我正在调用一个函数,在每次通过for循环时使用 odeint
(我可以't break anything out of that loop, sadly). But, things are running much slower than I' d希望 . 这是代码:
def get_STM(t_i, t_f, X_ref_i, dxdt, Amat):
"""Evaluate the state transition matrix rate of change for a given A matrix.
"""
STM_i = np.eye(X_ref_i.size).flatten()
args = (dxdt, Amat)
X_aug_i = np.hstack((X_ref_i, STM_i))
t = [t_i, t_f]
# Propogate reference trajectory & STM together!
X_aug_f = odeint(dxdt_interface, X_aug_i, t, args=args)
X_f = X_aug_f[-1, :X_ref_i.size]
STM_f = X_aug_f[-1, X_ref_i.size:].reshape(X_ref_i.size, X_ref_i.size)
return X_f, STM_f
def dxdt_interface(X,t,dxdt,Amat):
"""
Provides an interface between odeint and dxdt
Parameters :
------------
X : (42-by-1 np array) augmented state (with Phi)
t : time
dxdt : (function handle) time derivative of the (6-by-1) state vector
Amat : (function handle) state-space matrix
Returns:
--------
(42-by-1 np.array) time derivative of the components of the augmented state
"""
# State derivative
Xdot = np.zeros_like(X)
X_stacked = np.hstack((X[:6], t))
Xdot_state = dxdt(*(X_stacked))
Xdot[:6] = Xdot_state[:6].T
# STM
Phi = X[6:].reshape((Xdot_state.size, Xdot_state.size))
# State-Space matrix
A = Amat(*(X_stacked))
Xdot[6:] = (A .dot (Phi)).reshape((A.size))
return Xdot
问题是,我在每次运行时调用_867308_大约8640次,这导致232217次调用 dxdt_interface
,大约占总计算时间的70%,每次调用5ms get_STM
(99.9%是由于 odeint
) .
我'm new to SciPy'的集成技术,根据 odeint
的documentation,我无法弄清楚如何加快速度 . 我调查 dxdt_interface
与Numba,但我不能让它工作,因为 dxdt
和 Amat
是象征性的 .
有没有什么技术可以加速 odeint
我错过了?
编辑:下面包含 Amat
和 dxdt
函数 . 请注意,这些不在我的major for循环中调用,它们创建传递给我的 get_STM
函数的符号lambdified函数的句柄(我调用 import sympy as sym
) .
def get_A(use_j3=False):
""" Returns the jacobian of the state time rate of change
Parameters
----------
R : Earth's equatorial radius (m)
theta_dot : Earth's rotation rate (rad/s)
mu : Earth's standard gravitationnal parameter (m^3/s^2)
j2 : second zonal harmonic coefficient
j3 : third zonal harmonic coefficient
Returns
----------
A : (function handle) jacobian of the state time rate of change
"""
theta_dot = EARTH['rotation rate']
R = EARTH['radius']
mu = EARTH['mu']
j2 = EARTH['J2']
if use_j3:
j3 = EARTH['J3']
else:
j3 = 0
# Symbolic derivations
x, y, z, mus, j2s, j3s, Rs, t = sym.symbols('x y z mus j2s j3s Rs t', real=True)
theta_dots = sym.symbols('theta_dots', real=True)
xdot,ydot,zdot = sym.symbols('xdot ydot zdot ', real=True)
X = sym.Matrix([x,y,z,xdot,ydot,zdot])
A_mat = sym.lambdify( (x,y,z,xdot,ydot,zdot,t), dxdt_s().jacobian(X).subs([
(theta_dots, theta_dot),(Rs, R),(j2s,j2),(j3s,j3),(mus,mu)]), modules='numpy')
return A_mat
def Dxdt(use_j3=False):
""" Returns the time derivative of the state vector
Parameters
----------
R : Earth's equatorial radius (m)
theta_dot : Earth's rotation rate (rad/s)
mu : Earth's standard gravitationnal parameter (m^3/s^2)
j2 : second zonal harmonic coefficient
j3 : third zonal harmonic coefficient
Returns
----------
dxdt : (function handle) time derivative of the state vector
"""
theta_dot = EARTH['rotation rate']
R = EARTH['radius']
mu = EARTH['mu']
j2 = EARTH['J2']
if use_j3:
j3 = EARTH['J3']
else:
j3 = 0
# Symbolic derivations
x, y, z, mus, j2s, j3s, Rs, t = sym.symbols('x y z mus j2s j3s Rs t', real=True)
theta_dots = sym.symbols('theta_dots', real=True)
xdot,ydot,zdot = sym.symbols('xdot ydot zdot ', real=True)
dxdt = sym.lambdify( (x,y,z,xdot,ydot,zdot,t), dxdt_s().subs([
(theta_dots, theta_dot),(Rs, R),(j2s,j2),(j3s,j3),(mus,mu)]), modules='numpy')
return dxdt
1 回答
使用
dxdt
和Amat
作为黑盒子,你可以做很多事情来加快速度 . 一种可能性是简化调用它们 .hstack
可能有点矫枉过正 .元组方法快得多:
我会做更多这样的测试来优化
dxdt_interface
调用 .