odeint

I would like to use runge_kutta4 method in the odeint C++ library . I've solved the problem in Matlab. My following code in Matlab to solve x'' = -x - g*x' , with initial values x1 = 1 , x2 = 0 , is as follows main.m clear all clc t = 0:0.1:10; x0 = [1; 0]; [t, x] = ode45('ODESolver', t, x0); plot(t, x(:,1)); title('Position'); xlabel('time (sec)'); ylabel('x(t)'); ODESolver.m function dx = ODESolver(t, x) dx = zeros(2,1); g = 0.15; dx(1) = x(2); dx(2) = -x(1) - g*x(2); end I've installed the odeint Library. My code for using runge_kutta4 is as follows #include <iostream> #include <boost

For solving simple ODEs using SciPy, I used to use the odeint function, with form: scipy.integrate.odeint(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0, ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0, hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12, mxords=5, printmessg=0)[source] where a simple function to be integrated could include additional arguments of the form: def dy_dt(t, y, arg1, arg2): # processing code here In SciPy 1.0, it seems the ode and odeint funcs have been replaced by a newer solve_ivp method. scipy.integrate.solve_ivp(fun, t_span, y0,

Okay so how would i approach to writing a code to optimize the constants a and b in a differential equation, like dy/dt = a*y^2 + b, using curve_fit? I would be using odeint to solve the ODE and then curve_fit to optimize a and b. If you could please provide input on this situation i would greatly appreciate it! You might be better served by looking at ODEs with Sympy . Scipy/Numpy are fundamentally numerical packages and aren't really set up to do algebraic/symbolic operations. You definitely can do this: import numpy as np from scipy.integrate import odeint from scipy.optimize import curve