How to make odeint successful?

Question

I am starting to use python, currently using scipy odeintto compute a related ODE system, however, when I start, the python shell always tells me that

>>> 
Excess work done on this call (perhaps wrong Dfun type).
Run with full_output = 1 to get quantitative information.
>>> 

So, I have to change my time step and the last time to make it integrable. To do this, I need to try different combinations, which is pretty painful. Can someone tell me how can I ask to odeintautomatically change the time interval and the final time for the successful integration of this ode system?

and here is the part of the code that called odeint:

def main(t, init_pop_a, init_pop_b, *args, **kwargs):
    """
    solve the obe for a given set of parameters
    """
    # construct initial condition
    # initially, rho_ee = 0
    rho_init = zeros((16,16))*1j ########
    rho_init[1,1] = init_pop_a
    rho_init[2,2] = init_pop_b
    rho_init[0,0] = 1 - (init_pop_a + init_pop_b)########
    rho_init_ravel, params = to_1d(rho_init)
    # perform the integration
    result = odeint(wrapped_bloch3, rho_init_ravel, t, args=args)
                        # BUG: need to pass kwargs
    # rewrap the result
    return from_1d(result, params, prepend=(len(t),))

things = [2*pi, 20*pi, 0,0, 0,0, 0.1,100]
Omega_a, Omega_b, Delta_a, Delta_b, \
init_pop_a, init_pop_b, tstep, tfinal = things
args = ( Delta_a, Delta_b, Omega_a, Omega_b )
t = arange(0, tfinal + tstep, tstep)
data = main(t, init_pop_a, init_pop_b, *args)

plt.plot(t,abs(data[:,4,4]))

where wrapped_bloch3 is a calculation of the dy / dt function.

Answer 1

. , : ODE scipy

odeint .

from scipy.integrate import odeint
import numpy as np
def func(t, y):
    return 1 + 1j
t = np.linspace(0, 1, 200)
y = odeint(func, 0, t)
# -> This outputs:
#
# TypeError: can't convert complex to float
# odepack.error: Result from function call is not a proper array of floats.

ode:

from scipy.integrate import ode
import numpy as np

def myodeint(func, y0, t):
    y0 = np.array(y0, complex)
    func2 = lambda t, y: func(y, t)   # odeint has these the other way :/
    sol = ode(func2).set_integrator('zvode').set_initial_value(y0, t=t[0])
    y = [sol.integrate(tp) for tp in t[1:]]
    y.insert(0, y0)
    return np.array(y)

def func(y, t, alpha):
    return 1j*alpha*y

alpha = 3.3
t = np.linspace(0, 1, 200)
y = myodeint(lambda y, t: func(y, t, alpha), [1, 0, 0], t)