numpy - How to plot a defined function against one of its arguments in Python -

from __future__ import division import numpy np import matplotlib.pyplot plt  def f(x, t):       #function x'(t) = f(x,t)     return -x  def exact(t):       #exact solution     return np.exp(-t)  def rk4(x0, t0, dt):      #runge-kutta fourth order approximation     t = np.arange(0, 1+dt, dt)     n = len(t)     x = np.array([x0]*n)     e = np.array([x0]*n)     e0 = x0-exact(1)     x[0],t[0],e[0] = x0,t0,e0     in range(n-1):         h = t[i+1] - t[i]         k1 = h*f(x[i], t[i])         k2 = h*f(x[i] + 0.5 * k1, t[i] + 0.5 * h)         k3 = h*f(x[i] + 0.5 * k2, t[i] + 0.5 * h)         k4 = h*f(x[i] + k3, t[i+1])         x[i+1] = x[i] + (k1 + 2.0*(k2 + k3) + k4 )/6.0         e[i+1] = e[i]+(x[i+1]-x[i])     return e  vecrk4 = np.vectorize(rk4) dtime = np.arange(10e-4,1,10e-5) s = vecrk4(1.0,0.0,dtime) plt.plot(dtime,s) 

i'm trying plot rk4 function x0 = 1.0, t0 = 0.0 function of dt. tried vectorizing function , creating array timestep dt, error "valueerror: setting array element sequence."

the problem return value e not single number, numpy array.

vectorizing many arrays give list, vectorizing many numpy arrays not work here.

to come original question: way plot function against 1 of arguments using vectorization is:

from __future__ import division import numpy np import matplotlib.pyplot plt  def myfunc(a,b):     return 2*b+a   vecrk4 = np.vectorize(myfunc) dtime = np.arange(10e-4,1,10e-5) s = vecrk4(a=3, b=dtime) plt.plot(dtime,s) plt.show()