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KozaiLidov/lib/integrator.py
2021-11-05 22:40:33 +01:00

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Python
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#!/usr/bin/python
# -*- coding:utf-8 -*-
"""
Implementation of the various integrators for numerical integration.
Comes from the assumption that the problem is analytically defined in position-momentum (q-p) space for a given hamiltonian H.
"""
from os import system
import numpy as np
from lib.plots import DynamicUpdate
globals()['G'] = 6.67e-11 #Gravitational constant in SI units
globals()['Ms'] = 2e30 #Solar mass in kg
globals()['au'] = 1.5e11 #Astronomical unit in m
def dv_dt(m_array, q_array):
"""
Time derivative of the velocity, given by the position derivative of the Hamiltonian.
dv/dt = -1/m*dH/dq
"""
dv_array = np.zeros(q_array.shape)
for i in range(q_array.shape[0]):
q_j = np.delete(q_array, i, 0)
m_j = np.delete(m_array, i, 0)
dv_array[i] = -G*np.sum((m_j*(q_j-q_array[i])).T/np.sqrt(np.sum((q_j-q_array[i])**2, axis=1))**3, axis=1).T
dv_array[np.isnan(dv_array)] = 0.
return dv_array
def frogleap(duration, step, dyn_syst, recover_param=False, display=False):
"""
Leapfrog integrator for first order partial differential equations.
iteration : half-step drift -> full-step kick -> half-step drift
"""
N = np.ceil(duration/step).astype(int)
q_array = dyn_syst.get_positions()
v_array = dyn_syst.get_velocities()
masses = dyn_syst.get_masses()
m_array = np.ones(q_array.shape)
for i in range(q_array.shape[0]):
m_array[i,:] = masses[i]
E = np.zeros(N)
L = np.zeros((N,3))
if display:
try:
system("mkdir tmp")
except IOError:
system("rm tmp/*")
d = DynamicUpdate()
d.on_launch()
for j in range(N):
# half-step drift
q_array, v_array = q_array + step/2*v_array , v_array
# full-step kick
q_array, v_array = q_array , v_array - step*dv_dt(m_array, q_array)
# half-step drift
q_array, v_array = q_array + step/2*v_array , v_array
for i, body in enumerate(dyn_syst.bodylist):
body.q = q_array[i]
body.v = v_array[i]
body.p = body.v*body.m
dyn_syst.COMShift()
E[j] = dyn_syst.Eval()
L[j] = dyn_syst.Lval()
if display:
# display progression
if len(dyn_syst.bodylist) == 1:
d.on_running(q_array[0], q_array[1], q_array[2], step=j, label="step {0:d}/{1:d}".format(j,N))
else:
d.on_running(q_array[:,0], q_array[:,1], q_array[:,2], step=j, label="step {0:d}/{1:d}".format(j,N))
if display:
system("convert -delay 5 -loop 0 tmp/?????.png tmp/temp.gif && rm tmp/?????.png")
system("convert tmp/temp.gif -fuzz 30% -layers Optimize plots/dynsyst.gif && rm tmp/temp.gif")
if recover_param:
return E, L