[SciPy-User] How to create a request to change a function?

[Fabrice Silva]

see https://pastebin.com/TKt12AEE for a readable version of the code
sample

Le vendredi 31 janvier 2020, Fabrice Silva a écrit :
> Le vendredi 31 janvier 2020, Augusto Dufloth a écrit :
> > Hello,
> > 
> > I have a request to change one of scipy’s function.
> > 
> > I am trying to use odeint to integrate a set of equations of
> > motion. My state equations rely on time dependent inputs. And these
> > inputs are numpy arrays.
> > 
> > When I do a flight path reconstruction, the inputs comes from the
> > inertial system of the aircraft and I plug it into the state
> > equations, so I get the deterministic position of the aircraft and
> > than I can compare it to the measured values.
> > 
> > The issue is that I can’t input an array. Searching stackoverflow I
> > saw solutions using for-loops and assigning an arg for each time
> > step. This is counter productive and create a huge time calculation
> > for long arrays.
> > 
> > I created my own C-dll to deal with this for loop issue, but it’s
> > super counter productive. 
> > 
> > Can we get an option that the input args are arrays if same size of
> > t, and as the state is integrated, the index of the arg follows the
> > states?
> 
> Hi,As far as I understand, you want to use time-varying parameters
> (which includes right-hand side terms of ODEs) specified as numpy
> array (i.e., at discrete time values) while ODEs are defined in the
> continuous-time domain. Specification of an extrapolation method is
> required for the well-posedness of the problem: the most trivial is
> the "Sample-and-hold" process which maintain values until next sample
> step.
> One simple solution (maybe one that you arleady tested) is to use the
> ode class and its integrate method.Adapting the example given in the
> docs 
> https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html#scipy.integrate.ode
> from scipy.integrate import ode
> def f(t, y, arg1):    return [1j*arg1*y[0] + y[1], -arg1*y[1]**2]def
> jac(t, y, arg1):    return [[1j*arg1, 1], [0, -arg1*2*y[1]]]
> r = ode(f, jac).set_integrator('zvode', method='bdf')
> t = np.arange(0, 10, 1)results = np.empty((len(t), 1 + len(y0)),
> dtype=float) + np.nan
> r.set_initial_value([1.0j, 2.0], t[0])for ind in range(len(t) -
> 1):    r.set_f_params(param[ind]).set_jac_params(param[ind])    tmp_r
> esult = r.integrate(t[ind+1])    if not
> r.successful():        break    results[ind] = tmp_result
> This introduced a reasonnable overhead in performance while begin
> quite readable.
> Best regards
> Fabrice
> 

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