69 lines
2.4 KiB
Python

import numpy as np
import scipy.integrate as spi
import logging
logger = logging.getLogger(__name__)
def ode_setup(particles: np.ndarray, force_function: callable) -> tuple[np.ndarray, callable]:
"""
Linearizes the ODE system for the particles interacting gravitationally.
Returns:
- the Y0 array corresponding to the initial conditions (x0 and v0)
- the function that computes the right hand side of the ODE with function signature f(t, y)
Assumes that the particles array has the following columns: x, y, z, vx, vy, vz, m.
"""
if particles.shape[1] != 7:
raise ValueError("Particles array must have 7 columns: x, y, z, vx, vy, vz, m")
n = particles.shape[0]
# for scipy integrators we need to flatten the n 3D positions and n 3D velocities
y0 = np.zeros(6*n)
y0[:3*n] = particles[:, :3].flatten()
y0[3*n:] = particles[:, 3:6].flatten()
# the masses don't change we can define them once
masses = particles[:, 6]
logger.debug(f"Reshaped {particles.shape} to y0 with {y0.shape} and masses with {masses.shape}")
def f(y, t):
"""
Computes the right hand side of the ODE system.
The ODE system is linearized around the current positions and velocities.
"""
n = y.size // 6
logger.debug(f"y with shape {y.shape}")
# unsqueeze and unstack to extract the positions and velocities
y = y.reshape((2*n, 3))
x = y[:n, ...]
v = y[n:, ...]
logger.debug(f"Unstacked y into x with shape {x.shape} and v with shape {v.shape}")
# compute the forces
x_with_m = np.zeros((n, 4))
x_with_m[:, :3] = x
x_with_m[:, 3] = masses
forces = force_function(x_with_m)
# compute the accelerations
a = forces / masses[:, None]
a.flatten()
# the [:, None] is to force broadcasting in order to divide each row of forces by the corresponding mass
# reshape into a 1D array
return np.vstack((v, a)).flatten()
return y0, f
def to_particles(y: np.ndarray) -> np.ndarray:
"""
Converts the 1D array y into a 2D array with the shape (n, 6) where n is the number of particles.
The columns are x, y, z, vx, vy, vz
"""
n = y.size // 6
y = y.reshape((2*n, 3))
x = y[:n, ...]
v = y[n:, ...]
return np.hstack((x, v))