def test_save_and_load_with_jac(self):
self.ODE = jitcode_lyap(**vanilla,n_lyap=n,wants_jacobian=True)
filename = self.ODE.save_compiled(overwrite=True)
self.ODE = jitcode_lyap((),n=n,n_lyap=n,module_location=filename)
self.ODE.set_integrator("vode")
self.assertTrue(_is_C(self.ODE.f))
self.assertTrue(_is_C(self.ODE.jac))
def test_save_and_load_with_jac(self):
self.ODE = jitcode_lyap(**vanilla, n_lyap=n, wants_jacobian=True)
filename = self.ODE.save_compiled(overwrite=True)
self.ODE = jitcode_lyap((), n=n, n_lyap=n, module_location=filename)
self.ODE.set_integrator("vode")
self.assertTrue(_is_C(self.ODE.f))
self.assertTrue(_is_C(self.ODE.jac))
def test_identity_of_lyaps(self):
n = len(f)
x = np.random.random((n+1)*n)
ODE1 = jitcode_lyap(f, n_lyap=n)
ODE2 = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=n)
ODE1.set_integrator("dopri5")
ODE2.set_integrator("dopri5")
assert_allclose(ODE1.f(0.0,x), ODE2.f(0.0,x))
def test_identity_of_lyaps(self):
n = len(f)
x = np.random.random((n+1)*n)
ODE1 = jitcode_lyap(f, n_lyap=n)
ODE2 = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=n)
ODE1.set_integrator("dopri5")
ODE2.set_integrator("dopri5")
assert_allclose(ODE1.f(0.0,x), ODE2.f(0.0,x))
def test_regular(self):
self.ODE = jitcode_lyap(**vanilla, n_lyap=n)
self.ODE.set_integrator("dopri5")
import numpy as np
a = -0.025794
b1 = 0.0065
b2 = 0.0135
c = 0.02
k = 0.128
t, y = provide_basic_symbols()
f = [
y(0) * (a - y(0)) * (y(0) - 1.0) - y(1) + k * (y(2) - y(0)),
b1 * y(0) - c * y(1),
y(2) * (a - y(2)) * (y(2) - 1.0) - y(3) + k * (y(0) - y(2)),
b2 * y(2) - c * y(3)
]
initial_state = np.array([1., 2., 3., 4.])
n = len(f)
ODE = jitcode_lyap(f, n_lyap=n)
ODE.set_integrator("vode")
ODE.set_initial_value(initial_state, 0.0)
data = np.vstack(ODE.integrate(T) for T in range(10, 100000, 10))
for i in range(n):
lyap = np.average(data[1000:, n + i])
stderr = sem(data[1000:, n + i])
print("%i. Lyapunov exponent: % .4f ± %.4f" % (i + 1, lyap, stderr))
def evaluate(scenario):
ODE = jitcode_lyap(**scenario, n_lyap=n)
ODE.compile_C()
return ODE.f(0.0, x)
def test_lyapunov_with_helpers_and_jac(self):
self.ODE = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=self.n)
self.ODE.set_integrator("vode")
def test_lyapunov_with_helpers(self):
self.ODE = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=self.n)
self.ODE.set_integrator("dopri5")
def test_lyapunov_with_jac(self):
self.ODE = jitcode_lyap(f, n_lyap=self.n)
self.ODE.set_integrator("vode")
b1 = 0.0065
b2 = 0.0135
c = 0.02
k = 0.128
f = [
y(0) * ( a-y(0) ) * ( y(0)-1.0 ) - y(1) + k * (y(2) - y(0)),
b1*y(0) - c*y(1),
y(2) * ( a-y(2) ) * ( y(2)-1.0 ) - y(3) + k * (y(0) - y(2)),
b2*y(2) - c*y(3)
]
initial_state = np.array([1.,2.,3.,4.])
n = len(f)
ODE = jitcode_lyap(f, n_lyap=n)
ODE.set_integrator("vode")
ODE.set_initial_value(initial_state,0.0)
times = range(10,100000,10)
lyaps = []
for time in times:
lyaps.append(ODE.integrate(time)[1])
# converting to NumPy array for easier handling
lyaps = np.vstack(lyaps)
for i in range(n):
lyap = np.average(lyaps[1000:,i])
stderr = sem(lyaps[1000:,i]) # Note that this only an estimate
print("%i. Lyapunov exponent: % .4f ± %.4f" % (i+1,lyap,stderr))
def test_regular(self):
self.ODE = jitcode_lyap(**vanilla,n_lyap=n)
self.ODE.set_integrator("dopri5")
def test_lyapunov_with_helpers_and_jac(self):
self.ODE = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=self.n)
self.ODE.set_integrator("vode")
def test_lyapunov_with_helpers(self):
self.ODE = jitcode_lyap(f_alt, get_f_alt_helpers(), n_lyap=self.n)
self.ODE.set_integrator("dopri5")
def test_lyapunov_with_jac(self):
self.ODE = jitcode_lyap(f, n_lyap=self.n)
self.ODE.set_integrator("vode")
def test_lyapunov(self):
self.ODE = jitcode_lyap(f, n_lyap=self.n)
self.ODE.set_integrator("dopri5")
def test_jac(self):
self.ODE = jitcode_lyap(**vanilla, n_lyap=n)
self.ODE.set_integrator("lsoda")
def test_lyapunov(self):
self.ODE = jitcode_lyap(f, n_lyap=self.n)
self.ODE.set_integrator("dopri5")
def evaluate(scenario): ODE = jitcode_lyap(**scenario,n_lyap=n) ODE.compile_C() return ODE.f(0.0,x)
def test_jac(self):
self.ODE = jitcode_lyap(**vanilla,n_lyap=n)
self.ODE.set_integrator("lsoda")
