Estimating Lyapunov Spectra of ODEs using Python

Wolf et al. (1985) outlined an algorithm that estimates the Lyapunov spectra of systems whose equations are known using local Jacobian matrices and Gram-Schmidt orthonormalization. Python code is available for Wolf’s algorithm and discrete maps and their inverted counterparts.  I have adapted this code to estimate Lyapunov spectra for continuous-time systems like the Lorenz attractor and Rossler attractor. Additionally, Python code is available to generate time series for ordinary differential equations. Lyapunov spectrum code is also available on Clint Sprott’s website.

Source Code:

import math, operator, random
h = 0.01
cmax = 1000     #   number of iterations to perform
choice = input("Which system would you like?\n (1) Rossler \n (2) Lorenz\n")
while str(choice) != "1" and str(choice) != "2":
  print("Please select 1 or 2")
  choice = input("Which system would you like?\n (1) Rossler \n (2) Lorenz\n")

print "\n",
a = 0.2
b = 0.2
c = 5.7

def derivs(x, xnew, n):
  if choice == 1:
    return Rossler(x, xnew, n)
  else:
    return Lorenz(x, xnew, n)

def Rossler(x, xnew, n):
  # Nonlinear Rossler Equations
  xnew[1]  = -x[2]-x[3]
  xnew[2]  = x[1] + a * x[2]
  xnew[3]  = b + x[3] * (x[1] - c)
  # Linearized Rossler Equations
  xnew[4]  = -1*x[7]-x[10]
  xnew[5]  = -1*x[8]-x[11]
  xnew[6]  = -1*x[9]-x[12]
  xnew[7]  = x[4] + a*x[7]
  xnew[8]  = x[5] + a*x[8]
  xnew[9]  = x[6] + a*x[9]
  xnew[10] = x[3]*x[4] + x[1]*x[10] - c*x[10]
  xnew[11] = x[3]*x[5] + x[1]*x[11] - c*x[11]
  xnew[12] = x[3]*x[6] + x[1]*x[12] - c*x[12]
  return [x, xnew]

def Lorenz(x, xnew, n):
  # Nonlinear Lorenz Equations
  xnew[1]  = 10 * (x[2] - x[1])
  xnew[2]  = -1*x[1] * x[3] + 28 * x[1] - x[2]
  xnew[3]  = x[1] * x[2] - 8/3.0 * x[3]
  # Linearized Lorenz Equations
  xnew[4]  = -10 * x[4] + 10 * x[7]
  xnew[5]  = -10 * x[5] + 10 * x[8]
  xnew[6]  = -10 * x[6] + 10 * x[9]
  xnew[7]  = 28*x[4]-x[3]*x[4] - x[7] - x[1]*x[10]
  xnew[8]  = 28*x[5]-x[3]*x[5] - x[8] - x[1]*x[11]
  xnew[9]  = 28*x[6]-x[3]*x[6] - x[9] - x[1]*x[12]
  xnew[10] = x[2]*x[4] + x[1]*x[7] - 8/3.0 * x[10]
  xnew[11] = x[2]*x[5] + x[1]*x[8] - 8/3.0 * x[11]
  xnew[12] = x[2]*x[6] + x[1]*x[9] - 8/3.0 * x[12]
  return [x, xnew] 

def timeseries(cmax):
  X0 = []
  Y0 = []
  Z0 = []
  xList = []
  yList = []
  zList = []
  changeInTime = h
  # Initial conditions
  if choice == 1:
    # Rossler
    X0.append(0.01)
    Y0.append(0.01)
    Z0.append(0.01)
  else:
    # Lorenz
    X0.append(0)
    Y0.append(1)
    Z0.append(0)
  t = 0
  while len(xList) <= cmax:
    [x,y,z] = Rk4o(X0, Y0, Z0, h, len(X0))
    X0.append(x)
    Y0.append(y)
    Z0.append(z)
    if 200 < t:
      xList.append(x)
      yList.append(y)
      zList.append(z)
    changeInTime += h
    t = t + 1
  return [xList, yList, zList]

def f(x,y,z):
  if choice == 1:
    dxdt = -y-z
  else:
    dxdt = 10 * (y - x)
  return dxdt

def g(x,y,z):
  if choice == 1:
    dydt = x + a * y
  else:
    dydt = 28 * x - y - x*z
  return dydt

def e(x,y,z):
  if choice == 1:
    dzdt = b + z * (x - c)
  else:
    dzdt = x * y - 8/3.0 * z
  return dzdt

def Rk4o(xList, yList, zList, h, t):
  k1x = h*f(xList[t-1],yList[t-1], zList[t-1])
  k1y = h*g(xList[t-1],yList[t-1], zList[t-1])
  k1z = h*e(xList[t-1],yList[t-1], zList[t-1])

  k2x = h*f(xList[t-1] + k1x/2,yList[t-1] + k1y/2, zList[t-1] + k1y/2)
  k2y = h*g(xList[t-1] + k1x/2,yList[t-1] + k1y/2, zList[t-1] + k1y/2)
  k2z = h*e(xList[t-1] + k1x/2,yList[t-1] + k1y/2, zList[t-1] + k1y/2)

  k3x = h*f(xList[t-1] + k2x/2,yList[t-1] + k2y/2, zList[t-1] + k2y/2)
  k3y = h*g(xList[t-1] + k2x/2,yList[t-1] + k2y/2, zList[t-1] + k2y/2)
  k3z = h*e(xList[t-1] + k2x/2,yList[t-1] + k2y/2, zList[t-1] + k2y/2)

  k4x = h*f(xList[t-1] + k3x/2,yList[t-1] + k3y/2, zList[t-1] + k3y/2)
  k4y = h*g(xList[t-1] + k3x/2,yList[t-1] + k3y/2, zList[t-1] + k3y/2)
  k4z = h*e(xList[t-1] + k3x/2,yList[t-1] + k3y/2, zList[t-1] + k3y/2)

  x = xList[t-1] + k1x/6 + k2x/3 + k3x/3 + k4x/6
  y = yList[t-1] + k1y/6 + k2y/3 + k3y/3 + k4y/6
  z = zList[t-1] + k1z/6 + k2z/3 + k3z/3 + k4z/6
  return [x,y,z]

n = 3           #   number of variables in nonlinear system
nn=n*(n+1)      #   total number of variables (nonlinear + linear)
m = 0
x = []
xnew = []
v = []
ltot = []
znorm = []
gsc = []
A = []
B = []
C = []
D = []
i = 0
while i <= nn:
  x.append(0)
  xnew.append(0)
  v.append(0)
  A.append(0)
  B.append(0)
  C.append(0)
  D.append(0)
  i = i + 1

i = 0
while i <= n:
  ltot.append(0)
  znorm.append(0)
  gsc.append(0)
  i = i + 1

irate=10      #   integration steps per reorthonormalization
io= 100       #   number of iterations between normalization

#   initial conditions for nonlinear maps
#   must be within the basin of attraction

# Generate a random transient before starting the initial conditions
i = 1
while i <= n:
  v[i] = 0.001
  i = i + 1

transient = random.randint(n,100000)
# Generate the initial conditions for the system
[tempx,tempy,tempz] = timeseries(transient)

v[1] = tempx[len(tempx)-1]
v[2] = tempy[len(tempy)-1]
v[3] = tempz[len(tempz)-1]

i = n+1
while i <= nn:  #   initial conditions for linearized maps
  v[i]=0        #   Don't mess with these; they are problem independent!
  i = i + 1

i = 1
while i <= n:
  v[(n+1)*i]=1
  ltot[i]=0
  i = i + 1
#print "v = ",v
t=0
w = 0
while (w < cmax):
  j = 1
  while j <= irate:
    i = 1
    while i <= nn:
      x[i]=v[i]
      i = i + 1
    [x, xnew] = derivs(x, xnew, n)
    i = 1
    while i <= nn:
      A[i] = xnew[i]
      x[i] = v[i] + (h*A[i]) / 2.0
      i = i + 1
    [x, xnew] = derivs(x, xnew, n)
    i = 1
    while i <= nn:
      B[i] = xnew[i]
      x[i] = v[i] + (h*B[i]) / 2.0
      i = i + 1
    [x, xnew] = derivs(x, xnew, n)
    i = 1
    while i <= nn:
      C[i] = xnew[i]
      x[i] = v[i] + h*C[i]
      i = i + 1
    [x, xnew] = derivs(x, xnew, n)
    i = 1
    while i <= nn:
      D[i] = xnew[i]
      v[i] = v[i] + h*(A[i] + D[i] + 2*(B[i] + C[i]))/6.0
      i = i + 1
    t = t + h
    j = j + 1

  #construct new orthonormal basis by gram-schmidt:
  znorm[1]=0  #normalize first vector

  j = 1
  while j <= n:
    znorm[1]=znorm[1]+v[n*j+1]**2
    j = j + 1

  znorm[1] = math.sqrt(znorm[1])

  j = 1
  while j <= n:
    v[n*j+1]=v[n*j+1]/znorm[1]
    j = j + 1

  #generate new orthonormal set:
  j = 2
  while j <= n:
    k = 1
    while k <= j-1:
      gsc[k]=0
      l = 1
      while l <= n:
        gsc[k]=gsc[k]+v[n*l+j]*v[n*l+k]
        l = l + 1
      k = k + 1

    k = 1
    while k <= n: #construct a new vector
      l = 1
      while l <= j-1:
        v[n*k+j]=v[n*k+j]-gsc[l]*v[n*k+l]
        l = l + 1
      k = k + 1

    znorm[j]=0     #calculate the vector's norm

    k = 1
    while k <= n: #construct a new vector
      znorm[j]=znorm[j]+v[n*k+j]**2
      k = k + 1

    znorm[j]=math.sqrt(znorm[j])

    k = 1
    while k <= n: #normalize the new vector
      v[n*k+j] = v[n*k+j] / znorm[j]
      k = k + 1

    j = j + 1

  k = 1
  while k <= n: #update running vector magnitudes
    if znorm[k] > 0:
      ltot[k] = ltot[k] + math.log(znorm[k])
    k = k + 1

  m = m + 1
  if m % io == 0 or w == cmax-1:  #normalize exponent and print every io iterations
    lsum=0
    kmax=0
    k = 1
    while k <= n:
      le = ltot[k] / t
      lsum = lsum + le
      if lsum > 0:
        lsum0 = lsum
        kmax = k
      k = k + 1
  w = w + 1
if choice == 1:
  print "Rossler:"
else:
  print "Lorenz:"
print n, "LEs = "
lsum=0
kmax=0
k = 1
while k <= n:
  le = ltot[k] / t
  lsum = lsum + le
  if lsum > 0:
    lsum0 = lsum
    kmax = k
  print le
  k = k + 1