.where(R > cutoff_radius, R, cutoff_radius)
F = ((G * M * R**-2)[:, :, np.newaxis] * U).sum(axis=0)
A = (F.T / m).T
X2 = X.copy()
X2[: 2 * n] = V.flatten()
X2[2 * n :] = A.flatten()
return X2
Animation#
# SETUP
G = 1.0e03
nr = 3
nt = 1
nm = nr * nt + 1
m = np.ones(nm) * 4.0e-3
m[0] = 1.0
r = np.linspace(1.0, 2.0, nr)
theta = np.linspace(0.0, np.pi * 2, nt, endpoint=False)
R, Theta = np.meshgrid(r, theta)
r = np.concatenate([[0.0], R.flatten()])
theta = np.concatenate([[0.0], Theta.flatten()])
v = np.zeros_like(r)
v[1:] = (
(G * m[0] / r[1:]) ** 0.5
* 0.75
* np.random.normal(loc=1.0, scale=0.05, size=nm - 1)
)
x = r * np.cos(theta)
y = r * np.sin(theta)
vx = -v * np.sin(theta)
vy = v * np.cos(theta)
P = np.array([x, y]).transpose()
V = np.array([vx, vy]).transpose()
vG = (V * m[:, np.newaxis]).sum(axis=0) / m.sum()
V -= vG
s = Gravity(m=m, P=P, V=V, G=G, nk=4000)
dt = 1.0e-3
nt = 50
pcolors = "r"
tcolors = "k"
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_aspect("equal")
margin = 1.0
plt.axis([-2, 2, -2, 2])
plt.grid()
ax.axis("off")
points = []
msize = 10.0 * (s.m / s.m.max()) ** (1.0 / 6.0)
for i in range(nm):
plc = len(pcolors)
pc = pcolors[i % plc]
tlc = len(tcolors)
tc = tcolors[i % tlc]
(trail,) = ax.plot([], [], "-" + tc)
(point,) = ax.plot([], [], "o" + pc, markersize=msize[i])
points.append(point)
points.append(trail)
def init():
for i in range(2 * nm):
points[i].set_data([], [])
return points
def animate(i):
s.solve(dt, nt)
x, y = s.xy()
for i in range(nm):
points[2 * i].set_data(x[i : i + 1], y[i : i + 1])
xt, yt = s.trail(i)
points[2 * i + 1].set_data(xt, yt)
return points
anim = animation.FuncAnimation(
fig, animate, init_func=init, frames=1600, interval=20, blit=True
)
plt.close()
anim
# plt.show()