Here is how I would proceed (given that this is numerics it is completely in numpy/scipy)
- I am assuming your free variable is x 1. Get k in a nice form - if they come from arrays, get interpolation functions for them: `k_func=scipy.interpolate.interp1d(x_array,k_array)` - if they come from a known expression of x, u and u' just implement it as a function 2. Rewrite the scalar second order ode into a vector first order ode (this is maybe the most fundamental idea in numerical ode solving. It is important because first order ODEs are "trivial" to solve by iteration (not really, but you can search for the details)) u'' == f(u', u, x) becomes [v', u'] == [f(v, u, x), v] (i.e. just u' = v). Now write a function `def my_first_derivative` that takes as an argument [v, u] and returns [f(v, u, x), v] 3. Use the ode solver solution_array = scipy.integrate.odeint(my_first_derivative, initial_conditions_u_v, array_of_x_points) Most importantly, read the documentation of the functions that I mentioned. -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sympy+unsubscr...@googlegroups.com. To post to this group, send email to sympy@googlegroups.com. Visit this group at http://groups.google.com/group/sympy?hl=en-US. For more options, visit https://groups.google.com/groups/opt_out.
