scipy.integrate.ode with two coupled ODEs?

I'm currently trying to use SciPy's integrate.ode package to solve a pair of first-order ODEs that are coupled: say, the Lotka-Volterra predator-prey equation. However, this means during the integration loop I have to update the parameters I'm sending to the methods on every iteration, and simply keeping track of the previous value and calling set_f_params() on each iteration doesn't seem to be doing the trick.

hprev = Ho
pprev = Po
yh = np.zeros(0)
yp = np.zeros(0)
while dh.successful() and dp.successful() and dp.t < endtime and dh.t < endtime:
    hparams = [alpha, beta, pprev]
    pparams = [delta, gamma, hprev]
    dh.set_f_params(hparams)
    dp.set_f_params(pparams)
    dh.integrate(dh.t + stepsize)
    dp.integrate(dp.t + stepsize)
    yh = np.append(yh, dh.y)
    yp = np.append(yp, dp.y)
    hprev = dh.y
    pprev = dp.y

The values I'm setting at each iteration through set_f_params don't seem to be propagated to the callback methods, which wasn't terribly surprising gi开发者_开发问答ven none of the examples on the web seem to involve "live" variable passing to the callbacks, but this was the only method by which I could think to get these values into the callback methods.

Does anyone have any advice on how to use SciPy to numerically integrate these ODEs?

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I could be wrong, but this example seems very close to your problem. :) It uses odeint to solve the system of ODEs.

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I had a similar issue. Turns out, the integrator doesn't re-evaluate the differential equation function for every call of integrate(), but does it at its own internal times. I changed max_step option of the integrator to be the same as stepsize and that worked for me.