Kalman-and-Bayesian-Filters.../exp/RungeKutta.py

111 lines
2.2 KiB
Python

# -*- coding: utf-8 -*-
"""
Created on Sat Jul 05 09:54:39 2014
@author: rlabbe
"""
from __future__ import division
import matplotlib.pyplot as plt
from scipy.integrate import ode
class BallEuler(object):
def __init__(self, y=100., vel=10.):
self.x = 0.
self.y = y
self.vel = vel
self.y_vel = 0.0
def step (self, dt):
g = -9.8
self.x += self.vel*dt
self.y += self.y_vel*dt
self.y_vel += g*dt
#print self.x, self.y
def rk4(y, x, dx, f):
"""computes 4th order Runge-Kutta for dy/dx.
y is the initial value for y
x is the initial value for x
dx is the difference in x (e.g. the time step)
f is a callable function (y, x) that you supply to compute dy/dx for
the specified values.
"""
k1 = dx * f(y, x)
k2 = dx * f(y + 0.5*k1, x + 0.5*dx)
k3 = dx * f(y + 0.5*k2, x + 0.5*dx)
k4 = dx * f(y + k3, x + dx)
return y + (k1 + 2*k2 + 2*k3 + k4) / 6
def fy(y, t):
""" returns velocity of ball at time t.
return -9.8*t
def fx(y, t):
""" returns velocity of ball. Need to set vx.vel prior to first call
return fx.vel
class BallRungeKutta(object):
def __init__(self, y=100., vel=10.):
self.x = 0.
self.y = y
self.vel = vel
self.y_vel = 0.0
self.t = 0
fx.vel = vel
def step2 (self, dt):
self.x = rk4 (self.x, self.t, dt, fx)
self.y = rk4 (self.y, self.t, dt, fy)
self.t += dt
print self.x, self.y
if __name__ == "__main__":
dt = 1./30
y0 = 15.
vel = 100.
be = BallEuler (y=y0, vel=vel)
brk = BallRungeKutta (y=y0, vel=vel)
solver = ode(f).set_integrator('dopri5')
solver.set_initial_value(y0, 0)
t = 0
y = y0
while be.y >= 0:
be.step (dt)
#plt.scatter (be.x, be.y, color='red')
while brk.y >= 0:
brk.step2 (dt)
y = solver.integrate(t+dt)
t += dt
#print brk.x, y[0]
plt.scatter (brk.x, brk.y, color='blue', marker='v')
plt.scatter (brk.x, y[0], color='green', marker='+')